From 32496cf181713fb093500f7f8a2be2674b0e3ba7 Mon Sep 17 00:00:00 2001
From: Daiana <daiana.ss@gmail.com>
Date: Sat, 17 Aug 2024 20:52:40 -0300
Subject: [PATCH 1/2] atividades sala

---
 exercicios/para-sala/dados_ficticios.csv   | 1001 ++++++++++++++++++++
 exercicios/para-sala/exercicio_aula.ipynb  |  426 +++++++++
 exercicios/para-sala/exercicio_extra.ipynb |  591 ++++++++++++
 3 files changed, 2018 insertions(+)
 create mode 100644 exercicios/para-sala/dados_ficticios.csv
 create mode 100644 exercicios/para-sala/exercicio_aula.ipynb
 create mode 100644 exercicios/para-sala/exercicio_extra.ipynb

diff --git a/exercicios/para-sala/dados_ficticios.csv b/exercicios/para-sala/dados_ficticios.csv
new file mode 100644
index 0000000..bead322
--- /dev/null
+++ b/exercicios/para-sala/dados_ficticios.csv
@@ -0,0 +1,1001 @@
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diff --git a/exercicios/para-sala/exercicio_aula.ipynb b/exercicios/para-sala/exercicio_aula.ipynb
new file mode 100644
index 0000000..2933090
--- /dev/null
+++ b/exercicios/para-sala/exercicio_aula.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C</td>\n",
+       "      <td>53.417560</td>\n",
+       "      <td>126.034826</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-01-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>D</td>\n",
+       "      <td>68.761708</td>\n",
+       "      <td>131.230224</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A</td>\n",
+       "      <td>59.504238</td>\n",
+       "      <td>100.640083</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-01-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C</td>\n",
+       "      <td>44.230963</td>\n",
+       "      <td>84.931643</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-01-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.015853</td>\n",
+       "      <td>109.199443</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-01-05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "0         C  53.417560  126.034826        1  2023-01-01\n",
+       "1         D  68.761708  131.230224        2  2023-01-02\n",
+       "2         A  59.504238  100.640083        6  2023-01-03\n",
+       "3         C  44.230963   84.931643        5  2023-01-04\n",
+       "4         C  41.015853  109.199443        9  2023-01-05"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('dados_ficticios.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtro = df[(df['Categoria'] == 'A') & (df['Valor_1'] > 60)]"
+   ]
+  },
+  {
+   "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>Categoria</th>\n",
+       "      <th>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.076061</td>\n",
+       "      <td>113.927749</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>A</td>\n",
+       "      <td>41.360092</td>\n",
+       "      <td>119.106104</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>B</td>\n",
+       "      <td>50.485216</td>\n",
+       "      <td>101.768138</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-05-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.690499</td>\n",
+       "      <td>129.550602</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-05-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>A</td>\n",
+       "      <td>52.704568</td>\n",
+       "      <td>77.166218</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2023-05-05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "120         D  44.076061  113.927749        6  2023-05-01\n",
+       "121         A  41.360092  119.106104        5  2023-05-02\n",
+       "122         B  50.485216  101.768138        9  2023-05-03\n",
+       "123         C  41.690499  129.550602        2  2023-05-04\n",
+       "124         A  52.704568   77.166218        8  2023-05-05"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[(df['Data'] >= '2023-05-01') & (df['Data'] <= '2023-05-31')]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>47.012307</td>\n",
