dia1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semana Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Uma escola necessita trabalhar com filmes em seu planejamento pedagógico.\n",
    "- Ela necessita de visualizar as notas de diversos filmes em suas diversas categorias.\n",
    "- Os filmes devem atravessar gerações."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ambientação Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello EDA\n",
      "será: Top\n"
     ]
    }
   ],
   "source": [
    "# comentário de uma linha\n",
    "\n",
    "\"\"\"comentário de múltiplas linhas\n",
    "\"\"\"\n",
    "print(\"Hello EDA\")\n",
    "msg = input(\"Como será essa semana EDA?\")\n",
    "print(f\"será: {msg}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Características Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Multiplataforma\n",
    "- DC e IA\n",
    "- Identação\n",
    "- Tipagem Dinâmica\n",
    "- Bibliotecas\n",
    "- Paradigma Funcional (lambda)\n",
    "- Paradigma Orientação a Objetos\n",
    "- Paradigma Estruturado\n",
    "- Microserviços"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Pessoa():\n",
    "    def __init__(self,nome,idade):\n",
    "        self.nome = nome\n",
    "        self.idade = idade\n",
    "    def pega_nome(self):\n",
    "        return self.nome\n",
    "    def dobro_idade(self):\n",
    "        return self.idade * 2\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'João'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pessoa = Pessoa(\"João\", 35)\n",
    "pessoa.pega_nome()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "# instalar o numpy\n",
    "! pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (2.2.2)\n",
      "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from pandas) (2.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# instalar o pandas\n",
    "! pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pra entender melhor!\n",
    "- Sem lambda como seria?\n",
    "\n",
    "def dobro(self,x): <br>\n",
    "  <p> return x * 2`<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defininindo um lambda\n",
    "dobro = lambda x:x*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "matriz_4x4 = np.array([[1, 2, 3, 4],\n",
    "                       [2, 4, 6, 8],\n",
    "                       [3, 8, 12, 16],\n",
    "                       [4, 16, 24, 32]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(matriz_4x4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   0   1   2   3\n",
       "0  1   2   3   4\n",
       "1  2   4   6   8\n",
       "2  3   8  12  16\n",
       "3  4  16  24  32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dobrado = df.apply(dobro)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
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       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>48</td>\n",
       "      <td>64</td>\n",
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      "text/plain": [
       "   0   1   2   3\n",
       "0  2   4   6   8\n",
       "1  4   8  12  16\n",
       "2  6  16  24  32\n",
       "3  8  32  48  64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dobrado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iniciando nosso primeiro EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solução - Melhores Filmes para a Escola\n",
    "\n",
    "### Importando o DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filmes = pd.read_csv('datasets/ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostrando todas as colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renomear as colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filmes.columns = ['usuario_id','filmes_id','avaliacao','quando']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostrar os cinco primeiros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usuario_id</th>\n",
       "      <th>filmes_id</th>\n",
       "      <th>avaliacao</th>\n",
       "      <th>quando</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   usuario_id  filmes_id  avaliacao     quando\n",
       "0           1          1        4.0  964982703\n",
       "1           1          3        4.0  964981247\n",
       "2           1          6        4.0  964982224\n",
       "3           1         47        5.0  964983815\n",
       "4           1         50        5.0  964982931"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostrar uma SERIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         4.0\n",
       "1         4.0\n",
       "2         4.0\n",
       "3         5.0\n",
       "4         5.0\n",
       "         ... \n",
       "100831    4.0\n",
       "100832    5.0\n",
       "100833    5.0\n",
       "100834    5.0\n",
       "100835    3.0\n",
       "Name: avaliacao, Length: 100836, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes['avaliacao']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### de uma forma mais sintática"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         4.0\n",
       "1         4.0\n",
       "2         4.0\n",
       "3         5.0\n",
       "4         5.0\n",
       "         ... \n",
       "100831    4.0\n",
       "100832    5.0\n",
       "100833    5.0\n",
       "100834    5.0\n",
       "100835    3.0\n",
       "Name: avaliacao, Length: 100836, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.avaliacao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dúvida, mas o que realmente é uma SERIES?\n",
    "\n",
    "https://dataat.github.io/introducao-analise-de-dados-espaciais/python.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nossa primeira EDA (analisar a quantidade de filmes por avaliações)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "usuario_id  filmes_id  avaliacao  quando    \n",
       "610         170875     3.0        1493846415    1\n",
       "1           1          4.0        964982703     1\n",
