06. Statistical Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c91ffc19",
   "metadata": {},
   "source": [
    "## <font color=green> Exploração de Dados com análise estatística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b84bc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20360f93",
   "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>UF</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Anos de Estudo</th>\n",
       "      <th>Renda</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>800</td>\n",
       "      <td>1.603808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.739790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>880</td>\n",
       "      <td>1.760444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3500</td>\n",
       "      <td>1.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>150</td>\n",
       "      <td>1.690631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76835</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>812</td>\n",
       "      <td>1.687030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76836</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.792934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76837</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>1300</td>\n",
       "      <td>1.830587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76838</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.726344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76839</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>900</td>\n",
       "      <td>1.658305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76840 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       UF  Sexo  Idade  Cor  Anos de Estudo  Renda    Altura\n",
       "0      11     0     23    8              12    800  1.603808\n",
       "1      11     1     23    2              12   1150  1.739790\n",
       "2      11     1     35    8              15    880  1.760444\n",
       "3      11     0     46    2               6   3500  1.783158\n",
       "4      11     1     47    8               9    150  1.690631\n",
       "...    ..   ...    ...  ...             ...    ...       ...\n",
       "76835  53     1     46    2              11    812  1.687030\n",
       "76836  53     0     30    4               7   1500  1.792934\n",
       "76837  53     0     32    8              12   1300  1.830587\n",
       "76838  53     0     57    8               4   1500  1.726344\n",
       "76839  53     0     38    8               4    900  1.658305\n",
       "\n",
       "[76840 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = pd.read_csv(\"datasets/dados.csv\")\n",
    "dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eade9af",
   "metadata": {},
   "source": [
    "### Variáveis utilizadas\n",
    "\n",
    "> ### Renda\n",
    "> ***\n",
    "\n",
    "Rendimento mensal do trabalho principal para pessoas de 10 anos ou mais de idade.\n",
    "\n",
    "> ### Idade\n",
    "> ***\n",
    "\n",
    "Idade do morador na data de referência em anos.\n",
    "\n",
    "> ### Altura (elaboração própria)\n",
    "> ***\n",
    "\n",
    "Altura do morador em metros.\n",
    "\n",
    "> ### UF\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|11|Rondônia|\n",
    "|12|Acre|\n",
    "|13|Amazonas|\n",
    "|14|Roraima|\n",
    "|15|Pará|\n",
    "|16|Amapá|\n",
    "|17|Tocantins|\n",
    "|21|Maranhão|\n",
    "|22|Piauí|\n",
    "|23|Ceará|\n",
    "|24|Rio Grande do Norte|\n",
    "|25|Paraíba|\n",
    "|26|Pernambuco|\n",
    "|27|Alagoas|\n",
    "|28|Sergipe|\n",
    "|29|Bahia|\n",
    "|31|Minas Gerais|\n",
    "|32|Espírito Santo|\n",
    "|33|Rio de Janeiro|\n",
    "|35|São Paulo|\n",
    "|41|Paraná|\n",
    "|42|Santa Catarina|\n",
    "|43|Rio Grande do Sul|\n",
    "|50|Mato Grosso do Sul|\n",
    "|51|Mato Grosso|\n",
    "|52|Goiás|\n",
    "|53|Distrito Federal|\n",
    "\n",
    "> ### Sexo\t\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|0|Masculino|\n",
    "|1|Feminino|\n",
    "\n",
    "> ### Anos de Estudo\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|1|Sem instrução e menos de 1 ano|\n",
    "|2|1 ano|\n",
    "|3|2 anos|\n",
    "|4|3 anos|\n",
    "|5|4 anos|\n",
    "|6|5 anos|\n",
    "|7|6 anos|\n",
    "|8|7 anos|\n",
    "|9|8 anos|\n",
    "|10|9 anos|\n",
    "|11|10 anos|\n",
    "|12|11 anos|\n",
    "|13|12 anos|\n",
    "|14|13 anos|\n",
    "|15|14 anos|\n",
    "|16|15 anos ou mais|\n",
    "|17|Não determinados| \n",
    "||Não aplicável|\n",
    "\n",
    "> ### Cor\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|0|Indígena|\n",
    "|2|Branca|\n",
    "|4|Preta|\n",
    "|6|Amarela|\n",
    "|8|Parda|\n",
    "|9|Sem declaração|"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27d0666",
   "metadata": {},
   "source": [
    "- O primeiro passo em um trabalho de análise é o conhecimento do comportamento das variáveis envolvidas no estudo. Utilizando técnicas estatísticas como as análises das <b>DISTRIBUIÇÕES DE FREQUÊNCIAS</b> e <b>HISTOGRAMAS</b> podemos avaliar melhor a forma como os fenômenos em estudo se distribuem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b1f120",
   "metadata": {},
   "source": [
    "### Distribuição de frequências para variáveis qualitativas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe6a714",
   "metadata": {},
   "source": [
    "- O método value_counts devolve uma Series contendo valores únicos como seu índice e as frequências como seus valores; a ordem dos contadores é decrescente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30d99b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    53250\n",