+       "      <td>101.616545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>49.047868</td>\n",
+       "      <td>93.715846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>39.910509</td>\n",
+       "      <td>103.563094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>48.450760</td>\n",
+       "      <td>101.268104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Valor_1     Valor_2\n",
+       "Categoria                       \n",
+       "A          47.012307  101.616545\n",
+       "B          49.047868   93.715846\n",
+       "C          39.910509  103.563094\n",
+       "D          48.450760  101.268104"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_media = df.groupby('Categoria')[['Valor_1', 'Valor_2']].mean()\n",
+    "agrupado_media.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>Valor_3</th>\n",
+       "      <th>Contagem</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>58</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>38</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>50</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Valor_3  Contagem\n",
+       "Categoria                   \n",
+       "A               58        12\n",
+       "B               11         2\n",
+       "C               38         7\n",
+       "D               50        10"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_custom = df.groupby('Categoria').agg({\n",
+    "    'Valor_3': 'sum',\n",
+    "    'Categoria': 'count'\n",
+    "}).rename(columns={'Categoria': 'Contagem'})\n",
+    "\n",
+    "agrupado_custom.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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99NBDDnNCctuezZs3a+PGjfmu193dXR07dtRXX33lcAuGpKQkxcXFqU2bNgU6FHajfPLJJw6Hej7//HOdOHHCfpZc8+bNFRQUpJkzZyojI8M+btmyZdq9e7e6dOni9GuPGTNGvr6+euKJJ5SUlJSj/+DBg/bvT26fR3JysubMmZNjOV9fX/t3+kq9e/fWxo0btWLFihx9Z8+e1aVLlyT99R27ePGiPvroI3t/dna2YmJiHJYJDg7Wbbfdpo8//tjh9Xbu3Kn//ve/6ty5cz5bn7fCbCtuTuyZQamT2+miV2vbtq2GDBmiqKgo/fTTT+rYsaM8PDy0f/9+xcfHa+rUqfr73/+uypUra/To0YqKilLXrl3VuXNnbd++XcuWLVOlSpXyfY0GDRqodu3aGj16tI4dO6aAgAAtXLjwmnt0LhszZozmzZunv/3tb3rmmWfsp2aHhoY63FogICBAsbGx+sc//qE77rhDffr0UeXKlXXkyBEtXbpUrVu31vvvv3/N1/Pz81OPHj3sVwO+eqJx165dtWjRIj344IPq0qWLDh06pJkzZ6pRo0ZKS0vLd90TJ07UypUr1aZNGw0bNkxlypTRBx98oIyMDL399tsFej8KYt26dfZJq6dOndK5c+c0ceJESX9dobkgV2euUKGC2rRpo0GDBikpKUnR0dGqU6eOffKrh4eH3nrrLQ0aNEht27ZV37597adm16xZ87puiVG7dm3FxcXp4YcfVsOGDdW/f381adJEmZmZ+v777xUfH2+/H1jHjh3l6empbt26aciQIUpLS9NHH32koKAgnThxwmG94eHhio2N1cSJE1WnTh0FBQWpXbt2ev7557V48WJ17dpVAwcOVHh4uM6dO6cdO3bo888/12+//aZKlSqpR48euuuuu/Tcc8/pwIEDatCggRYvXqw//vhDkuOen3feeUedOnVSRESEHn/8cfup2YGBgU7fr6sw24qblOtOpAKu35WnZufn6lOzL/vwww9NeHi48fHxMf7+/qZp06ZmzJgx5vjx4/YxWVlZ5tVXXzXBwcHGx8fH3HvvvWbnzp0mNDT0mqdm//rrr6Z9+/bGz8/PVKpUyQwePNj8/PPPOU5nzcsvv/xi2rZta7y9vU316tXN66+/bmbNmuVwavaVrx8ZGWkCAwONt7e3qV27thk4cKD58ccfr/k6ly1dutRIMsHBwQ6naRtjTHZ2tpk0aZIJDQ01Xl5e5vbbbzdff/21GTBggAkNDXUYq6tOzTbGmG3btpnIyEjj5+dnypYta+677z7z/fffO4wp6OeZl8un9Ob2uLqeq13+/ObPn2/GjRtngoKCjI+Pj+nSpUuup9F/+umn5vbbbzdeXl6mQoUKpl+/fub33393GDNgwADj6+tb6O3Yt2+fGTx4sKlZs6bx9PQ0/v7+pnXr1mb69OnmwoUL9nGLFy82zZo1M97e3qZmzZrmrbfeMrNnz87x/UhMTDRdunQx/v7+RpLDadqpqalm3Lhxpk6dOsbT09NUqlTJtGrVyrz77rsmMzPTPu7UqVPmkUceMf7+/iYwMNAMHDjQbNiwwUgyCxYscKj/22+/Na1btzY+Pj4mICDAdOvWzfz6668OYy5/VqdOncqx/bmdml3QbcXNyWZMPlcpAoCbxNq1a3XfffcpPj4+z7Pg4OjLL7/Ugw8+qPXr16t169auLgc3MebMAACu6epbcGRlZWn69OkKCAhwuHI04ArMmQFQomVlZV1zErOfn5/8/PxuUEU3pxEjRuj8+fOKiIhQRkaGFi1apO+//16TJk0qVafSw5oIMwBKtKNHj17zwmoTJkxwenIpCqZdu3Z677339PXXX+vChQuqU6eOpk+frqeeesrVpQFizgyAEu3ChQs5rqp7tbCwMIfrAAG4uRBmAACApd0Uh5mys7N1/Phx+fv753klTAAAULIYY5Samqpq1arle1uQmyLMHD9+vERdLh0AABTc0aNH871h600RZvz9/SX99WaUpMumAwCAvKWkpCgkJMT+ezwvN0WYuXxoKSAggDADAIDFXGuKCBfNAwAAlkaYAQAAlkaYAQAAlkaYAQAAlubSMPPKK6/IZrM5PBo0aGDvv3DhgoYPH66KFSvKz89PPXv2VFJSkgsrBgAAJY3L98w0btxYJ06csD+uvGz5s88+qyVLlig+Pl4JCQk6fvy4HnroIRdWCwAAShqXn5pdpkwZVa1aNUd7cnKyZs2apbi4OLVr106SNGfOHDVs2FCbNm1Sy5Ytb3SpAACgBHL5npn9+/erWrVqCgsLU79+/XTkyBFJ0tatW3Xx4kW1b9/ePrZBgwaqUaOGNm7cmO86MzIylJKS4vAAAAClk0vDTIsWLTR37lwtX75csbGxOnTokO6++26lpqYqMTFRnp6eKleunMMyVapUUWJiYr7rjYqKUmBgoP3BrQwAACi9XHqYqVOnTvafmzVrphYtWig0NFSfffaZfHx8nF7vuHHjNGrUKPvzy5dDBgAApY/LDzNdqVy5cqpXr54OHDigqlWrKjMzU2fPnnUYk5SUlOscmyt5eXnZb13ALQwAACjdSlSYSUtL08GDBxUcHKzw8HB5eHho1apV9v69e/fqyJEjioiIcGGVAACgJHHpYabRo0erW7duCg0N1fHjxzVhwgS5u7urb9++CgwM1OOPP65Ro0apQoUKCggI0IgRIxQREcGZTAAAwM6lYeb3339X3759debMGVWuXFlt2rTRpk2bVLlyZUnSlClT5Obmpp49eyojI0ORkZGaMWOGK0sGAAAljM0YY1xdRHFLSUlRYGCgkpOTmT8DAIBFFPT3t8svmgeUNm9uP+3qElxi7O2VXF0CgJtUiZoADAAAUFiEGQAAYGmEGQAAYGnMmQEAoICYE1cysWcGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGrczuAG4/DUAAMWHPTMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSSlSYefPNN2Wz2TRy5Eh724ULFzR8+HBVrFhRfn5+6tmzp5KSklxXJAAAKFFKTJj54Ycf9MEHH6hZs2YO7c8++6yWLFmi+Ph4JSQk6Pjx43rooYdcVCUAAChpSkSYSUtLU79+/fTRRx+pfPny9vbk5GTNmjVLkydPVrt27RQeHq45c+bo+++/16ZNm1xYMQAAKClKRJgZPny4unTpovbt2zu0b926VRcvXnRob9CggWrUqKGNGzfmub6MjAylpKQ4PAAAQOlUxtUFLFiwQNu2bdMPP/yQoy8xMVGenp4qV66cQ3uVKlWUmJiY5zqjoqL06quvFnWpAACgBHJpmDl69KieeeYZrVy5Ut7e3kW23nHjxmnUqFH25ykpKQoJCSmy9QPAZW9uP+3qElxi7O2VXF0CYOfSw0xbt27VyZMndccdd6hMmTIqU6aMEhISNG3aNJUpU0ZVqlRRZmamzp4967BcUlKSqlatmud6vby8FBAQ4PAAAAClk0v3zNx///3asWOHQ9ugQYPUoEED/fOf/1RISIg8PDy0atUq9ezZU5K0d+9eHTlyRBEREa4oGQAAlDAuDTP+/v5q0qSJQ5uvr68qVqxob3/88cc1atQoVahQQQEBARoxYoQiIiLUsmVLV5QMAABKGJdPAL6WKVOmyM3NTT179lRGRoYiIyM1Y8YMV5cFAABKiBIXZtauXevw3NvbWzExMYqJiXFNQQAAoEQrEdeZAQAAcBZhBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWFqRhpmDBw+qXbt2RblKAACAfBVpmElLS1NCQkJRrhIAACBfZQozeNq0afn2Hzt27LqKAQAAKKxChZmRI0cqODhYnp6eufZnZmYWSVEAAAAFVagwExoaqrfeeku9e/fOtf+nn35SeHh4kRQGAABQEIWaMxMeHq6tW7fm2W+z2WSMue6iAAAACqpQe2Zee+01paen59nfqFEjHTp06LqLAgAAKKhC7Zlp1KiRmjdvnme/h4eHQkND7c83bNigjIwM56sDAAC4hmK9aF6nTp04wwkAABSrYg0zzJ8BAADFjdsZAAAASyPMAAAASyPMAAAASyvWMGOz2Ypz9QAAAIUPM8YYHTlyRBcuXCjQWAAAgOLkVJipU6eOjh49es2xqampCgsLc6owAACAgih0mHFzc1PdunV15syZ4qgHAACgUJyaM/Pmm2/q+eef186dO4u6HgAAgEIp1L2ZLuvfv7/S09N16623ytPTUz4+Pg79f/zxR5EUBwAAcC1OhZno6OgiLgMAAMA5ToWZAQMGFHUdAAAATnEqzEhSVlaWvvzyS+3evVuS1LhxY3Xv3l3u7u5FVhwAAMC1OBVmDhw4oM6dO+vYsWOqX7++JCkqKkohISFaunSpateuXaRFAgAA5MWps5mefvpp1a5dW0ePHtW2bdu0bds2HTlyRLVq1dLTTz9d4PXExsaqWbNmCggIUEBAgCIiIrRs2TJ7/4ULFzR8+HBVrFhRfn5+6tmzp5KSkpwpGQAAlFJOhZmEhAS9/fbbqlChgr2tYsWKevPNN5WQkFDg9dxyyy168803tXXrVv34449q166dHnjgAe3atUuS9Oyzz2rJkiWKj49XQkKCjh8/roceesiZkgEAQCnl1GEmLy8vpaam5mhPS0uTp6dngdfTrVs3h+dvvPGGYmNjtWnTJt1yyy2aNWuW4uLi1K5dO0nSnDlz1LBhQ23atEktW7Z0pnQAAFDKOLVnpmvXrvq///s/bd68WcYYGWO0adMmPfnkk+revbtThWRlZWnBggU6d+6cIiIitHXrVl28eFHt27e3j2nQoIFq1KihjRs35ruujIwMpaSkODwAAEDp5FSYmTZtmmrXrq2IiAh5e3vL29tbrVu3Vp06dTR16tRCrWvHjh3y8/OTl5eXnnzySX3xxRdq1KiREhMT5enpqXLlyjmMr1KlihITE/NdZ1RUlAIDA+2PkJCQwm4iAACwCKcOM5UrV05fffWV9u/frz179kiSGjZsqDp16hR6XfXr19dPP/2k5ORkff755xowYECh5t3kZty4cRo1apT9eUpKCoEGAIBSyunrzEhS3bp1Vbdu3esqwNPT0x6CwsPD9cMPP2jq1Kl6+OGHlZmZqbNnzzrsnUlKSlLVqlXzXaeXl5e8vLyuqy4AAGANBQ4zV+7puJbJkyc7VYwkZWdnKyMjQ+Hh4fLw8NCqVavUs2dPSdLevXt15MgRRUREOL1+AABQuhQ4zGzfvr1A42w2W4FffNy4cerUqZNq1Kih1NRUxcXFae3atVqxYoUCAwP1+OOPa9SoUapQoYICAgI0YsQIRUREcCYTAACwK3CYWbNmTZG/+MmTJ9W/f3+dOHFCgYGBatasmVasWKEOHTpIkqZMmSI3Nzf17NlTGRkZioyM1IwZM4q8DgAAYF3XNWfmes2aNSvffm9vb8XExCgmJuYGVQQAAKzG6TDz448/6rPPPtORI0eUmZnp0Ldo0aLrLgwAAKAgnLrOzIIFC9SqVSvt3r1bX3zxhS5evKhdu3Zp9erVCgwMLOoaAQAA8uRUmJk0aZKmTJmiJUuWyNPTU1OnTtWePXvUu3dv1ahRo6hrBAAAyJNTYebgwYPq0qWLpL+uE3Pu3DnZbDY9++yz+vDDD4u0QAAAgPw4FWbKly9vv9Fk9erVtXPnTknS2bNnlZ6eXnTVAQAAXINTE4DvuecerVy5Uk2bNlWvXr30zDPPaPXq1Vq5cqXuv//+oq4RAAAgT4UKMzt37lSTJk30/vvv68KFC5KkF198UR4eHvr+++/Vs2dPvfTSS8VSKAAAQG4KFWaaNWumO++8U0888YT69OkjSXJzc9PYsWOLpTgAAIBrKdScmYSEBDVu3FjPPfecgoODNWDAAH333XfFVRsAAMA1FSrM3H333Zo9e7ZOnDih6dOn67ffflPbtm1Vr149vfXWW0pMTCyuOgEAAHLl1NlMvr6+GjRokBISErRv3z716tVLMTExqlGjhrp3717UNQIAAOTJqTBzpTp16uiFF17QSy+9JH9/fy1durQo6gIAACiQ67rR5Lp16zR79mwtXLhQbm5u6t27tx5//PGiqg0AAOCaCh1mjh8/rrlz52ru3Lk6cOCAWrVqpWnTpql3797y9fUtjhoBAADyVKgw06lTJ3377beqVKmS+vfvr8cee0z169cvrtoAAACuqVBhxsPDQ59//rm6du0qd3f34qoJAACgwAoVZhYvXlxcdQAAADjlus9mAgAAcCXCDAAAsDTCDAAAsDTCDAAAsDSnw8y8efPUunVrVatWTYcPH5YkRUdH66uvviqy4gAAAK7FqTATGxurUaNGqXPnzjp79qyysrIkSeXKlVN0dHRR1gcAAJAvp8LM9OnT9dFHH+nFF190uN5M8+bNtWPHjiIrDgAA4FqcCjOHDh3S7bffnqPdy8tL586du+6iAAAACsqpMFOrVi399NNPOdqXL1+uhg0bXm9NAAAABebUXbNHjRql4cOH68KFCzLGaMuWLZo/f76ioqL0r3/9q6hrBAAAyJNTYeaJJ56Qj4+PXnrpJaWnp+uRRx5RtWrVNHXqVPXp06eoawQAAMiTU2FGkvr166d+/fopPT1daWlpCgoKKsq6AAAACsTpMHNZ2bJlVbZs2aKoBQAAoNAKHGZuv/122Wy2Ao3dtm2b0wUBAAAURoHDTI8ePew/X7hwQTNmzFCjRo0UEREhSdq0aZN27dqlYcOGFXmRAAAAeSlwmJkwYYL95yeeeEJPP/20Xn/99Rxjjh49WnTVAQAAXINT15mJj49X//79c7Q/+uijWrhw4XUXBQAAUFBOhRkfHx9t2LAhR/uGDRvk7e193UUBAAAUlFNnM40cOVJDhw7Vtm3bdNddd0mSNm/erNmzZ+vll18u0gIBAADy41SYGTt2rMLCwjR16lT9+9//liQ1bNhQc+bMUe/evYu0QAAAgPw4fZ2Z3r17E1wAAIDLOTVnBgAAoKQgzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEtz+mym33//XYsXL9aRI0eUmZnp0Dd58uTrLgwAAKAgnAozq1atUvfu3RUWFqY9e/aoSZMm+u2332SM0R133FHUNQIAAOTJqcNM48aN0+jRo7Vjxw55e3tr4cKFOnr0qNq2batevXoVdY0AAAB5cirM7N69236jyTJlyuj8+fPy8/PTa6+9prfeeqtICwQAAMiPU2HG19fXPk8mODhYBw8etPedPn26aCoDAAAoAKfmzLRs2VLr169Xw4YN1blzZz333HPasWOHFi1apJYtWxZ1jQAAAHlyKsxMnjxZaWlpkqRXX31VaWlp+vTTT1W3bl3OZAIAADeUU2EmLCzM/rOvr69mzpxZZAUBAAAUBhfNAwAAllbgPTMVKlTQvn37VKlSJZUvX142my3PsX/88UeRFAcAAHAtBQ4zU6ZMkb+/vyQpOjq6uOoBAAAolAKHmQEDBuT6MwAAgCsVOMykpKQUeKUBAQFOFQMAAFBYBQ4z5cqVy3eezJWysrKcLggAAKAwChxm1qxZY//5t99+09ixYzVw4EBFRERIkjZu3KiPP/5YUVFRRV8lAABAHgocZtq2bWv/+bXXXtPkyZPVt29fe1v37t3VtGlTffjhh8ypAQAAN4xT15nZuHGjmjdvnqO9efPm2rJly3UXBQAAUFBOhZmQkBB99NFHOdr/9a9/KSQkpMDriYqK0p133il/f38FBQWpR48e2rt3r8OYCxcuaPjw4apYsaL8/PzUs2dPJSUlOVM2AAAohZy6ncGUKVPUs2dPLVu2TC1atJAkbdmyRfv379fChQsLvJ6EhAQNHz5cd955py5duqQXXnhBHTt21K+//ipfX19J0rPPPqulS5cqPj5egYGBeuqpp/TQQw9pw4YNzpQOAABKGafCTOfOnbVv3z7FxsZqz549kqRu3brpySefLNSemeXLlzs8nzt3roKCgrR161bdc889Sk5O1qxZsxQXF6d27dpJkubMmaOGDRtq06ZN3KEbAAA4F2akvw41TZo0qShrUXJysqS/bp0gSVu3btXFixfVvn17+5gGDRqoRo0a2rhxY55hJiMjQxkZGfbnhblGDgAAsBanbzT53Xff6dFHH1WrVq107NgxSdK8efO0fv16p9aXnZ2tkSNHqnXr1mrSpIkkKTExUZ6enipXrpzD2CpVqigxMTHPdUVFRSkwMND+KMzeIgAAYC0FCjObN2/WxYsX7c8XLlyoyMhI+fj4aNu2bfa9IMnJyU7vrRk+fLh27typBQsWOLX8lcaNG6fk5GT74+jRo9e9TgAAUDIVOMx07NhRqampkqSJEydq5syZ+uijj+Th4WEf17p1a23btq3QRTz11FP6+uuvtWbNGt1yyy329qpVqyozM1Nnz551GJ+UlKSqVavmuT4vLy8FBAQ4PAAAQOlUoDDz9NNPq2vXrvYL5+3du1f33HNPjnGBgYE5gkd+jDF66qmn9MUXX2j16tWqVauWQ394eLg8PDy0atUqe9vevXt15MgR+5WHAQDAza3AE4Cfe+45e4CoWrWqDhw4oJo1azqMWb9+vcLCwgr84sOHD1dcXJy++uor+fv72+fBBAYGysfHR4GBgXr88cc1atQoVahQQQEBARoxYoQiIiI4kwkAAEgq5NlMrVq1kiQNHjxYzzzzjGbPni2bzabjx49r48aNGj16tF5++eUCry82NlaSdO+99zq0z5kzRwMHDpT01zVt3Nzc1LNnT2VkZCgyMlIzZswoTNkAAKAUc+rU7LFjxyo7O1v333+/0tPTdc8998jLy0ujR4/WiBEjCrweY8w1x3h7eysmJkYxMTHOlAoAAEo5p8KMzWbTiy++qOeff14HDhxQWlqaGjVqJD8/v6KuDwAAIF9OXzRPkjw9PdWoUaOiqgUAAKDQChVmHnvssQKNmz17tlPFAAAAFFahwszcuXMVGhqq22+/vUDzXQAAAIpbocLM0KFDNX/+fB06dEiDBg3So48+ar+PEgAAgCsU6t5MMTExOnHihMaMGaMlS5YoJCREvXv31ooVK9hTAwAAXKLQN5r08vJS3759tXLlSv36669q3Lixhg0bppo1ayotLa04agQAAMiT03fNliQ3NzfZbDYZY5SVlVVUNQEAABRYocNMRkaG5s+frw4dOqhevXrasWOH3n//fR05coTrzAAAgBuuUBOAhw0bpgULFigkJESPPfaY5s+fr0qVKhVXbQAAANdUqDAzc+ZM1ahRQ2FhYUpISFBCQkKu4xYtWlQkxQEAAFxLocJM//79ZbPZiqsWAACAQiv0RfMAAABKkus6mwkAAMDVCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSXB5m1q1bp27duqlatWqy2Wz68ssvHfqNMRo/fryCg4Pl4+Oj9u3ba//+/a4pFgAAlDguDzPnzp3TrbfeqpiYmFz73377bU2bNk0zZ87U5s2b5evrq8jISF24cOEGVwoAAEqiMq4uoFOnTurUqVOufcYYRUdH66WXXtIDDzwgSfrkk09UpUoVffnll+rTp8+NLBUAAJRALt8zk59Dhw4pMTFR7du3t7cFBgaqRYsW2rhxY57LZWRkKCUlxeEBAABKpxIdZhITEyVJVapUcWivUqWKvS83UVFRCgwMtD9CQkKKtU4AAOA6JTrMOGvcuHFKTk62P44ePerqkgAAQDEp0WGmatWqkqSkpCSH9qSkJHtfbry8vBQQEODwAAAApVOJDjO1atVS1apVtWrVKntbSkqKNm/erIiICBdWBgAASgqXn82UlpamAwcO2J8fOnRIP/30kypUqKAaNWpo5MiRmjhxourWratatWrp5ZdfVrVq1dSjRw/XFQ0AAEoMl4eZH3/8Uffdd5/9+ahRoyRJAwYM0Ny5czVmzBidO3dO//d//6ezZ8+qTZs2Wr58uby9vV1VMgAAKEFcHmbuvfdeGWPy7LfZbHrttdf02muv3cCqAACAVZToOTMAAADXQpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWZpkwExMTo5o1a8rb21stWrTQli1bXF0SAAAoASwRZj799FONGjVKEyZM0LZt23TrrbcqMjJSJ0+edHVpAADAxSwRZiZPnqzBgwdr0KBBatSokWbOnKmyZctq9uzZri4NAAC4WBlXF3AtmZmZ2rp1q8aNG2dvc3NzU/v27bVx48Zcl8nIyFBGRob9eXJysiQpJSWleIvNw4W0VJe8rqulpHi6ugSX4PO+ufB531z4vG/06/71e9sYk++4Eh9mTp8+raysLFWpUsWhvUqVKtqzZ0+uy0RFRenVV1/N0R4SElIsNSJ3OT8BlGZ83jcXPu+bi6s/79TUVAUGBubZX+LDjDPGjRunUaNG2Z9nZ2frjz/+UMWKFWWz2VxY2Y2VkpKikJAQHT16VAEBAa4uB8WMz/vmwud9c7lZP29jjFJTU1WtWrV8x5X4MFOpUiW5u7srKSnJoT0pKUlVq1bNdRkvLy95eXk5tJUrV664SizxAgICbqov/82Oz/vmwud9c7kZP+/89shcVuInAHt6eio8PFyrVq2yt2VnZ2vVqlWKiIhwYWUAAKAkKPF7ZiRp1KhRGjBggJo3b6677rpL0dHROnfunAYNGuTq0gAAgItZIsw8/PDDOnXqlMaPH6/ExETddtttWr58eY5JwXDk5eWlCRMm5DjkhtKJz/vmwud9c+Hzzp/NXOt8JwAAgBKsxM+ZAQAAyA9hBgAAWBphBgAAWBphBgAAWBph5iaxc+dOV5cAAECxIMyUYqmpqfrwww9111136dZbb3V1OQCcsHr1ajVq1CjXG+UmJyercePG+u6771xQGVByEGZKoXXr1mnAgAEKDg7Wu+++q3bt2mnTpk2uLgvF4MyZM/afjx49qvHjx+v555/nl1spEh0drcGDB+d6CfvAwEANGTJEkydPdkFlKE7Z2dmaPXu2unbtqiZNmqhp06bq3r27Pvnkk2veQfpmxHVmSonExETNnTtXs2bNUkpKinr37q2ZM2fq559/VqNGjVxdHorYjh071K1bNx09elR169bVggUL9Le//U3nzp2Tm5ubzp07p88//1w9evRwdam4TqGhoVq+fLkaNmyYa/+ePXvUsWNHHTly5AZXhuJijFG3bt30zTff6NZbb1WDBg1kjNHu3bu1Y8cOde/eXV9++aWryyxR2DNTCnTr1k3169fXL7/8oujoaB0/flzTp093dVkoRmPGjFHTpk21bt063Xvvveratau6dOmi5ORk/fnnnxoyZIjefPNNV5eJIpCUlCQPD488+8uUKaNTp07dwIpQ3ObOnat169Zp1apV2r59u+bPn68FCxbo559/1rfffqvVq1frk08+cXWZJQp7ZkqBMmXK6Omnn9bQoUNVt25de7uHhwd7ZkqpSpUqafXq1WrWrJnS0tIUEBCgH374QeHh4ZL++mu9ZcuWOnv2rGsLxXWrXbu23nvvvTz3si1atEijR4/W//73vxtbGIpNx44d1a5dO40dOzbX/kmTJikhIUErVqy4wZWVXOyZKQXWr1+v1NRUhYeHq0WLFnr//fd1+vRpV5eFYvTHH3+oatWqkiQ/Pz/5+vqqfPny9v7y5csrNTXVVeWhCHXu3Fkvv/yyLly4kKPv/PnzmjBhgrp27eqCylBcfvnlF/3tb3/Ls79Tp076+eefb2BFJR97ZkqRc+fO6dNPP9Xs2bO1ZcsWZWVlafLkyXrsscfk7+/v6vJQhNzc3JSUlKTKlStLkvz9/fXLL7+oVq1akv46NFGtWjVlZWW5skwUgaSkJN1xxx1yd3fXU089pfr160v6a+9bTEyMsrKytG3bNm68W4p4enrq8OHDCg4OzrX/+PHjqlWrljIyMm5wZSUXYaaU2rt3r2bNmqV58+bp7Nmz6tChgxYvXuzqslBE3Nzc1KlTJ/sddJcsWaJ27drJ19dXkpSRkaHly5cTZkqJw4cPa+jQoVqxYoX9TBabzabIyEjFxMTYQyxKB3d3dyUmJtr/WLkaf6zkRJgp5bKysrRkyRLNnj2bMFOKDBo0qEDj5syZU8yV4Eb6888/deDAARljVLduXYdDiyg9rv5j5Wr8sZITYQYAgBKEP1YKjzADAAAsjbOZAACApRFmAACApRFmAACApRFmACAXa9eulc1m4yrKgAUQZgBct8TERI0YMUJhYWHy8vJSSEiIunXrplWrVhVo+blz56pcuXLFW2QhtWrVSidOnFBgYKCrSwFwDWVcXQAAa/vtt9/UunVrlStXTu+8846aNm2qixcvasWKFRo+fLj27Nnj6hIL7eLFi/L09LTfMgJAycaeGQDXZdiwYbLZbNqyZYt69uypevXqqXHjxho1apQ2bdokSZo8ebKaNm0qX19fhYSEaNiwYUpLS5P01+GcQYMGKTk5WTabTTabTa+88oqkvy4ONnr0aFWvXl2+vr5q0aKF1q5d6/D6H330kUJCQlS2bFk9+OCDmjx5co69PLGxsapdu7Y8PT1Vv359zZs3z6HfZrMpNjZW3bt3l6+vr954440ch5nOnDmjvn37qnr16ipbtqyaNm2q+fPnF/n7CcAJBgCcdObMGWOz2cykSZPyHTdlyhSzevVqc+jQIbNq1SpTv359M3ToUGOMMRkZGSY6OtoEBASYEydOmBMnTpjU1FRjjDFPPPGEadWqlVm3bp05cOCAeeedd4yXl5fZt2+fMcaY9evXGzc3N/POO++YvXv3mpiYGFOhQgUTGBhof+1FixYZDw8PExMTY/bu3Wvee+894+7ublavXm0fI8kEBQWZ2bNnm4MHD5rDhw+bNWvWGEnmzz//NMYY8/vvv5t33nnHbN++3Rw8eNBMmzbNuLu7m82bNxfhOwrAGYQZAE7bvHmzkWQWLVpUqOXi4+NNxYoV7c/nzJnjEECMMebw4cPG3d3dHDt2zKH9/vvvN+PGjTPGGPPwww+bLl26OPT369fPYV2tWrUygwcPdhjTq1cv07lzZ/tzSWbkyJEOY64OM7np0qWLee655/LsB3BjcJgJgNNMAS8g/u233+r+++9X9erV5e/vr3/84x86c+aM0tPT81xmx44dysrKUr169eTn52d/JCQk6ODBg5L+uqHqXXfd5bDc1c93796t1q1bO7S1bt1au3fvdmhr3rx5vtuQlZWl119/XU2bNlWFChXk5+enFStW6MiRI9fcfgDFiwnAAJxWt25d2Wy2fCf5/vbbb+ratauGDh2qN954QxUqVND69ev1+OOPKzMzU2XLls11ubS0NLm7u2vr1q1yd3d36PPz8yvS7ZBkv+N4Xt555x1NnTpV0dHR9vk/I0eOVGZmZpHXAqBw2DMDwGkVKlRQZGSkYmJidO7cuRz9Z8+e1datW5Wdna333ntPLVu2VL169XT8+HGHcZ6enjnuAHz77bcrKytLJ0+eVJ06dRwel88yql+/vn744QeH5a5+3rBhQ23YsMGhbcOGDWrUqFGhtnXDhg164IEH9Oijj+rWW29VWFiY9u3bV6h1ACgehBkA1yUmJkZZWVm66667tHDhQu3fv1+7d+/WtGnTFBERoTp16ujixYuaPn26/ve//2nevHmaOXOmwzpq1qyptLQ0rVq1SqdPn1Z6errq1aunfv36qX///lq0aJEOHTqkLVu2KCoqSkuXLpUkjRgxQt98840mT56s/fv364MPPtCyZctks9ns637++ec1d+5cxcbGav/+/Zo8ebIWLVqk0aNHF2o769atq5UrV+r777/X7t27NWTIECUlJV3/Gwjg+rl60g4A6zt+/LgZPny4CQ0NNZ6enqZ69eqme/fuZs2aNcYYYyZPnmyCg4ONj4+PiYyMNJ988kmOybVPPvmkqVixopFkJkyYYIwxJjMz04wfP97UrFnTeHh4mODgYPPggw+aX375xb7chx9+aKpXr258fHxMjx49zMSJE03VqlUd6psxY4YJCwszHh4epl69euaTTz5x6JdkvvjiC4e2qycAnzlzxjzwwAPGz8/PBAUFmZdeesn079/fPPDAA0XxFgK4DjZjCjiDDwAsYPDgwdqzZ4++++47V5cC4AZhAjAAS3v33XfVoUMH+fr6atmyZfr44481Y8YMV5cF4AZizwwAS+vdu7fWrl2r1NRUhYWFacSIEXryySddXRaAG4gwAwAALI2zmQAAgKURZgAAgKURZgAAgKURZgAAgKURZgAAgKURZgAAgKURZgAAgKURZgAAgKURZgAAgKX9f88QJ3shIfcpAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "media_valor_1 = df.groupby('Categoria')['Valor_1'].mean()\n",