       "            3          4.0        964981247     1\n",
       "            6          4.0        964982224     1\n",
       "610         160080     3.0        1493848031    1\n",
       "                                               ..\n",
       "1           151        5.0        964984041     1\n",
       "            110        4.0        964982176     1\n",
       "            101        5.0        964980868     1\n",
       "            70         3.0        964982400     1\n",
       "            50         5.0        964982931     1\n",
       "Name: count, Length: 100836, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avaliacao\n",
       "4.0    26818\n",
       "3.0    20047\n",
       "5.0    13211\n",
       "3.5    13136\n",
       "4.5     8551\n",
       "2.0     7551\n",
       "2.5     5550\n",
       "1.0     2811\n",
       "1.5     1791\n",
       "0.5     1370\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.avaliacao.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A média de todas as notas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(3.501556983616962)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.avaliacao.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostrar valores únicos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4. , 5. , 3. , 2. , 1. , 4.5, 3.5, 2.5, 0.5, 1.5])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.avaliacao.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contando histórias com Histogramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (4.53.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (2.1.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from seaborn) (2.1.0)\n",
      "Requirement already satisfied: pandas>=1.2 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from seaborn) (2.2.2)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from seaborn) (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.53.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ead\\desktop\\aulas python leandro\\semanaedatarde\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contar a história da quantidade de filmes por avaliações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filmes.avaliacao.plot(kind='hist', color='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filmes.avaliacao.plot(kind='hist', color='brown')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estatística Descritiva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>usuario_id</th>\n",
       "      <th>filmes_id</th>\n",
       "      <th>avaliacao</th>\n",
       "      <th>quando</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>610</td>\n",
       "      <td>166534</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1493848402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>610</td>\n",
       "      <td>168248</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493850091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>610</td>\n",
       "      <td>168250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1494273047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493846352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1493846415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        usuario_id  filmes_id  avaliacao      quando\n",
       "0                1          1        4.0   964982703\n",
       "1                1          3        4.0   964981247\n",
       "2                1          6        4.0   964982224\n",
       "3                1         47        5.0   964983815\n",
       "4                1         50        5.0   964982931\n",
       "...            ...        ...        ...         ...\n",
       "100831         610     166534        4.0  1493848402\n",
       "100832         610     168248        5.0  1493850091\n",
       "100833         610     168250        5.0  1494273047\n",
       "100834         610     168252        5.0  1493846352\n",
       "100835         610     170875        3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a média de avaliações é: 3.50\n"
     ]
    }
   ],
   "source": [
    "# Calculando a média\n",
    "print(f\"a média de avaliações é: {df_filmes.avaliacao.mean():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a mediana de avaliações é: 3.5\n"
     ]
    }
   ],
   "source": [
    "# Caculando a mediana\n",
    "print(f\"a mediana de avaliações é: {df_filmes.avaliacao.median()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " a soma de avaliações é: 353083.0\n"
     ]
    }
   ],
   "source": [
    "# Calculando a soma\n",
    "print(f\" a soma de avaliações é: {df_filmes.avaliacao.sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mediana\n",
    "-- Para definir o padrão de qualidade, é o segundo quartil, dos 3 quartis. Os 50% do elemento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100836.000000\n",
       "mean          3.501557\n",
       "std           1.042529\n",
       "min           0.500000\n",
       "25%           3.000000\n",
       "50%           3.500000\n",
       "75%           4.000000\n",
       "max           5.000000\n",
       "Name: avaliacao, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.avaliacao.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando variável\n",
    "notas = df_filmes.avaliacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "1    4.0\n",
       "2    4.0\n",
       "3    5.0\n",
       "4    5.0\n",
       "Name: avaliacao, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrando o cabeçalho\n",
    "notas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='avaliacao'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(notas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='avaliacao'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=notas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Gráfico Boxplot ou Caixa\n",