       "1    23590\n",
       "Name: Sexo, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados['Sexo'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674e4cdf",
   "metadata": {},
   "source": [
    "- Atribuindo o parametro True para normalize e multiplicando por 100, você tem o percentual de frequencia para cada categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "624aa5ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    69.299844\n",
       "1    30.700156\n",
       "Name: Sexo, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados['Sexo'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01169439",
   "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>Sexo</th>\n",
       "      <th>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>53250</td>\n",
       "      <td>69.299844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>23590</td>\n",
       "      <td>30.700156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sexo       Frequência  Porcentagem (%)\n",
       "Masculino       53250        69.299844\n",
       "Feminino        23590        30.700156"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia = dados['Sexo'].value_counts()\n",
    "percentual = dados['Sexo'].value_counts(normalize = True) * 100\n",
    "dist_freq_qualitativas = pd.DataFrame({'Frequência': frequencia, 'Porcentagem (%)': percentual})\n",
    "dist_freq_qualitativas.rename(index = {0: 'Masculino', 1: 'Feminino'}, inplace = True)\n",
    "dist_freq_qualitativas.rename_axis('Sexo', axis = 'columns', inplace = True)\n",
    "dist_freq_qualitativas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0f85e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_freq_qualitativas['Frequência'].plot.barh(rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82b413de",
   "metadata": {},
   "outputs": [],
   "source": [
    "sexo = {0: 'Masculino',\n",
    "        1: 'Feminino'}\n",
    "\n",
    "cor = {0: 'Indígena',\n",
    "        2: 'Branca',\n",
    "        4: 'Preta',\n",
    "        6: 'Amarela',\n",
    "        8: 'Parda',\n",
    "        9: 'Sem declaração'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7a6c41",
   "metadata": {},
   "source": [
    "- A função crosstab permite cruzar os dados afim de calcular a frequência entre categorias . Para o nosso exemplo, ele permite contabilizar a quantidade de pessoas por genero de cada etnia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "854e6f9b",
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>256</td>\n",
       "      <td>22194</td>\n",
       "      <td>5502</td>\n",
       "      <td>235</td>\n",
       "      <td>25063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>101</td>\n",
       "      <td>9621</td>\n",
       "      <td>2889</td>\n",
       "      <td>117</td>\n",
       "      <td>10862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor        Indígena  Branca  Preta  Amarela  Parda\n",
       "Sexo                                              \n",
       "Masculino       256   22194   5502      235  25063\n",
       "Feminino        101    9621   2889      117  10862"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor)\n",
    "frequencia.rename(index = sexo, inplace = True)\n",
    "frequencia.rename(columns = cor, inplace = True)\n",
    "frequencia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2317d95",
   "metadata": {},
   "source": [
    "- Para incluir linha e coluna com subtotal, basta atribuir True ao parâmetro margins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35f22a0c",
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>256</td>\n",
       "      <td>22194</td>\n",
       "      <td>5502</td>\n",
       "      <td>235</td>\n",
       "      <td>25063</td>\n",
       "      <td>53250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>101</td>\n",
       "      <td>9621</td>\n",
       "      <td>2889</td>\n",
       "      <td>117</td>\n",
       "      <td>10862</td>\n",
       "      <td>23590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>357</td>\n",
       "      <td>31815</td>\n",
       "      <td>8391</td>\n",
       "      <td>352</td>\n",
       "      <td>35925</td>\n",
       "      <td>76840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor        Indígena  Branca  Preta  Amarela  Parda    All\n",
       "Sexo                                                     \n",
       "Masculino       256   22194   5502      235  25063  53250\n",
       "Feminino        101    9621   2889      117  10862  23590\n",
       "All             357   31815   8391      352  35925  76840"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia_sub = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor, margins=True)\n",
    "frequencia_sub.rename(index = sexo, inplace = True)\n",
    "frequencia_sub.rename(columns = cor, inplace = True)\n",
    "frequencia_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "666063ed",
   "metadata": {},
   "source": [
    "- Outra opção é utilizar o método pivot_table é parecida com a crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8aca1056",
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>256</td>\n",
       "      <td>22194</td>\n",
       "      <td>5502</td>\n",
       "      <td>235</td>\n",
       "      <td>25063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>101</td>\n",
       "      <td>9621</td>\n",
       "      <td>2889</td>\n",
       "      <td>117</td>\n",
       "      <td>10862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor        Indígena  Branca  Preta  Amarela  Parda\n",
       "Sexo                                              \n",
       "Masculino       256   22194   5502      235  25063\n",
       "Feminino        101    9621   2889      117  10862"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia_sub = pd.pivot_table(dados, \n",
    "                        index=[\"Sexo\"], \n",
    "                        columns=[\"Cor\"], \n",
    "                        aggfunc='size',\n",
    "                        fill_value=0)\n",
    "frequencia_sub.rename(index = sexo, inplace = True)\n",
    "frequencia_sub.rename(columns = cor, inplace = True)\n",
    "frequencia_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6de00cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cor'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequencia.T.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c7c6c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cor', ylabel='Sexo'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(frequencia, annot = True, fmt = 'g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e380a25f",
   "metadata": {},
   "source": [
    "- Calculando o percentual de pessoas por gênero de cada etnia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f32e4f5",
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>0.333160</td>\n",
       "      <td>28.883394</td>\n",
       "      <td>7.160333</td>\n",
       "      <td>0.305830</td>\n",
       "      <td>32.617126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>0.131442</td>\n",
       "      <td>12.520822</td>\n",
       "      <td>3.759761</td>\n",
       "      <td>0.152264</td>\n",
       "      <td>14.135867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor        Indígena     Branca     Preta   Amarela      Parda\n",
       "Sexo                                                         \n",
       "Masculino  0.333160  28.883394  7.160333  0.305830  32.617126\n",
       "Feminino   0.131442  12.520822  3.759761  0.152264  14.135867"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor,\n",
    "                         normalize = True) * 100\n",
    "percentual.rename(index = sexo, inplace = True)\n",
    "percentual.rename(columns = cor, inplace = True)\n",
    "percentual"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f0ad98",
   "metadata": {},
   "source": [
    "- Calculando a renda média do genero de cada etnia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "477d47b0",
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>1081.710938</td>\n",
       "      <td>2925.744435</td>\n",
       "      <td>1603.861687</td>\n",
       "      <td>4758.251064</td>\n",
       "      <td>1659.577425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>2464.386139</td>\n",
       "      <td>2109.866750</td>\n",
       "      <td>1134.596400</td>\n",
       "      <td>3027.341880</td>\n",
       "      <td>1176.758516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor           Indígena       Branca        Preta      Amarela        Parda\n",
       "Sexo                                                                      \n",
       "Masculino  1081.710938  2925.744435  1603.861687  4758.251064  1659.577425\n",
       "Feminino   2464.386139  2109.866750  1134.596400  3027.341880  1176.758516"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor,\n",
    "                         aggfunc = 'mean',\n",
    "                         values = dados.Renda)\n",
    "percentual.rename(index = sexo, inplace = True)\n",
    "percentual.rename(columns = cor, inplace = True)\n",
    "percentual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25af4698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cor'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentual.T.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85df305",
   "metadata": {},
   "source": [
    "### Distribuição de frequências para variáveis quantitativas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dee1095",
   "metadata": {},
   "source": [
    "Passo 1 - Especificar os limites de cada classe\n",
    "Utilizar a seguinte classificação:\n",
    "\n",
    "A ► Acima de 20 SM\n",
    "\n",
    "B ► De 10 a 20 SM\n",
    "\n",
    "C ► De 4 a 10 SM\n",
    "\n",
    "D ► De 2 a 4 SM\n",
    "\n",
    "E ► Até 2 SM\n",
    "\n",
    "onde SM é o valor do salário mínimo na época. Em nosso caso R$ 788,00 (2015):\n",
    "\n",
    "A ► Acima de 15.760\n",
    "\n",
    "B ► De 7.880 a 15.760\n",
    "\n",
    "C ► De 3.152 a 7.880\n",
    "\n",
    "D ► De 1.576 a 3.152\n",
    "\n",
    "E ► Até 1.576"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b057a2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(dados[\"Renda\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "053f446e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(dados[\"Renda\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f04972",
   "metadata": {},
   "source": [
    "Passo 2 - Criar a tabela de frequências"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c833d0e1",
   "metadata": {},
   "source": [
    "- Definindo classes de rendas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a91653f6",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qt5F5w2UjQLg"
   },
   "outputs": [],
   "source": [
    "classes = [0, 1576, 3152, 7880, 15760, 200000]\n",
    "labels = ['E', 'D', 'C', 'B', 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e2b059",
   "metadata": {},
   "source": [
    "- Contabilizando a quantidade de pessoas de cada classe de renda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dfb8804a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E    49755\n",
       "D    16700\n",
       "C     7599\n",
       "B     2178\n",
       "A      608\n",
       "Name: Renda, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia = pd.value_counts(\n",
    "    pd.cut(x = dados.Renda,\n",
    "               bins = classes,\n",
    "               labels = labels,\n",
    "               include_lowest = True)\n",
    ")\n",
    "frequencia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63220e88",
   "metadata": {},
   "source": [
    "- Contabilizando a porcentagem de pessoas de cada classe de renda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "994cbe41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E    64.751432\n",
       "D    21.733472\n",
       "C     9.889381\n",
       "B     2.834461\n",
       "A     0.791255\n",
       "Name: Renda, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual = pd.value_counts(\n",
    "    pd.cut(x = dados.Renda,\n",
    "               bins = classes,\n",
    "               labels = labels,\n",
    "               include_lowest = True),\n",
    "    normalize = True\n",
    ")*100\n",
    "percentual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80cf2052",
   "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>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>608</td>\n",
       "      <td>0.791255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2178</td>\n",
       "      <td>2.834461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7599</td>\n",
       "      <td>9.889381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>16700</td>\n",
       "      <td>21.733472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>49755</td>\n",
       "      <td>64.751432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequência  Porcentagem (%)\n",
       "A         608         0.791255\n",
       "B        2178         2.834461\n",
       "C        7599         9.889381\n",
       "D       16700        21.733472\n",
       "E       49755        64.751432"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas = pd.DataFrame(\n",
    "    {'Frequência': frequencia, 'Porcentagem (%)': percentual}\n",
    ")\n",
    "dist_freq_quantitativas_personalizadas.sort_index(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "accd06de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas['Frequência'].plot.barh(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7990e8cd",
   "metadata": {},
   "source": [
    "- O <b>HISTOGRAMA</b> é a representação gráfica de uma distribuição de frequências. É uma gráfico formado por um conjunto de retângulos colocados lado a lado, onde a área de cada retângulo é proporcional à frequência da classe que ele representa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09c79119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dados.Altura.hist(bins = 50, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "830eba24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas['Frequência'].plot.bar(width= 1, color= 'blue', alpha = 0.2, figsize= (12, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40161f27",
   "metadata": {},
   "source": [
    "***\n",
    "O box plot dá uma idéia da posição, dispersão, assimetria, caudas e dados discrepantes (outliers). A posição central é dada pela mediana e a dispersão por $IIQ$. As posições relativas de $Q1$, $Mediana$ e $Q3$ dão uma noção da simetria da distribuição. Os comprimentos das cauda são dados pelas linhas que vão do retângulo aos valores remotos e pelos valores atípicos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c945b8",
   "metadata": {},
   "source": [
    "<img src='figures/img_box.png' width='65%'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f7dcc78",
   "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>UF</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Anos de Estudo</th>\n",
       "      <th>Renda</th>\n",
       "      <th>Altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>800</td>\n",
       "      <td>1.603808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.739790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>880</td>\n",
       "      <td>1.760444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3500</td>\n",
       "      <td>1.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>150</td>\n",
       "      <td>1.690631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76835</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>812</td>\n",
       "      <td>1.687030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76836</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.792934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76837</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>1300</td>\n",
       "      <td>1.830587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76838</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.726344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76839</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>900</td>\n",
       "      <td>1.658305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76840 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       UF  Sexo  Idade  Cor  Anos de Estudo  Renda    Altura\n",
       "0      11     0     23    8              12    800  1.603808\n",
       "1      11     1     23    2              12   1150  1.739790\n",