+    "\n",
+    "\n",
+    "media_valor_1.plot(kind='bar', color='skyblue', title='Média de Valor_1 por Categoria')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Média de Valor_1')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/exercicio_extra.ipynb b/exercicios/para-sala/exercicio_extra.ipynb
new file mode 100644
index 0000000..87f038a
--- /dev/null
+++ b/exercicios/para-sala/exercicio_extra.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dados fictícios para exemplo\n",
+    "dados = {\n",
+    "    'ID_Consulta': [1, 2, 3, 4, 5],\n",
+    "    'Nome_Paciente': ['João Silva', 'Maria Oliveira', 'Carlos Souza', 'Ana Paula', 'Marcos Lima'],\n",
+    "    'Data_Consulta': ['2024-08-01', '2024-08-02', '2024-08-03', '2024-08-04', '2024-08-05'],\n",
+    "    'Sintomas': ['Febre, Tosse', 'Dor de Cabeça', 'Náusea, Vômito', 'Dor de Garganta', 'Cansaço, Falta de Ar'],\n",
+    "    'Diagnostico': ['Gripe', 'Enxaqueca', 'Gastrite', 'Amigdalite', 'Bronquite'],\n",
+    "    'Prescricao': ['Antitérmico, Repouso', 'Analgésico', 'Antiácido, Dieta', 'Antibiótico', 'Broncodilatador'],\n",
+    "    'Duracao_Consulta_Minutos': [30, 45, 25, 40, 35], # Duração em minutos\n",
+    "    'Gravidade_Sintomas': [4, 6, 5, 7, 8] # Gravidade dos sintomas em uma escala de 1 a 10\n",
+    "}\n",
+    "df = pd.DataFrame(dados)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Limpeza**\n",
+    "\n",
+    "- Valores nulos => .isnull()\n",
+    "- Dados duplicados => .drop_duplicates()\n",
+    "- \n",
+    "\n",
+    "df.dtypes => verifica tipo, daí (se preciso):\n",
+    "pd.to_datetime(df['Data_Consulta']) = converte a coluna pro tipo datetime\n",
+    "df['Duracao_Consulta_Minutos'] = pd.to_numeric(df['Duracao_Consulta_Minutos'], errors='coerce') => converte para tipo numérico\n",
+    "df['Gravidade_Sintomas'] = pd.to_numeric(df['Gravidade_Sintomas'], errors='coerce') => converte para tipo numérico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                  int64\n",
+       "Nome_Paciente               object\n",
+       "Data_Consulta               object\n",
+       "Sintomas                    object\n",
+       "Diagnostico                 object\n",
+       "Prescricao                  object\n",
+       "Duracao_Consulta_Minutos     int64\n",
+       "Gravidade_Sintomas           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                 0\n",
+       "Nome_Paciente               0\n",
+       "Data_Consulta               0\n",
+       "Sintomas                    0\n",
+       "Diagnostico                 0\n",
+       "Prescricao                  0\n",
+       "Duracao_Consulta_Minutos    0\n",
+       "Gravidade_Sintomas          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Data_Consulta'] = pd.to_datetime(df['Data_Consulta'])\n",
+    "\n",
+    "df['Duracao_Consulta_Minutos'] = pd.to_numeric(df['Duracao_Consulta_Minutos'], errors='coerce')\n",
+    "\n",
+    "df['Gravidade_Sintomas'] = pd.to_numeric(df['Gravidade_Sintomas'], errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Gráfico de Dispersão\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], color='blue')\n",
+    "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n",
+    "plt.xlabel('Duração da Consulta (minutos)')\n",
+    "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Análise**\n",
+    "\n",