    "- O objetivo dele é mostrar a mediana e os quartis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset movies.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filmes_todos = pd.read_csv('datasets/movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes_todos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filmes_todos.columns = ['filme_id','título','genero']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>filme_id</th>\n",
       "      <th>título</th>\n",
       "      <th>genero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   filme_id                              título  \\\n",
       "0         1                    Toy Story (1995)   \n",
       "1         2                      Jumanji (1995)   \n",
       "2         3             Grumpier Old Men (1995)   \n",
       "3         4            Waiting to Exhale (1995)   \n",
       "4         5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genero  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes_todos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>filme_id</th>\n",
       "      <th>título</th>\n",
       "      <th>genero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   filme_id            título                                       genero\n",
       "0         1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes_todos.query(\"filme_id == 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4.0\n",
       "516      4.0\n",
       "874      4.5\n",
       "1434     2.5\n",
       "1667     4.5\n",
       "        ... \n",
       "97364    2.5\n",
       "98479    4.0\n",
       "98666    2.5\n",
       "99497    3.0\n",
       "99534    5.0\n",
       "Name: avaliacao, Length: 215, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.query(\"filmes_id == 1\").avaliacao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Média por Filme (groupby())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Como agrupar por filmes e calcular as médias de uma só vez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "medias_por_filme = df_filmes.groupby('filmes_id').mean()['avaliacao']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filmes_id\n",
       "1    3.920930\n",
       "2    3.431818\n",
       "3    3.259615\n",
       "4    2.357143\n",
       "5    3.071429\n",
       "Name: avaliacao, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medias_por_filme.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "medias_por_filme.plot(kind='hist', color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='avaliacao'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=medias_por_filme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9724.000000\n",
       "mean        3.262448\n",
       "std         0.869874\n",
       "min         0.500000\n",
       "25%         2.800000\n",
       "50%         3.416667\n",
       "75%         3.911765\n",
       "max         5.000000\n",
       "Name: avaliacao, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medias_por_filme.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='avaliacao'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxenplot(x=medias_por_filme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='avaliacao', ylabel='Count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(medias_por_filme,kde=True,bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Contando uma história dos filmes')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(medias_por_filme)\n",
    "plt.title(\"Contando uma história dos filmes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercício - Exporte a planilha no formato .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pesquisa = pd.read_csv('datasets/pesquisa.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Carimbo de data/hora</th>\n",
       "      <th>Endereço de e-mail</th>\n",
       "      <th>Pontuação</th>\n",
       "      <th>Responda qual a sua compreensão</th>\n",
       "      <th>Quais tópicos de estatística você acha mais desafiadores</th>\n",
       "      <th>Como você se sente em relação ao estudo de estatística</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>20/08/2024 17:02:00</td>\n",
       "      <td>neutrin.fullstack@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Carimbo de data/hora           Endereço de e-mail Pontuação  \\\n",
       "count                    17                           17        17   \n",
       "unique                   16                           17         1   \n",
       "top     20/08/2024 17:02:00  neutrin.fullstack@gmail.com     0 / 1   \n",
       "freq                      2                            1        17   \n",
       "\n",
       "       Responda qual a sua compreensão  \\\n",
       "count                               17   \n",
       "unique                               3   \n",
       "top                                boa   \n",
       "freq                                 9   \n",
       "\n",
       "       Quais tópicos de estatística você acha mais desafiadores  \\\n",
       "count                                                  17         \n",
       "unique                                                  4         \n",
       "top                                               quartis         \n",
       "freq                                                    9         \n",
       "\n",
       "       Como você se sente em relação ao estudo de estatística  \n",
       "count                                                  17      \n",
       "unique                                                  3      \n",
       "top                                       tenho interesse      \n",
       "freq                                                    8      "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesquisa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Carimbo de data/hora', 'Endereço de e-mail', 'Pontuação',\n",
       "       'Responda qual a sua compreensão',\n",
       "       'Quais tópicos de estatística você acha mais desafiadores',\n",