       "2      11     1     35    8              15    880  1.760444\n",
       "3      11     0     46    2               6   3500  1.783158\n",
       "4      11     1     47    8               9    150  1.690631\n",
       "...    ..   ...    ...  ...             ...    ...       ...\n",
       "76835  53     1     46    2              11    812  1.687030\n",
       "76836  53     0     30    4               7   1500  1.792934\n",
       "76837  53     0     32    8              12   1300  1.830587\n",
       "76838  53     0     57    8               4   1500  1.726344\n",
       "76839  53     0     38    8               4    900  1.658305\n",
       "\n",
       "[76840 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dados.copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6481b33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sexo = {0: 'Masculino',\n",
    "        1: 'Feminino'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "60136b69",
   "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>UF</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Anos de Estudo</th>\n",
       "      <th>Renda</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Genero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>800</td>\n",
       "      <td>1.603808</td>\n",
       "      <td>Masculino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.739790</td>\n",
       "      <td>Feminino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>880</td>\n",
       "      <td>1.760444</td>\n",
       "      <td>Feminino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3500</td>\n",
       "      <td>1.783158</td>\n",
       "      <td>Masculino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>150</td>\n",
       "      <td>1.690631</td>\n",
       "      <td>Feminino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76835</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>812</td>\n",
       "      <td>1.687030</td>\n",
       "      <td>Feminino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76836</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.792934</td>\n",
       "      <td>Masculino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76837</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>1300</td>\n",
       "      <td>1.830587</td>\n",
       "      <td>Masculino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76838</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.726344</td>\n",
       "      <td>Masculino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76839</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>900</td>\n",
       "      <td>1.658305</td>\n",
       "      <td>Masculino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76840 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       UF  Sexo  Idade  Cor  Anos de Estudo  Renda    Altura     Genero\n",
       "0      11     0     23    8              12    800  1.603808  Masculino\n",
       "1      11     1     23    2              12   1150  1.739790   Feminino\n",
       "2      11     1     35    8              15    880  1.760444   Feminino\n",
       "3      11     0     46    2               6   3500  1.783158  Masculino\n",
       "4      11     1     47    8               9    150  1.690631   Feminino\n",
       "...    ..   ...    ...  ...             ...    ...       ...        ...\n",
       "76835  53     1     46    2              11    812  1.687030   Feminino\n",
       "76836  53     0     30    4               7   1500  1.792934  Masculino\n",
       "76837  53     0     32    8              12   1300  1.830587  Masculino\n",
       "76838  53     0     57    8               4   1500  1.726344  Masculino\n",
       "76839  53     0     38    8               4    900  1.658305  Masculino\n",
       "\n",
       "[76840 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Genero'] = df['Sexo'].apply(lambda x: sexo[x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcdd98f3",
   "metadata": {},
   "source": [
    "- Analisando medidas de dispersão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e9c789b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valor Minimo: 1.339244614\n",
      "Valor Máximo: 2.028496765\n",
      "Mediana: 1.6993247325\n",
      "Primeiro Quartil (Q1):  1.645785297\n",
      "Segundo Quartil (Q2):  1.6993247325\n",
      "Terceiro Quartil (Q3):  1.75306238275\n",
      "Intervalo Interquartil (IIQ):  0.10727708575000006\n"
     ]
    }
   ],
   "source": [
    "print(\"Valor Minimo:\", df['Altura'].min())\n",
    "print(\"Valor Máximo:\", df['Altura'].max())\n",
    "print(\"Mediana:\", df['Altura'].median())\n",
    "print(\"Primeiro Quartil (Q1): \", df['Altura'].quantile(0.25))\n",
    "print(\"Segundo Quartil (Q2): \", df['Altura'].quantile(0.5))\n",
    "print(\"Terceiro Quartil (Q3): \", df['Altura'].quantile(0.75))\n",
    "print(\"Intervalo Interquartil (IIQ): \", df['Altura'].quantile(0.75) - df['Altura'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2e13b631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores acima de 1.9139780113750002 são outliers\n",
      "Valores abaixo de 1.484869668375 são outliers\n"
     ]
    }
   ],
   "source": [
    "IIQ = df['Altura'].quantile(0.75) - df['Altura'].quantile(0.25)\n",
    "Q1 = df['Altura'].quantile(0.25)\n",
    "Q3 = df['Altura'].quantile(0.75)\n",
    "\n",
    "Valor_Maximo = Q3 + 1.5 * IIQ\n",
    "Valor_Minimo = Q1 - 1.5 * IIQ\n",
    "\n",
    "print(f\"Valores acima de {Valor_Maximo} são outliers\")\n",