+    "De acordo com o gráfico, entendemos que não há relação entre a gravidade dos sintomas e duração da consulta, sugerindo que a duração da consulta seja influenciada por outros fatores não explorados nesta análise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = df['Gravidade_Sintomas']\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "scatter = plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], c=colors, cmap='viridis')\n",
+    "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n",
+    "plt.xlabel('Duração da Consulta (minutos)')\n",
+    "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n",
+    "plt.colorbar(scatter, label='Gravidade dos Sintomas')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From f91e22761aa939b690dad2cb611cb739c2bca069 Mon Sep 17 00:00:00 2001
From: Daiana <daiana.ss@gmail.com>
Date: Mon, 26 Aug 2024 15:16:49 -0300
Subject: [PATCH 2/2] atividade semanal

---
 exercicios/para-casa/atividade_casa.ipynb | 889 ++++++++++++++++++++++
 exercicios/para-casa/dados_vendas.csv     | 366 +++++++++
 2 files changed, 1255 insertions(+)
 create mode 100644 exercicios/para-casa/atividade_casa.ipynb
 create mode 100644 exercicios/para-casa/dados_vendas.csv

diff --git a/exercicios/para-casa/atividade_casa.ipynb b/exercicios/para-casa/atividade_casa.ipynb
new file mode 100644
index 0000000..0852cb8
--- /dev/null
+++ b/exercicios/para-casa/atividade_casa.ipynb
@@ -0,0 +1,889 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Consulta e Filtragem de Dados ##\n",
+    "\n",
+    "**Carregar os dados**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"dados_vendas.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado?**\n",
+    "\n",
+    "O DataFrame df possui 365 linhas e 5 colunas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365, 5)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 2: Quais são os tipos de dados (dtypes) das colunas?**\n",
+    "\n",
+    "O tipo de dados das colunas Data e Categoria é *object*.\n",
+    "\n",
+    "O tipo da coluna Quantidade_Vendida é *int* (integer).\n",
+    "\n",
+    "O tipo das colunas Preco_Unitario e Valor_Total é *float*. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exibir as primeiras 5 linhas do DataFrame:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 3: Quantas categorias únicas de produtos estão presentes no DataFrame?**\n",
+    "\n",
+    "Há 5 categorias únicas de produtos no DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorias_unicas = len(df['Categoria'].unique())\n",
+    "categorias_unicas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos':**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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",
+       "  </tbody>\n",
+       "</table>\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"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+    "eletronicos.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 4: Qual é a média do valor total das vendas na categoria 'Eletrônicos'?**\n",
+    "\n",
+    "A média do valor total das vendas desta categoria é 12.291,26"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(12291.26)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(eletronicos['Valor_Total'].mean(), 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Filtrar as vendas acima de R$ 1000:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "vendas_acima_1000.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 5: Quantas vendas acima de R$ 1000 ocorreram?**\n",
+    "\n",
+    "Ocorreram 330 vendas acima de R$ 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(330, 5)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vendas_acima_1000.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Agrupamento e Agregação ##\n",
+    "\n",