       "       'Como você se sente em relação ao estudo de estatística'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesquisa.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renomeie todas as colunas\n",
    "['data_hora','email','compreensao','desafiador','sentimento']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Carimbo de data/hora</th>\n",
       "      <th>Endereço de e-mail</th>\n",
       "      <th>Pontuação</th>\n",
       "      <th>Responda qual a sua compreensão</th>\n",
       "      <th>Quais tópicos de estatística você acha mais desafiadores</th>\n",
       "      <th>Como você se sente em relação ao estudo de estatística</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20/08/2024 16:24:25</td>\n",
       "      <td>neutrin.fullstack@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>população</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20/08/2024 17:00:24</td>\n",
       "      <td>henriqueserafin7@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20/08/2024 17:00:26</td>\n",
       "      <td>jpguiotti@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>população</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20/08/2024 17:01:02</td>\n",
       "      <td>thi.henriquemaia@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/08/2024 17:01:18</td>\n",
       "      <td>eulete.souza@df.estudante.senai.br</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>quartis</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20/08/2024 17:01:57</td>\n",
       "      <td>fabriciotaveira176@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>ótima</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20/08/2024 17:02:00</td>\n",
       "      <td>dcguidosenai@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20/08/2024 17:02:00</td>\n",
       "      <td>valleriam@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>média</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20/08/2024 17:02:32</td>\n",
       "      <td>andrerodriguesfeitosa9@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>amostra</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20/08/2024 17:02:51</td>\n",
       "      <td>engribeiro2017@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>ótima</td>\n",
       "      <td>população</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20/08/2024 17:02:53</td>\n",
       "      <td>jessicaportessantos@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20/08/2024 17:02:56</td>\n",
       "      <td>julianarochaximenes@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20/08/2024 17:03:32</td>\n",
       "      <td>escola.saga.caio@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20/08/2024 17:04:13</td>\n",
       "      <td>lafaietepedro3@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20/08/2024 17:05:14</td>\n",
       "      <td>palmeiravictor53@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>média</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20/08/2024 17:10:31</td>\n",
       "      <td>pjeronimo842@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>média</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20/08/2024 17:11:37</td>\n",
       "      <td>leandrosousajesus@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>população</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carimbo de data/hora                  Endereço de e-mail Pontuação  \\\n",
       "0   20/08/2024 16:24:25         neutrin.fullstack@gmail.com     0 / 1   \n",
       "1   20/08/2024 17:00:24          henriqueserafin7@gmail.com     0 / 1   \n",
       "2   20/08/2024 17:00:26                 jpguiotti@gmail.com     0 / 1   \n",
       "3   20/08/2024 17:01:02          thi.henriquemaia@gmail.com     0 / 1   \n",
       "4   20/08/2024 17:01:18  eulete.souza@df.estudante.senai.br     0 / 1   \n",
       "5   20/08/2024 17:01:57        fabriciotaveira176@gmail.com     0 / 1   \n",
       "6   20/08/2024 17:02:00              dcguidosenai@gmail.com     0 / 1   \n",
       "7   20/08/2024 17:02:00                 valleriam@gmail.com     0 / 1   \n",
       "8   20/08/2024 17:02:32    andrerodriguesfeitosa9@gmail.com     0 / 1   \n",
       "9   20/08/2024 17:02:51            engribeiro2017@gmail.com     0 / 1   \n",
       "10  20/08/2024 17:02:53       jessicaportessantos@gmail.com     0 / 1   \n",
       "11  20/08/2024 17:02:56       julianarochaximenes@gmail.com     0 / 1   \n",
       "12  20/08/2024 17:03:32          escola.saga.caio@gmail.com     0 / 1   \n",
       "13  20/08/2024 17:04:13            lafaietepedro3@gmail.com     0 / 1   \n",
       "14  20/08/2024 17:05:14          palmeiravictor53@gmail.com     0 / 1   \n",
       "15  20/08/2024 17:10:31              pjeronimo842@gmail.com     0 / 1   \n",
       "16  20/08/2024 17:11:37         leandrosousajesus@gmail.com     0 / 1   \n",
       "\n",
       "   Responda qual a sua compreensão  \\\n",
       "0                          regular   \n",
       "1                              boa   \n",
       "2                              boa   \n",
       "3                          regular   \n",
       "4                          regular   \n",
       "5                            ótima   \n",
       "6                              boa   \n",
       "7                          regular   \n",
       "8                              boa   \n",
       "9                            ótima   \n",
       "10                         regular   \n",
       "11                             boa   \n",
       "12                             boa   \n",
       "13                             boa   \n",
       "14                             boa   \n",
       "15                         regular   \n",
       "16                             boa   \n",
       "\n",
       "   Quais tópicos de estatística você acha mais desafiadores  \\\n",
       "0                                           população         \n",
       "1                                             quartis         \n",
       "2                                           população         \n",
       "3                                             quartis         \n",
       "4                                             quartis         \n",
       "5                                             quartis         \n",
       "6                                             quartis         \n",
       "7                                               média         \n",
       "8                                             amostra         \n",
       "9                                           população         \n",
       "10                                            quartis         \n",
       "11                                            quartis         \n",
       "12                                            quartis         \n",
       "13                                            quartis         \n",
       "14                                              média         \n",
       "15                                              média         \n",
       "16                                          população         \n",
       "\n",
       "   Como você se sente em relação ao estudo de estatística  \n",
       "0                                        entusiasmado      \n",
       "1                                        entusiasmado      \n",
       "2                                     tenho interesse      \n",
       "3                                     tenho interesse      \n",
       "4                                        entusiasmado      \n",
       "5                                     tenho interesse      \n",
       "6                                     tenho interesse      \n",
       "7                                     tenho interesse      \n",
       "8                                     tenho interesse      \n",
       "9                                        entusiasmado      \n",
       "10                                    tenho interesse      \n",
       "11                                                 ok      \n",
       "12                                                 ok      \n",
       "13                                                 ok      \n",
       "14                                       entusiasmado      \n",
       "15                                    tenho interesse      \n",
       "16                                       entusiasmado      "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesquisa.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pesquisa.columns = ['Data_hora','Email','Pontuação','Compreensão','Desafiador','Sentimento']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data_hora</th>\n",
       "      <th>Email</th>\n",
       "      <th>Pontuação</th>\n",
       "      <th>Compreensão</th>\n",
       "      <th>Desafiador</th>\n",
       "      <th>Sentimento</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20/08/2024 16:24:25</td>\n",
       "      <td>neutrin.fullstack@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>população</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20/08/2024 17:00:24</td>\n",
       "      <td>henriqueserafin7@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20/08/2024 17:00:26</td>\n",
       "      <td>jpguiotti@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>população</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20/08/2024 17:01:02</td>\n",
       "      <td>thi.henriquemaia@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/08/2024 17:01:18</td>\n",
       "      <td>eulete.souza@df.estudante.senai.br</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>quartis</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20/08/2024 17:01:57</td>\n",
       "      <td>fabriciotaveira176@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>ótima</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20/08/2024 17:02:00</td>\n",
       "      <td>dcguidosenai@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20/08/2024 17:02:00</td>\n",
       "      <td>valleriam@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>média</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20/08/2024 17:02:32</td>\n",
       "      <td>andrerodriguesfeitosa9@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>amostra</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20/08/2024 17:02:51</td>\n",
       "      <td>engribeiro2017@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>ótima</td>\n",
       "      <td>população</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20/08/2024 17:02:53</td>\n",
       "      <td>jessicaportessantos@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>quartis</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20/08/2024 17:02:56</td>\n",
       "      <td>julianarochaximenes@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20/08/2024 17:03:32</td>\n",
       "      <td>escola.saga.caio@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20/08/2024 17:04:13</td>\n",
       "      <td>lafaietepedro3@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>quartis</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20/08/2024 17:05:14</td>\n",
       "      <td>palmeiravictor53@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>média</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20/08/2024 17:10:31</td>\n",
       "      <td>pjeronimo842@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>regular</td>\n",
       "      <td>média</td>\n",
       "      <td>tenho interesse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20/08/2024 17:11:37</td>\n",
       "      <td>leandrosousajesus@gmail.com</td>\n",
       "      <td>0 / 1</td>\n",
       "      <td>boa</td>\n",
       "      <td>população</td>\n",
       "      <td>entusiasmado</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Data_hora                               Email Pontuação  \\\n",
       "0   20/08/2024 16:24:25         neutrin.fullstack@gmail.com     0 / 1   \n",