+    "**Agrupar os dados por categoria e calcular o total de vendas por categoria:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "total_por_categoria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 6: Qual categoria teve o maior valor total de vendas?**\n",
+    "\n",
+    "A categoria Brinquedos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Calcular a quantidade média vendida por categoria:**"
+   ]
+  },
+  {
+   "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>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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+    "media_quantidade_categoria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 7: Qual categoria teve a maior quantidade média vendida?**\n",
+    "\n",
+    "A categoria Alimentos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualização de Dados ##\n",
+    "\n",
+    "**Criar um gráfico de barras mostrando o total de vendas por categoria:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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='purple')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 8: Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?**\n",
+    "\n",
+    "A categoria Brinquedos foi a que mais vendeu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos':**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Data'] = pd.to_datetime(df['Data'])\n",
+    "\n",
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n",
+    "plt.xlabel('Data')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 9: Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?**\n",
+    "\n",
+    "O que conseguimos observar a partir do gráfico é que há grande variação nas vendas diárias, tendo dias com alto volume de vendas seguidos de quedas substanciais e, isto ocorre ao longo de todo o período analisado. Podemos concluir, portanto, que não há um padrão ou tendência observável a partir destes dados."
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/dados_vendas.csv b/exercicios/para-casa/dados_vendas.csv
new file mode 100644
index 0000000..c98fa34
--- /dev/null
+++ b/exercicios/para-casa/dados_vendas.csv
@@ -0,0 +1,366 @@
+Data,Categoria,Quantidade_Vendida,Preco_Unitario,Valor_Total
+2023-04-13,Roupas,74,60.43,4471.82
+2023-12-15,Alimentos,83,272.88,22649.04
+2023-09-28,Roupas,17,195.62,3325.54
+2023-04-17,Roupas,85,233.93,19884.05
+2023-03-13,Roupas,78,305.94,23863.32
+2023-07-08,Eletrônicos,73,256.12,18696.760000000002
+2023-01-21,Eletrônicos,1,274.53,274.53
+2023-04-13,Eletrônicos,51,248.32,12664.32
+2023-05-02,Alimentos,45,210.39,9467.55
+2023-08-03,Brinquedos,77,388.22,29892.940000000002
+2023-11-27,Roupas,4,15.98,63.92
+2023-03-29,Roupas,62,303.24,18800.88
+2023-04-10,Alimentos,65,287.1,18661.5
+2023-12-26,Roupas,32,360.93,11549.76
+2023-06-01,Eletrônicos,34,303.52,10319.68
+2023-05-11,Brinquedos,92,415.13,38191.96
+2023-05-30,Livros,95,479.95,45595.25
+2023-11-05,Roupas,72,177.84,12804.48
+2023-09-15,Eletrônicos,39,121.4,4734.6
+2023-12-10,Livros,26,217.56,5656.56
+2023-10-21,Brinquedos,34,151.09,5137.06
+2023-07-11,Livros,54,311.33,16811.82
+2023-10-04,Eletrônicos,3,456.81,1370.43
+2023-06-10,Livros,50,78.17,3908.5
+2023-11-10,Alimentos,12,59.39,712.6800000000001
+2023-01-22,Livros,65,135.45,8804.25
+2023-09-10,Roupas,54,365.79,19752.66
+2023-08-24,Roupas,5,300.55,1502.75
+2023-12-11,Alimentos,94,60.08,5647.5199999999995
+2023-02-18,Eletrônicos,94,460.19,43257.86
+2023-02-28,Roupas,57,397.14,22636.98
+2023-06-19,Brinquedos,17,21.28,361.76
+2023-07-07,Roupas,47,329.17,15470.990000000002
+2023-09-28,Roupas,23,387.91,8921.93
+2023-07-09,Eletrônicos,79,193.47,15284.13
+2023-06-24,Livros,85,43.77,3720.4500000000003
+2023-02-20,Roupas,14,47.89,670.46
+2023-12-30,Alimentos,66,61.08,4031.2799999999997
+2023-02-24,Livros,75,421.82,31636.5
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+2023-10-07,Brinquedos,66,12.49,824.34
+2023-04-24,Brinquedos,10,350.9,3509.0
+2023-10-15,Brinquedos,5,33.9,169.5
+2023-12-08,Brinquedos,74,270.98,20052.52
+2023-05-31,Roupas,97,481.44,46699.68