       "1   20/08/2024 17:00:24          henriqueserafin7@gmail.com     0 / 1   \n",
       "2   20/08/2024 17:00:26                 jpguiotti@gmail.com     0 / 1   \n",
       "3   20/08/2024 17:01:02          thi.henriquemaia@gmail.com     0 / 1   \n",
       "4   20/08/2024 17:01:18  eulete.souza@df.estudante.senai.br     0 / 1   \n",
       "5   20/08/2024 17:01:57        fabriciotaveira176@gmail.com     0 / 1   \n",
       "6   20/08/2024 17:02:00              dcguidosenai@gmail.com     0 / 1   \n",
       "7   20/08/2024 17:02:00                 valleriam@gmail.com     0 / 1   \n",
       "8   20/08/2024 17:02:32    andrerodriguesfeitosa9@gmail.com     0 / 1   \n",
       "9   20/08/2024 17:02:51            engribeiro2017@gmail.com     0 / 1   \n",
       "10  20/08/2024 17:02:53       jessicaportessantos@gmail.com     0 / 1   \n",
       "11  20/08/2024 17:02:56       julianarochaximenes@gmail.com     0 / 1   \n",
       "12  20/08/2024 17:03:32          escola.saga.caio@gmail.com     0 / 1   \n",
       "13  20/08/2024 17:04:13            lafaietepedro3@gmail.com     0 / 1   \n",
       "14  20/08/2024 17:05:14          palmeiravictor53@gmail.com     0 / 1   \n",
       "15  20/08/2024 17:10:31              pjeronimo842@gmail.com     0 / 1   \n",
       "16  20/08/2024 17:11:37         leandrosousajesus@gmail.com     0 / 1   \n",
       "\n",
       "   Compreensão Desafiador       Sentimento  \n",
       "0      regular  população     entusiasmado  \n",
       "1          boa    quartis     entusiasmado  \n",
       "2          boa  população  tenho interesse  \n",
       "3      regular    quartis  tenho interesse  \n",
       "4      regular    quartis     entusiasmado  \n",
       "5        ótima    quartis  tenho interesse  \n",
       "6          boa    quartis  tenho interesse  \n",
       "7      regular      média  tenho interesse  \n",
       "8          boa    amostra  tenho interesse  \n",
       "9        ótima  população     entusiasmado  \n",
       "10     regular    quartis  tenho interesse  \n",
       "11         boa    quartis               ok  \n",
       "12         boa    quartis               ok  \n",
       "13         boa    quartis               ok  \n",
       "14         boa      média     entusiasmado  \n",
       "15     regular      média  tenho interesse  \n",
       "16         boa  população     entusiasmado  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pesquisa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sentimento', ylabel='Porcentagem'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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shXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAs5bIIN4mJiQoODpaXl5fCw8O1bt26c/Zdvny5OnTooFq1aqlGjRoKCwvTokWLLmG1AADgclbl4WbJkiWKjY3V+PHjtXHjRrVp00ZRUVE6cOBAmf1r166tf/3rX1q7dq02b96smJgYxcTEaOXKlZe4cgAAcDmq5uzA9evXa9WqVTpw4IBKSkocXpsxY0a5tzNjxgwNGzZMMTExkqTZs2fr448/VlJSkp566qlS/W+99VaH54899pgWLFig1atXKyoqqlT/wsJCFRYW2p/n5eWVuzYAAHDlcSrcvPTSS3rmmWcUGhqqwMBA2Ww2+2t//PeFFBUVacOGDYqLi7O3ubm5KTIyUmvXrr3geGOMvvzyS2VkZGjy5Mll9klISNCECRPKXRMAALiyORVuZs2apaSkJA0ZMqRSO8/NzVVxcbECAwMd2gMDA7V9+/Zzjjt27JgaNGigwsJCubu767XXXlOvXr3K7BsXF6fY2Fj787y8PDVs2LBSdQMAgMuXU+HGzc1NERERrq6l3GrWrKm0tDTl5+crJSVFsbGxaty4calLVpLk6ekpT0/PS18kAACoEk4tKB4zZowSExMrvfOAgAC5u7srJyfHoT0nJ0dBQUHnHOfm5qYmTZooLCxMTzzxhPr376+EhIRK1wMAAK58Ts3cjB07VnfddZdCQkLUsmVLVa9e3eH15cuXl2s7Hh4eat++vVJSUtS3b19JUklJiVJSUjRy5Mhy11NSUuKwaBgAAFy9nAo3o0eP1qpVq9SjRw9de+21FVpE/GexsbGKjo5Whw4d1KlTJ82cOVMFBQX2T08NHjxYDRo0sM/MJCQkqEOHDgoJCVFhYaE++eQTLVq0SK+//rrTNQAAAOtwKtwsWLBAy5Yt01133VXpAgYMGKCDBw8qPj5e2dnZCgsLU3Jysn2RcVZWltzc/nf1rKCgQCNGjNCvv/4qb29vNW/eXG+//bYGDBhQ6VoAAMCVz6lwU7t2bYWEhLisiJEjR57zMlRqaqrD8xdeeEEvvPCCy/YNAACsxakFxc8995zGjx+vEydOuLoeAACASnFq5ubf//63du3apcDAQAUHB5daULxx40aXFAcAAFBRToWbs59sAgAAuNw4FW7Gjx/v6joAAABcwulvBT969KjefPNNxcXF6fDhw5J+vxz122+/uaw4AACAinJq5mbz5s2KjIyUv7+/MjMzNWzYMNWuXVvLly9XVlaWFi5c6Oo6AQAAysWpmZvY2FgNGTJEO3fulJeXl729d+/e+vrrr11WHAAAQEU5FW7Wr1+vhx9+uFR7gwYNlJ2dXemiAAAAnOVUuPH09FReXl6p9h07dqhOnTqVLgoAAMBZToWbe+65R88//7xOnz4tSbLZbMrKytKTTz6pfv36ubRAAACAinAq3EyfPl35+fmqW7euTp48qe7du6tJkyaqWbOmXnzxRVfXCAAAUG5OfVrK399fn3/+uVavXq3NmzcrPz9f7dq1U2RkpKvrAwAAqBCnws1Zt9xyi2655RZX1QIAAFBpTn+3VFlsNpu8vLzUpEkTdevWTe7u7pUqDgAAoKKcCjcvv/yyDh48qBMnTuiaa66RJB05ckQ+Pj7y9fXVgQMH1LhxY61atUoNGzZ0acEAAADn49SC4pdeekkdO3bUzp07dejQIR06dEg7duxQeHi4Zs2apaysLAUFBWnMmDGurhcAAOC8nJq5eeaZZ7Rs2TKFhITY25o0aaJp06apX79++uWXXzRlyhQ+Fg4AAC45p2Zu9u/frzNnzpRqP3PmjP0OxfXr19fx48crVx0AAEAFORVuevTooYcfflibNm2yt23atEnDhw/XbbfdJknasmWLbrjhBtdUCQAAUE5OhZu33npLtWvXVvv27eXp6SlPT0916NBBtWvX1ltvvSVJ8vX11fTp011aLAAAwIU4teYmKChIn3/+ubZv364dO3ZIkkJDQxUaGmrv06NHD9dUCAAAUAGVuolf8+bN1bx5c1fVAgAAUGlOh5tff/1VH374obKyslRUVOTw2owZMypdGAAAgDOcCjcpKSm655571LhxY23fvl033XSTMjMzZYxRu3btXF0jAABAuTm1oDguLk5jx47Vli1b5OXlpWXLlmnv3r3q3r277r//flfXCAAAUG5OhZv09HQNHjxYklStWjWdPHlSvr6+ev755zV58mSXFggAAFARToWbGjVq2NfZ1KtXT7t27bK/lpub65rKAAAAnODUmpubb75Zq1evVosWLdS7d2898cQT2rJli5YvX66bb77Z1TUCAACUm1PhZsaMGcrPz5ckTZgwQfn5+VqyZImaNm3KJ6UAAECVcircNG7c2P7vGjVqaPbs2S4rCAAAoDKcWnPTuHFjHTp0qFT70aNHHYIPAADApeZUuMnMzFRxcXGp9sLCQv3222+VLgoAAMBZFbos9eGHH9r/vXLlSvn7+9ufFxcXKyUlRcHBwS4rDgAAoKIqFG769u0rSbLZbIqOjnZ4rXr16goODuabwAEAQJWqULgpKSmRJN1www1av369AgICLkpRAAAAznLq01K7d+92dR0AAAAu4fS3gqekpCglJUUHDhywz+iclZSUVOnCAAAAnOFUuJkwYYKef/55dejQQfXq1ZPNZnN1XQAAAE5xKtzMnj1b8+fP16BBg1xdDwAAQKU4dZ+boqIidenSxdW1AAAAVJpT4eYf//iH3n33XVfXAgAAUGlOXZY6deqU5syZoy+++EKtW7dW9erVHV7nyzMBAEBVcSrcbN68WWFhYZKkn376yeE1FhcDAICq5FS4WbVqlavrAAAAcAmn1tyc9fPPP2vlypU6efKkJMkY45KiAAAAnOVUuDl06JB69uypZs2aqXfv3tq/f78kaejQoXriiSdcWiAAAEBFOBVuxowZo+rVqysrK0s+Pj729gEDBig5OdllxQEAAFSUU2tuPvvsM61cuVLXXXedQ3vTpk21Z88elxQGAADgDKdmbgoKChxmbM46fPiwPD09K10UAACAs5wKN127dtXChQvtz202m0pKSjRlyhT16NHDZcUBAABUlFOXpaZMmaKePXvqhx9+UFFRkf75z39q69atOnz4sNasWePqGgEAAMrNqZmbm266STt27NAtt9yiPn36qKCgQPfdd582bdqkkJAQV9cIAABQbk7N3EiSv7+//vWvf7myFgAAgEpzauZm3rx5Wrp0aan2pUuXasGCBZUuCgAAwFlOhZuEhAQFBASUaq9bt65eeumlShcFAADgLKfCTVZWlm644YZS7Y0aNVJWVlaliwIAAHCWU+Gmbt262rx5c6n2H3/8Uddee22liwIAAHCWU+HmgQce0OjRo7Vq1SoVFxeruLhYX375pR577DENHDjQ1TUCAACUm1Oflpo4caIyMzPVs2dPVav2+yZKSko0ePBg1twAAIAqVeFwY4xRdna25s+frxdeeEFpaWny9vZWq1at1KhRo4tRIwAAQLk5FW6aNGmirVu3qmnTpmratOnFqAsAAMApFV5z4+bmpqZNm+rQoUMXox4AAIBKcWpB8aRJkzRu3Dj99NNPrq4HAACgUpxaUDx48GCdOHFCbdq0kYeHh7y9vR1eP3z4sEuKAwAAqCinws3MmTNdWkRiYqKmTp2q7OxstWnTRq+88oo6depUZt+5c+dq4cKF9lmj9u3b66WXXjpnfwAAcHVxKtxER0e7rIAlS5YoNjZWs2fPVnh4uGbOnKmoqChlZGSobt26pfqnpqbqgQceUJcuXeTl5aXJkyfr9ttv19atW9WgQQOX1QUAAK5MTn8reHFxsVasWKH09HRJ0o033qh77rlH7u7uFdrOjBkzNGzYMMXExEiSZs+erY8//lhJSUl66qmnSvV/5513HJ6/+eabWrZsmVJSUjR48OBS/QsLC1VYWGh/npeXV6H6AADAlcWpBcU///yzWrRoocGDB2v58uVavny5/v73v+vGG2/Url27yr2doqIibdiwQZGRkf8ryM1NkZGRWrt2bbm2ceLECZ0+fVq1a9cu8/WEhAT5+/vbHw0bNix3fQAA4MrjVLgZPXq0QkJCtHfvXm3cuFEbN260f5nm6NGjy72d3NxcFRcXKzAw0KE9MDBQ2dnZ5drGk08+qfr16zsEpD+Ki4vTsWPH7I+9e/eWuz4AAHDlceqy1FdffaXvvvvOYbbk2muv1aRJkxQREeGy4i5k0qRJWrx4sVJTU+Xl5VVmH09PT3l6el6ymgAAQNVyKtx4enrq+PHjpdrz8/Pl4eFR7u0EBATI3d1dOTk5Du05OTkKCgo679hp06Zp0qRJ+uKLL9S6dety7xMAAFibU5el/vKXv+j//u//9P3338sYI2OMvvvuOz3yyCO65557yr0dDw8PtW/fXikpKfa2kpISpaSkqHPnzuccN2XKFE2cOFHJycnq0KGDM4cAAAAsyqlw8+9//1tNmjSxfxzby8tLERERatKkiWbNmlWhbcXGxmru3LlasGCB0tPTNXz4cBUUFNg/PTV48GDFxcXZ+0+ePFnPPvuskpKSFBwcrOzsbGVnZys/P9+ZQwEAABZToXBTUlKiyZMn66677tJvv/2mvn37aunSpXr//feVkZGhDz74QP7+/hUqYMCAAZo2bZri4+MVFhamtLQ0JScn2xcZZ2Vlaf/+/fb+r7/+uoqKitS/f3/Vq1fP/pg2bVqF9gsAwLkkJiYqODhYXl5eCg8P17p1687Zd+vWrerXr5+Cg4Nls9nKvNFtcXGxnn32Wd1www3y9vZWSEiIJk6cKGOMvc+0adNUt25d1a1bV9OnT3cY//3336t9+/Y6c+aMy47Ryiq05ubFF1/Uc889p8jISHl7e+uTTz6Rv7+/kpKSKlXEyJEjNXLkyDJfS01NdXiemZlZqX0BAHA+Fb257IkTJ9S4cWPdf//9GjNmTJnbnDx5sl5//XUtWLBAN954o3744QfFxMTI399fo0eP1ubNmxUfH6+PPvpIxhj95S9/0e23365WrVrpzJkzeuSRRzRnzhxVq+b07emuKhWauVm4cKFee+01rVy5UitWrNB///tfvfPOOyopKblY9QEAcEn98eayLVu21OzZs+Xj43PO/5Hv2LGjpk6dqoEDB57z07nffvut+vTpo7vuukvBwcHq37+/br/9dvuM0Pbt29W6dWvddttt6tmzp1q3bq3t27dLkqZOnapu3bqpY8eOF+eALahC4SYrK0u9e/e2P4+MjJTNZtO+fftcXhgAAJeaK24uW5YuXbooJSVFO3bskCT9+OOPWr16te68805JUqtWrbRjxw5lZWVpz5492rFjh2666Sbt2rVL8+bN0wsvvFC5A7vKVGh+68yZM6XuJ1O9enWdPn3apUUBAFAVzndz2bMzKc546qmnlJeXp+bNm8vd3V3FxcV68cUX9eCDD0qSWrRooZdeekm9evWS9Pvd9Vu0aKHIyEhNmTJFK1eu1HPPPafq1atr1qxZ6tatm/MHeRWoULgxxmjIkCEO026nTp3SI488oho1atjbli9f7roKAQC4wr333nt655139O677+rGG29UWlqaHn/8cdWvX9/+ZdSPPPKIHnnkEfuYBQsWqGbNmurcubNCQ0O1fv16/frrrxo4cKB2797NDWrPo0LhpqxvA//73//usmIAAKhKlbm57PmMGzdOTz31lAYOHCjp98tQe/bsUUJCQpl/W3NzczVhwgR9/fXX+v7779WsWTM1bdpUTZs21enTp7Vjxw61atXK6XqsrkLhZt68eRerDgAAqtwfby7bt29fSf+7uey5PtVbHidOnJCbm+MyV3d393N+IGfMmDEaM2aMrrvuOq1fv95h+ceZM2dUXFzsdC1XAz5TBgDAH8TGxio6OlodOnRQp06dNHPmzFI3l23QoIESEhIk/b4Iedu2bfZ///bbb0pLS5Ovr6+aNGkiSbr77rv14osv6vrrr9eNN96oTZs2acaMGXrooYdK7f/zzz/Xjh07tGDBAkm/fxpr+/bt+vTTT7V37165u7srNDT0UrwVVyzCDQAAfzBgwAAdPHhQ8fHxys7OVlhYWKmby/5xFmbfvn1q27at/fm0adM0bdo0de/e3X6vtldeeUXPPvusRowYoQMHDqh+/fp6+OGHFR8f77DvkydPauTIkVqyZIl9H9ddd51eeeUVxcTEyNPTUwsWLJC3t/dFfheubDbzx9sjXgXy8vLk7++vY8eOyc/Pz+nttB+30IVV4Uq3Yergqi6BcxIOLodzEnClivz9duq7pQAAAC5XXJYCAFwUzCbizy7VjCIzNwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFKqPNwkJiYqODhYXl5eCg8P17p1687Zd+vWrerXr5+Cg4Nls9k0c+bMS1coAAC4IlRpuFmyZIliY2M1fvx4bdy4UW3atFFUVJQOHDhQZv8TJ06ocePGmjRpkoKCgi5xtQAA4EpQpeFmxowZGjZsmGJiYtSyZUvNnj1bPj4+SkpKKrN/x44dNXXqVA0cOFCenp6XuFoAAHAlqLJwU1RUpA0bNigyMvJ/xbi5KTIyUmvXrnXZfgoLC5WXl+fwAAAA1lVl4SY3N1fFxcUKDAx0aA8MDFR2drbL9pOQkCB/f3/7o2HDhi7bNgAAuPxU+YLiiy0uLk7Hjh2zP/bu3VvVJQEAgIuoWlXtOCAgQO7u7srJyXFoz8nJceliYU9PT9bnAABwFamymRsPDw+1b99eKSkp9raSkhKlpKSoc+fOVVUWAAC4wlXZzI0kxcbGKjo6Wh06dFCnTp00c+ZMFRQUKCYmRpI0ePBgNWjQQAkJCZJ+X4S8bds2+79/++03paWlydfXV02aNKmy4wAAAJePKg03AwYM0MGDBxUfH6/s7GyFhYUpOTnZvsg4KytLbm7/m1zat2+f2rZta38+bdo0TZs2Td27d1dqauqlLh8AAFyGqjTcSNLIkSM1cuTIMl/7c2AJDg6WMeYSVAUAAK5Ulv+0FAAAuLoQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKVcFuEmMTFRwcHB8vLyUnh4uNatW3fe/kuXLlXz5s3l5eWlVq1a6ZNPPrlElQIAgMtdlYebJUuWKDY2VuPHj9fGjRvVpk0bRUVF6cCBA2X2//bbb/XAAw9o6NCh2rRpk/r27au+ffvqp59+usSVAwCAy1GVh5sZM2Zo2LBhiomJUcuWLTV79mz5+PgoKSmpzP6zZs3SHXfcoXHjxqlFixaaOHGi2rVrp1dfffUSVw4AAC5H1apy50VFRdqwYYPi4uLsbW5uboqMjNTatWvLHLN27VrFxsY6tEVFRWnFihVl9i8sLFRhYaH9+bFjxyRJeXl5laq9uPBkpcbDWip7PrkC5yT+iHMSl6PKnJdnxxpjLti3SsNNbm6uiouLFRgY6NAeGBio7du3lzkmOzu7zP7Z2dll9k9ISNCECRNKtTds2NDJqoHS/F95pKpLABxwTuJy5Irz8vjx4/L39z9vnyoNN5dCXFycw0xPSUmJDh8+rGuvvVY2m60KK7vy5eXlqWHDhtq7d6/8/PyquhyAcxKXJc5L1zDG6Pjx46pfv/4F+1ZpuAkICJC7u7tycnIc2nNychQUFFTmmKCgoAr19/T0lKenp0NbrVq1nC8apfj5+fELi8sK5yQuR5yXlXehGZuzqnRBsYeHh9q3b6+UlBR7W0lJiVJSUtS5c+cyx3Tu3NmhvyR9/vnn5+wPAACuLlV+WSo2NlbR0dHq0KGDOnXqpJkzZ6qgoEAxMTGSpMGDB6tBgwZKSEiQJD322GPq3r27pk+frrvuukuLFy/WDz/8oDlz5lTlYQAAgMtElYebAQMG6ODBg4qPj1d2drbCwsKUnJxsXzSclZUlN7f/TTB16dJF7777rp555hk9/fTTatq0qVasWKGbbrqpqg7hquXp6anx48eXuuwHVBXOSVyOOC8vPZspz2eqAAAArhBVfhM/AAAAVyLcAAAASyHcAAAASyHcWITNZjvnV1A4a/78+dwTCJeNzMxM2Ww2paWlVXUpFcbvElJTU2Wz2XT06NGqLuWqQLi5hG699VY9/vjjVV1GuQ0YMEA7duyo0Jgr7RhxcTz33HMKCwtz6TYbNmyo/fv388lIABdU5R8Fx+XL29tb3t7eVbLvoqIieXh4VMm+cXlyd3c/553IAeCPmLm5RIYMGaKvvvpKs2bNks1mk81mU2ZmpiTpp59+0p133ilfX18FBgZq0KBBys3NtY+99dZbNXr0aP3zn/9U7dq1FRQUpOeee67UPnJzc3XvvffKx8dHTZs21Ycffujw+ldffaVOnTrJ09NT9erV01NPPaUzZ86cs+Y/T6Wf/b/xRYsWKTg4WP7+/ho4cKCOHz/ukmMcOXKkHn/8cQUEBCgqKqpc495//321atVK3t7euvbaaxUZGamCggJJv08Dd+rUSTVq1FCtWrUUERGhPXv22Mf+5z//Ubt27eTl5aXGjRtrwoQJ530/riYlJSVKSEjQDTfcIG9vb7Vp00bvv/++pP9Nr6ekpKhDhw7y8fFRly5dlJGRIen382bChAn68ccf7efB/Pnzy7ysdPToUdlsNqWmpkqSjhw5ogcffFB16tSRt7e3mjZtqnnz5kkqfVmquLhYQ4cOtdcYGhqqWbNmORzH+c6Bs+dzUlKSrr/+evn6+mrEiBEqLi7WlClTFBQUpLp16+rFF1902OaMGTPUqlUr1ahRQw0bNtSIESOUn5/v0Gf+/Pm6/vrr5ePjo3vvvVeHDh0q9R6//vrrCgkJkYeHh0JDQ7Vo0SLnfli4bBQWFmr06NGqW7euvLy8dMstt2j9+vVl9j1x4oTuvPNORUREcKnqYjC4JI4ePWo6d+5shg0bZvbv32/2799vzpw5Y44cOWLq1Klj4uLiTHp6utm4caPp1auX6dGjh31s9+7djZ+fn3nuuefMjh07zIIFC4zNZjOfffaZvY8kc91115l3333X7Ny504wePdr4+vqaQ4cOGWOM+fXXX42Pj48ZMWKESU9PNx988IEJCAgw48ePP2fN8+bNM/7+/vbn48ePN76+vua+++4zW7ZsMV9//bUJCgoyTz/9tEuO0dfX14wbN85s377dbN++/YLj9u3bZ6pVq2ZmzJhhdu/ebTZv3mwSExPN8ePHzenTp42/v78ZO3as+fnnn822bdvM/PnzzZ49e4wxxnz99dfGz8/PzJ8/3+zatct89tlnJjg42Dz33HOV/llbwQsvvGCaN29ukpOTza5du8y8efOMp6enSU1NNatWrTKSTHh4uElNTTVbt241Xbt2NV26dDHGGHPixAnzxBNPmBtvvNF+Hpw4ccLs3r3bSDKbNm2y7+fIkSNGklm1apUxxphHH33UhIWFmfXr15vdu3ebzz//3Hz44YfGGFNqfFFRkYmPjzfr1683v/zyi3n77beNj4+PWbJkiTHGXPAcOHs+9+/f32zdutV8+OGHxsPDw0RFRZlRo0aZ7du3m6SkJCPJfPfdd/aaX375ZfPll1+a3bt3m5SUFBMaGmqGDx9uf/27774zbm5uZvLkySYjI8PMmjXL1KpVy+F3afny5aZ69eomMTHRZGRkmOnTpxt3d3fz5ZdfuvpHiUto9OjRpn79+uaTTz4xW7duNdHR0eaaa64xhw4dsv/eHDlyxBw5csR06dLF3H777aagoKCqy7Ykws0l1L17d/PYY485tE2cONHcfvvtDm179+41kkxGRoZ93C233OLQp2PHjubJJ5+0P5dknnnmGfvz/Px8I8l8+umnxhhjnn76aRMaGmpKSkrsfRITE42vr68pLi4us96ywo2Pj4/Jy8uzt40bN86Eh4e75Bjbtm1boXEbNmwwkkxmZmap2g8dOmQkmdTU1DKPrWfPnuall15yaFu0aJGpV69emf2vJqdOnTI+Pj7m22+/dWgfOnSoeeCBB+z/kf7iiy/sr3388cdGkjl58qQx5vdzpU2bNg7jyxNu7r77bhMTE1NmXWWN/7NHH33U9OvXzxhz4XOgrPM5KirKBAcHO/xOhIaGmoSEhHPuc+nSpebaa6+1P3/ggQdM7969HfoMGDDA4XepS5cuZtiwYQ597r///lLjcOXIz8831atXN++88469raioyNSvX99MmTLF/nuTnp5uWrdubfr162cKCwursGJr47JUFfvxxx+1atUq+fr62h/NmzeXJO3atcver3Xr1g7j6tWrpwMHDji0/bFPjRo15OfnZ++Tnp6uzp07y2az2ftEREQoPz9fv/76a7nrDQ4OVs2aNc9bh7PH2L59+wqNa9OmjXr27KlWrVrp/vvv19y5c3XkyBFJUu3atTVkyBBFRUXp7rvv1qxZs7R//36HbT///PMO2x42bJj279+vEydOlPv9sKKff/5ZJ06cUK9evRzen4ULF57znKxXr54kXfBcuJDhw4dr8eLFCgsL0z//+U99++235+2fmJio9u3bq06dOvL19dWcOXOUlZUl6cLngFT6fA4MDFTLli0dvvIlMDDQ4bi++OIL9ezZUw0aNFDNmjU1aNAgHTp0yH7epKenKzw83GE/f/5i3/T0dEVERDi0RUREKD09/UJvES5Tu3bt0unTpx1+rtWrV1enTp0cfq69evVSkyZNtGTJEtYVXkSEmyqWn5+vu+++W2lpaQ6PnTt3qlu3bvZ+1atXdxhns9lUUlLi0FaePpXlzD7Ke4w1atSo0Dh3d3d9/vnn+vTTT9WyZUu98sorCg0N1e7duyVJ8+bN09q1a9WlSxctWbJEzZo103fffWff9oQJExy2u2XLFu3cuVNeXl6ueKuuWGfXj3z88ccO78+2bdvs624kx3PhbGg+37lwNjCYP3zjy+nTpx363HnnndqzZ4/GjBmjffv2qWfPnho7dmyZ21u8eLHGjh2roUOH6rPPPlNaWppiYmJUVFRk73O+c+DPx3D2OM53jmdmZuovf/mLWrdurWXLlmnDhg1KTEyUJIf9Audy11136euvv9a2bduquhRL49NSl5CHh4eKi4sd2tq1a6dly5YpODhY1apdvB9HixYttGzZMhlj7H+I1qxZo5o1a+q6665z2X5ceYzlGWez2RQREaGIiAjFx8erUaNG+uCDDxQbGytJatu2rdq2bau4uDh17txZ7777rm6++Wa1a9dOGRkZatKkifMHa1EtW7aUp6ensrKy1L1791Kv/3H25lzKOg/q1KkjSdq/f7/atm0rSWXes6ZOnTqKjo5WdHS0unbtqnHjxmnatGml+q1Zs0ZdunTRiBEjzlvbuc4BZ2zYsEElJSWaPn26Pay99957Dn1atGih77//3qHtj4HqbJ81a9YoOjra4XhatmzpVF2oemcXh69Zs0aNGjWS9Ht4X79+vcPtMSZNmiRfX1/17NlTqamp/MwvEmZuLqHg4GB9//33yszMVG5urkpKSvToo4/q8OHDeuCBB7R+/Xrt2rVLK1euVExMTKk/DpUxYsQI7d27V6NGjdL27dv1n//8R+PHj1dsbKzDFHxlufIYLzTu+++/10svvaQffvhBWVlZWr58uQ4ePKgWLVpo9+7diouL09q1a7Vnzx599tln2rlzp1q0aCFJio+P18KFCzVhwgRt3bpV6enpWrx4sZ555hmXvRdXqpo1a2rs2LEaM2aMFixYoF27dmnjxo165ZVXtGDBgnJtIzg4WLt371ZaWppyc3NVWFgob29v3XzzzZo0aZLS09P11VdflXq/4+Pj9Z///Ec///yztm7dqo8++sj+M/uzpk2b6ocfftDKlSu1Y8cOPfvssw6fTLnQOeCMJk2a6PTp03rllVf0yy+/aNGiRZo9e7ZDn9GjRys5OVnTpk3Tzp079eqrryo5Odmhz7hx4zR//ny9/vrr2rlzp2bMmKHly5efc5YKl78aNWpo+PDhGjdunJKTk7Vt2zYNGzZMJ06c0NChQx36Tps2TQ8++KBuu+02bd++vYoqtriqXvRzNcnIyDA333yz8fb2NpLM7t27jTHG7Nixw9x7772mVq1axtvb2zRv3tw8/vjj9sW/ZS3S7dOnj4mOjrY/l2Q++OADhz7+/v5m3rx59uepqammY8eOxsPDwwQFBZknn3zSnD59+pz1lrWg+M+LRF9++WXTqFGji3KMFxq3bds2ExUVZerUqWM8PT1Ns2bNzCuvvGKMMSY7O9v07dvX1KtXz3h4eJhGjRqZ+Ph4h4WiycnJpkuXLsbb29v4+fmZTp06mTlz5pzz/bialJSUmJkzZ5rQ0FBTvXp1U6dOHRMVFWW++uorh099nLVp0yaHn/epU6dMv379TK1atYwk+3m4bds207lzZ+Pt7W3CwsLMZ5995rCgeOLEiaZFixbG29vb1K5d2/Tp08f88ssvxpjSC4pPnTplhgwZYvz9/U2tWrXM8OHDzVNPPWU/Ry90DpR1PkdHR5s+ffo4tP353JwxY4apV6+e8fb2NlFRUWbhwoWl3o+33nrLXHfddcbb29vcfffdZtq0aQ6/S8YY89prr5nGjRub6tWrm2bNmpmFCxdW5EeEy9DJkyfNqFGjTEBAgPH09DQRERFm3bp1xhhT5u/NqFGjTL169ewfrIDr2Iz5wwVwAACAKxyXpQAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgBcUVJTU2Wz2XT06NGqLgXAZYpwA8ApBw8e1PDhw3X99dfL09NTQUFBioqK0po1a1y2j1tvvdXhSwclqUuXLtq/f7/8/f1dth9nDRkyRH379q3qMgD8Cd8KDsAp/fr1U1FRkRYsWKDGjRsrJydHKSkpOnTo0EXdr4eHh4KCgi7qPgBc4ar6y60AXHmOHDliJJnU1NTz9hk6dKgJCAgwNWvWND169DBpaWn2189+ceXChQtNo0aNjJ+fnxkwYIDJy8szxvz+JZaSHB67d+8u9QWEZ7/g9b///a9p1qyZ8fb2Nv369TMFBQVm/vz5plGjRqZWrVpm1KhR5syZM/b9nzp1yjzxxBOmfv36xsfHx3Tq1Mn+BZ5/3G5ycrJp3ry5qVGjhomKijL79u2z1//n+s6O37x5s+nRo4fx8vIytWvXNsOGDTPHjx930bsP4EK4LAWgwnx9feXr66sVK1aosLCwzD7333+/Dhw4oE8//VQbNmxQu3bt1LNnTx0+fNjeZ9euXVqxYoU++ugjffTRR/rqq680adIkSdKsWbPUuXNnDRs2TPv379f+/fvVsGHDMvd14sQJ/fvf/9bixYuVnJys1NRU3Xvvvfrkk0/0ySefaNGiRXrjjTf0/vvv28eMHDlSa9eu1eLFi7V582bdf//9uuOOO7Rz506H7U6bNk2LFi3S119/raysLI0dO1aSNHbsWP31r3/VHXfcYa+vS5cuKigoUFRUlK655hqtX79eS5cu1RdffKGRI0dW+n0HUE5Vna4AXJnef/99c8011xgvLy/TpUsXExcXZ3788UdjjDHffPON8fPzM6dOnXIYExISYt544w1jzO8zHz4+PvaZGmOMGTdunAkPD7c/7969u3nssccctlHWzI0k8/PPP9v7PPzww8bHx8dhtiQqKso8/PDDxhhj9uzZY9zd3c1vv/3msO2ePXuauLi4c243MTHRBAYG2p9HR0ebPn36OGxjzpw55pprrjH5+fn2to8//ti4ubmZ7OzsMt5JAK7GmhsATunXr5/uuusuffPNN/ruu+/06aefasqUKXrzzTdVUFCg/Px8XXvttQ5jTp48qV27dtmfBwcHq2bNmvbn9erV04EDBypci4+Pj0JCQuzPAwMDFRwcLF9fX4e2s9vesmWLiouL1axZM4ftFBYWOtT85+2Wp7709HS1adNGNWrUsLdFRESopKREGRkZCgwMrPDxAagYwg0Ap3l5ealXr17q1auXnn32Wf3jH//Q+PHjNWLECNWrV0+pqamlxtSqVcv+7+rVqzu8ZrPZVFJSUuE6ytrO+badn58vd3d3bdiwQe7u7g79/hiIytqGMabC9QG4tAg3AFymZcuWWrFihdq1a6fs7GxVq1ZNwcHBTm/Pw8NDxcXFrivw/2vbtq2Ki4t14MABde3a1entlFVfixYtNH/+fBUUFNhnb9asWSM3NzeFhoZWqm4A5cOCYgAVdujQId122216++23tXnzZu3evVtLly7VlClT1KdPH0VGRqpz587q27evPvvsM2VmZurbb7/Vv/71L/3www/l3k9wcLC+//57ZWZmKjc316lZnbI0a9ZMDz74oAYPHqzly5dr9+7dWrdunRISEvTxxx9XqL7NmzcrIyNDubm5On36tB588EF5eXkpOjpaP/30k1atWqVRo0Zp0KBBXJICLhHCDYAK8/X1VXh4uF5++WV169ZNN910k5599lkNGzZMr776qmw2mz755BN169ZNMTExatasmQYOHKg9e/ZU6A/82LFj5e7urpYtW6pOnTrKyspy2THMmzdPgwcP1hNPPKHQ0FD17dtX69ev1/XXX1/ubQwbNkyhoaHq0KGD6tSpozVr1sjHx0crV67U4cOH1bFjR/Xv3189e/bUq6++6rLaAZyfzXABGQAAWAgzNwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFL+H5Cw198t5vSSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criar o DataFrame com a contagem de cada sentimento\n",
    "df = pesquisa['Sentimento'].value_counts(normalize=True).reset_index()\n",
    "df.columns = ['Sentimento', 'Porcentagem']\n",
    "\n",
    "# Criar o gráfico de barras\n",
    "ax = sns.barplot(x='Sentimento', y='Porcentagem', data=df)\n",
    "\n",
    "# Adicionar as porcentagens nas barras\n",
    "ax.bar_label(ax.containers[0], fmt='%.2f%%')\n",
    "\n",
    "# Mostrar o gráfico\n",
    "sns.boxplot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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
}