    "print(f\"Valores abaixo de {Valor_Minimo} são outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e5fe060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Altura'].plot.box(vert=False, figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e74e4530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot = df.boxplot(vert=False, column='Altura', by='Genero', figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "73f9dd3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query('Renda < 10000').boxplot(vert=False, column='Renda', figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "324fc3ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Renda'}, xlabel='Genero'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query('Renda < 10000').boxplot(vert=False, column='Renda', by='Genero', figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1fcb6ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(vert=False, column='Anos de Estudo', figsize=(15,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8b9c25e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Anos de Estudo'}, xlabel='Genero'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(vert=False, column='Anos de Estudo', by='Genero', figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b473aa0f",
   "metadata": {},
   "source": [
    "### Medidas de dispersão\n",
    "- Embora as medidas de posição forneçam uma sumarização bastante importante dos dados, elas podem não ser suficientes para caracterizar conjuntos distintos, especialmente quando as observações de determinada distribuição apresentarem dados muito dispersos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5ea70c35",
   "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>Alunos</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matérias</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Alunos      Fulano  Beltrano  Sicrano\n",
       "Matérias                             \n",
       "Matemática     8.0      10.0      7.5\n",
       "Português     10.0       2.0      8.0\n",
       "Inglês         4.0       0.5      7.0\n",
       "Geografia      8.0       1.0      8.0\n",
       "História       6.0       3.0      8.0\n",
       "Física        10.0       9.5      8.5\n",
       "Química        8.0      10.0      7.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = {'Fulano': [8.0, 10, 4, 8, 6, 10, 8],\n",
    "                          'Beltrano': [10, 2, 0.5, 1, 3, 9.5, 10],\n",
    "                          'Sicrano': [7.5, 8, 7, 8, 8, 8.5, 7]},\n",
    "                 index = ['Matemática',\n",
    "                          'Português',\n",
    "                          'Inglês',\n",
    "                          'Geografia',\n",
    "                          'História',\n",
    "                          'Física',\n",
    "                          'Química'])\n",
    "\n",
    "df.index.name = 'Matérias'\n",
    "df.columns.name = 'Alunos'\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8ce6319e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: xlabel='Matérias'>, <Axes: xlabel='Matérias'>,\n",
       "        <Axes: xlabel='Matérias'>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAEtCAYAAACbNYiPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAAsTAAALEwEAmpwYAABHSklEQVR4nO3deZwU1bn/8c/DMDAIiAo4UYjMGJVEBYFB40rAfYm74m5IVJK4ZtF7o7lGTW6ixtyEm+u9GhMNRo2IRokxBkwQRGPUyCLiQjQy/mSSKKIgo6CAz++PUz30DFtPd3X36Znv+/XqF13V3VXfqe5+mlN16pS5OyIiIiIiIhKnLuUOICIiIiIiIhunRpuIiIiIiEjE1GgTERERERGJmBptIiIiIiIiEVOjTUREREREJGJqtImIiIiIiESsaylX1q9fP6+rq8vpue+//z49e/YsbqB2ijETxJlLmXIXY672ZJo9e/bb7t6/yJGKqtJrE8SZS5lyF2OuSs/UEWoTVH59UqbcxZhLmXJX9Prk7iW7NTQ0eK5mzJiR83NLJcZM7nHmUqbcxZirPZmAZ72EdaQYt0qvTe5x5lKm3MWYq9IzdYTa5B2gPilT7mLMpUy5K3Z9UvdIERERERGRiKnRJiIiIiIiEjE12kRERERERCJW0oFIRERERESkY1i9ejWLFy9m1apVJVtnnz59eOmll0q2vlxtKFdNTQ0DBw6kurq64OVvttFmZrcBnwfecvfdk3nbAPcAdUAjMNbd3y04jYiIiIiIVITFixfTu3dv6urqMLOSrHPFihX07t27JOtqj7a53J2lS5eyePFi6uvrC15+Lt0jJwKHt5n3LWC6u+8MTE+mO6wpc5vY77pHGTf1ffa77lGmzG0qdyTpAPS5qiBPTIBFs1rPWzQrzBeR4tL3TyRaq1atom/fviVrsFUSM6Nv376pHYXcbKPN3WcB77SZfSxwe3L/duC4VNJEaMrcJi6//3malq0EoGnZSi6//3n9B1sKos9VhRkwAu4dt+4/jotmhekBI8qZSqRz0PdPJGpqsG1cmtsm34FIat39n8n9fwG1KeWJzg3TFrJy9dpW81auXssN0xaWKZF0BPpcVZj6UXDyRLh3HHWL7gr/YTx5YpgvIsWl75+IbML3v/99dtttN4YOHcqwYcN4+umnOffcc3nxxRfLHS1VFq7vtpknmdUBD2Wd07bM3bfKevxdd996I68dD4wHqK2tbZg0aVJOwZqbm+nVq1dOzy2mcVPf3+hjEw+P42rssWyrbMq0abF/rtqzrcaMGTPb3UcWOVLq8qlNdYvuou71yTQOGktj/RnFjtguMX2+M5QpdzHmii1Te79/lVqboPL/75RNmXIXY67NZerTpw877bRTu5b59sq3+c4z3+F7n/0efWv6tjvT2rVrqaqqAuDpp5/miiuu4OGHH6Z79+4sXbqUjz76iO222y7n5a1Zs4auXQsfmzE7V7ZXX32V5cuXt5qXT33KN+GbZradu//TzLYD3trYE939FuAWgJEjR/ro0aNzWsHMmTPJ9bnFNOCpR1u6sLWav1WPKPJBPNsqmzJtWuyfq5i2VbG0uzYtmgXPTKdx0FjqlkynbvRZUe3pj/E9U6bcxZgrqkyRf//SVun/d8qmTLmLMdfmMr300kvtHhRkwgsTeG7pc9zx9zu4cu8r250pe8CP9957j9raWvr16wfQMn/06NH86Ec/YuTIkUydOpUrrriCtWvX0q9fP6ZPn87VV1/N3//+d1577TV22GEHrr32Ws466yzefz/sVL/xxhvZd999mTlzJldffTX9+vVjwYIFNDQ0cOedd2JmTJ8+nUsvvZQ1a9aw55578sMf/pCtttpqvbw1NTUMHz683X9nW/l2j3wQ+EJy/wvAbwtOEqnLDhtMj+rWreYe1VVcdtjgMiWSjkCfqwqTOYfm5IlhD3/SVWu9wRFEJH36/ol0CA13NjDk9iFMXjgZx5m8cDJDbh9Cw50NeS/z0EMP5Y033mCXXXbh/PPP57HHHmv1+JIlSzjvvPP4zW9+w3PPPce9997b8tiLL77In/70J+6++2623XZb/vjHPzJnzhzuueceLr744pbnzZ07lwkTJvDiiy/y2muv8ec//5lVq1Yxbtw47rnnHp5//nnWrFnDL37xi7z/jlxsttFmZncDfwEGm9liMzsHuA44xMxeAQ5Opjuk44YP4NoThjBgqx5AOBJy7QlDOG74gDInk0qmz1WFaZrT+hyazDk2TXPKmUqkc9D3T6RDmHrCVI6sP5KaqhoAaqpqOKr+KKadOC3vZfbq1YvZs2dzyy230L9/f0455RQmTpzY8vhTTz3FqFGjWobc32abbVoeO+aYY+jRI/w/bPXq1Zx33nkMGTKEk08+udX5cHvttRcDBw6kS5cuDBs2jMbGRhYuXEh9fT277LILAF/4whd48skn8/47crHZ7pHuftpGHjoo5SzROm74AI4bPiDKw9ZSufS5qiD7f239efWjOnT3LJFo6Psn0iH036I/Pat78uHaD+lW1Y0P135Iz2496dejX0HLraqqYvTo0YwePZohQ4Zw++23b/5FQM+e68YQ+MlPfkJtbS3PPfccH3/8MTU1NS2Pde/evdW61qxZU1DefOXbPVJERERERCRn76x6h7GDx/LrI3/N2MFjWbpyaUHLW7hwIa+88krL9Lx58xg0aFDL9N57782sWbNYtGhRWP87ba9iFixfvpztttuOLl26cMcdd7B27doNPi9j8ODBNDY28uqrrwJwxx13sN9++xX0t2xO4UOliIiIiIiIbMaEMRNa7v/H3v9R8PKam5u56KKLWLZsGV27dmWnnXbilltu4aSTTgKgf//+3HLLLZxwwgl8/PHHLeeutXX++edz4okn8qtf/YrDDz+81VG4DampqeGXv/wlJ598cstAJOecc07Bf8+mqNEmIiIiwRMTwkWrs7seLpoVzh/bUDdFEZEyamho2OC5ZDNnzmy5f8QRR3DEEUe0evzqq69uNb3zzjszf/78lunrr78eoKXbZcaNN97Ycv+ggw5i7ty5LdMrVqzI50/ImbpHioiISDBgROuRGTMjNw4YUc5UIiKdno60iYiISJAZmfHecdT1Pwiemd565EYRESkLHWkTERGRdepHwchzqHt9Mow8Rw02EZEIqNEmIiIi6yyaBc/eSuOgsfDsrbqItYhskruXO0K00tw2arSJiIhIkDmH7eSJNNaf0dJVUg03EdmQmpoali5dqobbBrg7S5cubXXNt0LonDYREREJmuasO4ft9ZnrznFrmqNukiKynoEDB7J48WKWLFlSsnWuWrUqtYZQmjaUq6amhoEDB6ayfDXaREREJNjQsP71o9RgE5ENqq6upr6+vqTrnDlzJsOHDy/pOnNR7FzqHikiIiIiIhIxNdpEREREREQiVlCjzcwuMbMFZvaCmX0tpUwiIiIiIiKSyLvRZma7A+cBewF7AJ83s53SCiYiIiIiIiKFHWn7DPC0u3/g7muAx4AT0oklIiIiIiIiAJbvdRXM7DPAb4F9gJXAdOBZd7+ozfPGA+MBamtrGyZNmpTT8pubm+nVq1de2YolxkwQZy5lyl2MudqTacyYMbPdfWSRI6WuI9UmiDOXMuUuxlyVnqlSaxN0rPqkTLmLMZcy5a7o9cnd874B5wCzgVnATcCETT2/oaHBczVjxoycn1sqMWZyjzOXMuUuxlztyUTYWVNQLSn3rdJrk3ucuZQpdzHmqvRMHaE2eQeoT8qUuxhzKVPuil2fChqIxN1vdfcGdx8FvAv8rZDliYiIiIiISGsFXVzbzLZ197fMbAfC+Wx7pxNLREREREREoMBGG/AbM+sLrAYucPdlhUcSERERERGRjIIabe5+QFpBREREREREZH0FndMmIiIiIiIixaVGm4iIiIiISMTUaBMRERERkegt+WAJE/41gbdXvl3uKCWnRpuIiIiIiETv5vk389qHr3HTczeVO0rJFTp6pIiIiIiISNE03NnAR2s/apmevHAykxdOpltVN2afObuMyUpHR9pERERERCRaU0+YypH1R1JTVQNATVUNR9UfxbQTp5U5Wemo0SYiIiIiItHqv0V/elb35MO1H9KVrny49kN6dutJvx79yh2tZNQ9UkREREREovbOqncYO3gsde/V0bhlY6cbjESNNhERERERidqEMRMAmDlzJmfufWZ5w5SBukeKiIiIiIhETI02ERERERGRiKnRJiIiIiIiErGCGm1m9nUze8HMFpjZ3WZWU2igKXOb2O+6Rxk39X32u+5RpsxtKnSRUkJ6/yqb3j8REUnLkg+WMOFfEzrdgBH5iHFbxZipM8u70WZmA4CLgZHuvjtQBZxaSJgpc5u4/P7naVq2EoCmZSu5/P7n9R/HCqH3r7Lp/RMRkTTdPP9mXvvwNW567qZyR4lejNsqxkydWaGjR3YFepjZamAL4B+FLOyGaQtZuXptq3krV6/lhmkLOW74gEIWLSWg96+y6f0TEZE0NNzZwEdrP2qZnrxwMpMXTqZbVTdmnzm7jMniE+O2ijGTgLl7/i82uwT4PrASeMTdz9jAc8YD4wFqa2sbJk2atNHljZv6/kYfm3h4z7xzpqW5uZlevXqVO8Z6Ysml9y8/seTK9/0bM2bMbHcfWYxMxdSe2pQtlverrRhzKVPuYsxV6ZkqtTZB5den5WuW88C7DzB/5XxW+2qqrZo9ttiD47c+ni2rtix3vGi2E8S5rWLMlC2m9y9bsetT3kfazGxr4FigHlgG3GtmZ7r7ndnPc/dbgFsARo4c6aNHj97oMgc89WhL16xW87fqwaZeVyozZ86MIkdbseTS+5efWHLF/v6lrT21KVss71dbMeZSptzFmEuZyqcj1Kfn/vIcc/42h650ZY2vYceBO3LM3seUOxYQ13aCOLdVjJkyYnv/Moqdq5DukQcDi9x9CYCZ3Q/sC9y5yVdtwmWHDeby+59v1UWrR3UVlx02uICYUip6/yqb3j8REUnLO6veYezgsdS9V0fjlo0azGITYtxWMWbq7ApptP0/YG8z24LQPfIg4NlCwmTOm7lh2kKalq1kwFY9uOywwTqfpkLo/atsev9ERCQtE8ZMAMLRhzP3PrO8YSIX47aKMVNnl3ejzd2fNrP7gDnAGmAuyaH8Qhw3fADHDR8Q7aFP2TS9f5VN75+IiIhIfAoaPdLdrwKuSimLiIiIiIiItFHQxbVFRERERESkuNRoExEREalwSz5YwoR/TdCAESIdlBptIiIiIhXu5vk389qHr3HTczeVO4qIFEFB57SJiIiISPk03NnAR2s/apmevHAykxdOpltVN2afObuMyUQkTTrSJiIiIlKhpp4wlSPrj6SmqgaAmqoajqo/imknTitzMhFJkxptIiIiIhWq/xb96Vndkw/XfkhXuvLh2g/p2a0n/Xr0K3c0EUmRukeKiIiIVLB3Vr3D2MFjqXuvjsYtGzUYiUgHpEabiIiISAWbMGYCADNnzuTMvc8sbxgRKQp1jxQREREREYmYGm0iIiIiIiIRU6NNREREREQkYnk32sxssJnNy7q9Z2ZfSzGbiIiIiIhIp5d3o83dF7r7MHcfBjQAHwAPpBVMRERERCRNSz5YwoR/TdAIm5KqUnyu0uoeeRDwd3d/PaXliYiIiIik6ub5N/Pah69x03M3lTuKdCCl+FylNeT/qcDdKS1LRERERCQ1DXc28NHaj1qmJy+czOSFk+lW1Y3ZZ84uYzKpZKX8XJm7F7YAs27AP4Dd3P3NDTw+HhgPUFtb2zBp0qScltvc3EyvXr0Kypa2GDNBnLmUKXcx5mpPpjFjxsx295FFjpS6jlSbIM5cypS7GHNVeqZKrU3QsepTLJmWr1nOA+8+wPyV81ntq6m2avbYYg+O3/p4tqzastzxgHi2VTZl2rR8P1d51Sd3L+gGHAs8kstzGxoaPFczZszI+bmlEmMm9zhzKVPuYszVnkzAs15gHSn3rdJrk3ucuZQpdzHmqvRMHaE2eQeoTzFluubJa3zIxCE+bOIwHzJxiH/3L98td6RWYtpWGcq0efl8rvKpT2l0jzwNdY0UERERkYi9s+odxg4eS917dTRu2ajBSCQVpfpcFdRoM7OewCHAl9OJIyIiIiKSvgljJgAwc+ZMztz7zPKGkQ6jVJ+rghpt7v4+0DelLCIiIiIiItJGWkP+i4iIiIiISBGo0SYiIiIiIhIxNdpEREREREQipkabiIiIiIhIxNRoExERERERiZgabSIiIiIiIhFTo01ERERERCRiarSJiIiIiIhETI02ERERERGRiKnRJiIiIiIiEjE12kRERERERCKmRpuIiIiIiEjECmq0mdlWZnafmb1sZi+Z2T5pBRMRERERERHoWuDr/xuY6u4nmVk3YIsUMomkasrcJm6YtpCmZSsZ8NSjXHbYYI4bPqDcsUREREREcpJ3o83M+gCjgHEA7v4R8FE6sUTSMWVuE5ff/zwrV68FoGnZSi6//3kANdxEREREpCIU0j2yHlgC/NLM5prZL8ysZ0q5RFJxw7SFLQ22jJWr13LDtIVlSiQiIiIi0j7m7vm90Gwk8BSwn7s/bWb/Dbzn7le2ed54YDxAbW1tw6RJk3JafnNzM7169corW7HEmAnizBVLpnFT39/oYxMPj2MfQyzbKlt7Mo0ZM2a2u48scqTUdaTaBHHmUqbcxZir0jNVam2CjlWflCl3MeZSptwVvT65e1434BNAY9b0AcDvN/WahoYGz9WMGTNyfm6pxJjJPc5csWTa99rpPujfH1rvtu+108sdrUUs2ypbezIBz3qedSSWW6XXJvc4cylT7mLMVemZOkJt8g5Qn5QpdzHmUqbcFbs+5d090t3/BbxhZoOTWQcBL+a7PJFiuOywwfSormo1r0d1FZcdNngjrxARERERiUuho0deBNyVjBz5GvDFwiOJpCcz2EjL6JFb9dDokSIiIiJSUQpqtLn7PKAi+4tL53Hc8AEcN3wAM2fOZPTo0eWOIyIiIiLSLgVdXFtERERERESKS402ERERERGRiKnRJiIiIiIiEjE12kRERERERCKmRpuIiIiIiEjE1GgTERERERGJmBptIiIiIiIiEVOjTUREREREJGJqtImIiIiIiERMjTYREREREZGIqdEmIiIiIiISMTXaREREREREIta1kBebWSOwAlgLrHH3kWmEEhERERERkaCgRltijLu/ncJyREREREREpA11jxQREREREYmYuXv+LzZbBLwLOPAzd79lA88ZD4wHqK2tbZg0aVJOy25ubqZXr155ZyuGGDNBnLmUKXcx5mpPpjFjxsyuxK7RHak2QZy5lCl3Meaq9EyVWpugY9UnZcpdjLmUKXdFr0/unvcNGJD8uy3wHDBqU89vaGjwXM2YMSPn55ZKjJnc48ylTLmLMVd7MgHPegF1JIZbpdcm9zhzKVPuYsxV6Zk6Qm3yDlCflCl3MeZSptwVuz4V1D3S3ZuSf98CHgD2KmR5IiIiIiIi0lrejTYz62lmvTP3gUOBBWkFExERERERkcJGj6wFHjCzzHJ+7e5TU0klIiIiIiIiQAGNNnd/DdgjxSwiIiIiIiLShob8FxERERERiZgabSIiIiIiIhFTo01ERERERCRiarSJiIiIiIhETI02ERERERGRiKnRJiIiIiIiEjE12kRERERERCKmRpuIiIiIiEjE1GgTERERERGJmBptIiIiIiIiEVOjTUREREREJGJqtImIiIiIiESs4EabmVWZ2VwzeyiNQJK7KXOb2O+6Rxk39X32u+5RpsxtKnckERERERFJWdcUlnEJ8BKwZQrLkhxNmdvE5fc/z8rVawFoWraSy+9/HoDjhg8oZzQREREREUlRQUfazGwgcBTwi3TiSK5umLawpcGWsXL1Wm6YtrBMiUREREREpBjM3fN/sdl9wLVAb+BSd//8Bp4zHhgPUFtb2zBp0qSclt3c3EyvXr3yzlYMMWUaN/X9jT428fCeJUyyYTFtq4wYM0GcudqTacyYMbPdfWSRI6WuI9UmiDOXMuUuxlyVnqlSaxN0rPqkTLmLMZcy5a7o9cnd87oBnwf+L7k/Gnhoc69paGjwXM2YMSPn55ZKTJn2vXa6D/r3h9a77Xvt9HJHc/e4tlVGjJnc48zVnkzAs55nHYnlVum1yT3OXMqUuxhzVXqmjlCbvAPUJ2XKXYy5lCl3xa5PhXSP3A84xswagUnAgWZ2ZwHLk3a47LDB9KiuajWvR3UVlx02uEyJRERERESkGPJutLn75e4+0N3rgFOBR939zNSSySYdN3wA154whAFb9QBgwFY9uPaEIRqERERERESkg0lj9Egpk+OGD+C44QOYOXMmo0ePLnccEREREREpglQabe4+E5iZxrJERERERERknYIvri0iIiIiIiLFo0abiIiIiIhIxNRoExERERERiZgabSIiIiIiIhFTo01ERERERCRiarSJiIiIiIhETI02ERERERGRiKnRJiIiIiIiEjE12kRERERERCKmRpuIiIiIiEjE1GgTERERERGJWN6NNjOrMbNnzOw5M3vBzK5JM5iIiIiIiIgUdqTtQ+BAd98DGAYcbmZ7p5JKRESko3tiAiya1XreollhvoiISJa8G20eNCeT1cnNU0klIiLS0Q0YAfeOW9dwWzQrTA8YUc5UIiISoa6FvNjMqoDZwE7A/7r706mkEhER6ejqR8HJE+HecdT1PwiemR6m60eVO5mIiETG3As/OGZmWwEPABe5+4I2j40HxgPU1tY2TJo0KadlNjc306tXr4KzpSnGTBBnLmXKXYy52pNpzJgxs919ZJEjpa4j1SaIM5cy5aZu0V3UvT6ZxkFjaaw/o9xxWsS4rTpDbYKOVZ+UKXcx5lKm3BW9Prl7KjfgO8Clm3pOQ0OD52rGjBk5P7dUYszkHmcuZcpdjLnakwl41lOqI+W6VXptco8zlzLl4LXH3K+v90W3net+fX2YjkR028o7X23yDlCflCl3MeZSptwVuz4VMnpk/+QIG2bWAzgEeDnf5YmIiHQqmXPYTp4YjrAlXSXXG5xEREQ6vUJGj9wOmGFm84G/An9094fSiSUiItLBNc1pfQ5b5hy3pjnlTCUiIhHKeyASd58PDE8xi4iISOex/9fWn1c/SgORiIjIego50iYiIiIiIiJFpkabiIiIiIhIxNRoExERERERiZgabSIiIiIiIhFTo01ERERERCRiarSJiIiIiIhETI02ERERERGRiKnRJiIiIiIiEjE12kRERERERCKmRpuIiIiIiEjE1GgTERERERGJmBptIiIiIiIiEcu70WZmnzSzGWb2opm9YGaXpBlMRERERERECjvStgb4prvvCuwNXGBmu6YTS0RENuuJCbBoVut5i2aF+SIiItJh5N1oc/d/uvuc5P4K4CVgQFrBRERkMwaMgHvHrWu4LZoVpgeMKGcqERERSVnXNBZiZnXAcODpNJYnIiI5qB8FJ0+Ee8dR1/8geGZ6mK4fVe5kIiIikiJz98IWYNYLeAz4vrvfv4HHxwPjAWpraxsmTZqU03Kbm5vp1atXQdnSFmMmiDOXMuUuxlztyTRmzJjZ7j6yyJFS15FqU92iu6h7fTKNg8bSWH9GueO0iHFbxZgJ4sxV6ZkqtTZBx6pPypS7GHMpU+6KXp/cPe8bUA1MA76Ry/MbGho8VzNmzMj5uaUSYyb3OHMpU+5izNWeTMCzXkAdieFW0bXptcfcr6/3Rbed6359fZiORHTbyuPM5B5nrkrP1BFqk1d6fXJlao8YcylT7opdnwoZPdKAW4GX3P3H+S5HRETylDmH7eSJ4Qhb0lVyvcFJREREpKIVMnrkfsBZwIFmNi+5HZlSLhER2ZymOa3PYcuc49Y0p5ypREREJGV5D0Ti7k8AlmIWERFpj/2/tv68+lEaiERERKSDKeRIm4iIiIiIiBSZGm0iIiIiIiIRU6NNREREREQkYmq0iYiIiIiIREyNNhERERERkYip0SYiIiIiIhIxNdpEREREREQipkabiIiIiIhIxNRoExERERERiZgabSIiIiIiIhFTo01ERERERCRiarSJiIiIiIhErKBGm5ndZmZvmdmCtAKJiIik6okJsGhW63mLZoX5IiIiFaBrga+fCNwI/CrfBaxevZrFixezatWqVvP79OnDSy+9VFi6lJUiU01NDQMHDqS6urqo6xER6TQGjIB7x8HJE8P0olmtpyOysd/EcqiU32H9bopIZ1BQo83dZ5lZXSHLWLx4Mb1796aurg4za5m/YsUKevfuXciiU1fsTO7O0qVLWbx4MfX19UVbj4hIp1I/KjTQ7h1HXf+D4JnpYbp+VLmTrWdjv4nlUAm/w/rdFJHOwty9sAWERttD7r77Rh4fD4wHqK2tbZg0aVKrx/v06cOnPvWp9X6c1q5dS1VVVUHZ0laKTO7O3//+d5YvX57za5qbm+nVq1cRU7WfMuUuxlztyTRmzJjZ7j6yyJFSt7natDExvl8QZ67YMtUtuou61yfTOGgsjfVnlDtOK5lttbHfxHKolN/hjf1uVmptgo5Vn5QpdzHmUqbcFf3/Tu5e0A2oAxbk8tyGhgZv68UXX1xvnrv7e++9t8H55VSqTBvbJhszY8aM4gQpgDLlLsZc7ckEPOsF1pFy3zZUm9LYNqUUY66oMr32mPv19b7otnPdr68P0xHJbKv21v9iqqTf4Q1tt45Qm7wD1Cdlyl2MuZQpd8X+v5NGjxQRkY4t6xy2xvozWrpKrjc4iYiISKTUaAOqqqoYNmxYy62xsXGjz504cSIXXnhh6cKJiEhhmua0Poctc45b05xypopW5jdx3333ZcSIETz55JObfU2mS1BjYyO//vWvix1RRKTTKWggEjO7GxgN9DOzxcBV7n5rGsE2ZsrcJm6YtpB/LFvJ9lv14LLDBnPc8AEFLbNHjx7MmzcvnYAiIhKX/b+2/rz6UVEORNIuT0wII2Nm/x2LZoXG6Ib+5hxlfhNXrFjBk08+yeWXX85jjz2W02szjbbTTz99vcfWrFlD166FDlotItI5FXSkzd1Pc/ft3L3a3QeWosF2+f3P07RsJQ40LVvJ5fc/z5S5Tamvq66ujrfffhuAZ599ltGjR6/3nN/97nd89rOfZfjw4Rx88MG8+eabAFx99dV86UtfYvTo0ey444789Kc/bXnNj3/8Y3bffXd23313JkyYkHpuERHpJDKXMsh088x0Ax0wIrVVvPfee2y99dYt0zfccAN77rknQ4cO5aqrrlrv+d/61rd4/PHHGTZsGD/5yU+YOHEixxxzDAceeCAHHXQQzc3NHHTQQYwYMYIhQ4bw29/+FgiNvc985jOcd9557Lbbbhx66KGsXLkSgHnz5rH33nszdOhQTj/9dN59993U/j4RkUpRUd0jb5i2kJWr17aat3L1Wm6YtrCg5a5cubKla+Txxx+f8+v2339/nnrqKebOncupp57KD3/4w5bHXn75ZaZNm8YzzzzDNddcw+rVq5k9eza//OUvefrpp3nqqaf4+c9/zty5cwvKLiIinVTWpQx49Pvrrj1X4BHEzG9iQ0MD5557LldeeSUAjzzyCK+88grPPPMM8+bNY/bs2cya1fq8wOuuu44DDjiAefPm8fWvfx2AOXPmcN999/HYY49RU1PDAw88wJw5c5gxYwbf/OY3M4Oa8corr3DBBRfwwgsvsNVWW/Gb3/wGgLPPPpvrr7+e+fPns+uuu3LNNdcU9PeJiFSiiuqn8I9lK9s1P1f5do9cvHgxp5xyCv/85z/56KOPWl0j5qijjqJ79+50796dbbfdljfffJMnnniC448/np49ewJwwgkn8PjjjzN8+PCC8ouISCdVPwpGngOzfgij/i2VLp/Z3SMXLFjA2WefzYIFC3jkkUd45JFHWn6zmpubeeWVVxg1atPrPOSQQ9hmm22AMGL1FVdcwaxZs+jSpQtNTU0tvVTq6+sZNmwYAA0NDTQ2NrJ8+XKWLVvG5z73OQBOP/10vvjFLxb8N4qIVJqKOtK2/VY92jW/EF27duXjjz8GYNWqVRt8zkUXXcSFF17I888/z89+9rNWz+vevXvL/aqqKtasWZN6RhGR6DwxYf1RGRfNCvMlfYtmwbO3hgbbs7emPiLmPvvsw9tvv82SJUtwdy6//HLmzZvHvHnzePXVVznnnHM2u4zMjkqAu+66iyVLljB79mzmzZtHbW1ty2+nfjdFRDauohptlx02mB7VrS+q2aO6issOG5z6uurq6pg9ezZASxeNtpYvX86AAWEQlNtvv32zyzzggAOYMmUKH3zwAe+//z4PPPAABxxwQHqhRUTKrQTnWUki61IGHPjtolzK4OWXX2bt2rX07duXww47jNtuu43m5mYAmpqaeOutt1o9v3fv3qxYsWKjy1u+fDnbbrst1dXVzJgxg9dff32T6+/Tpw9bb701jz/+OACTJk1qOeomItKZVFT3yMwokWmPHrkhV111Feeccw5XXnnlBgchgTDgyMknn8zWW2/NgQceyKJFiza5zBEjRjBu3Dj22msvAM4991x1jRSRjiXrPKu6/gfBM9NTOc9KNmBTlzIoYHtnzmn7+OOPMTNuv/12qqqqOPTQQ3nppZfYZ599gDDM/5133sm2227b8tqhQ4dSVVXFHnvswbhx41oNYgJwxhlncPTRRzNkyBBGjhzJpz/96c3muf322/nKV77CBx98wA477MAdd9yR998mIlKpKqrRBqHhlnYjLbPXMNsBBxzA3/72t1bzVqxYwbhx4xg3bhwAxx57LMcee+x6r7366qtbTS9YsKDl/je+8Q2+8Y1vFB5aRCRWyXlWdSmeZyUbUKRLGaxdGwb8WrFiBb1792712CWXXMIll1yy3msyv6PV1dU8+uijrR7L/GYC9OvXj7/85S8bXG/2b+Wll17acn/YsGE89dRTG80kItIZVFT3SBERqQDJeVaNg8YW5TwrERGRzkaNNhERSU/WeVaN9WcU5TwrERGRziaKRlvmGi2ibSEiFW5T51lJTvQ70D7aXiLSGZT9nLaamhqWLl1K3759MbNyxykrd2fp0qXU1NSUO4qISH6KdJ5VZ6HfxPbR76aIdBZlb7QNHDiQxYsXs2TJklbzV61aFV0RLkWmmpoaBg4cWNR1iIhInDb2m1gOlfI7rN9NEekMyt5oq66upr6+fr35M2fOjG44/BgziYhIx7Gx38RyiPE3L8ZMIiKlUNA5bWZ2uJktNLNXzexbaYUSERERERGRIO9Gm5lVAf8LHAHsCpxmZrumFUxEREREREQKO9K2F/Cqu7/m7h8Bk4D1rzQtIiIiIiIieSvknLYBwBtZ04uBz7Z9kpmNB8Ynk81mtjDH5fcD3i4gXzHEmAnizKVMuYsxV3syDSpmkGLpYLUJ4sylTLmLMVelZ6rI2gQdrj4pU+5izKVMuStqfbJ8r29iZicBh7v7ucn0WcBn3f3CvBa4/vKfdfeRaSwrLTFmgjhzKVPuYswVY6ZYxLptYsylTLmLMZcyVZ4Yt48y5S7GXMqUu2LnKqR7ZBPwyazpgck8ERERERERSUkhjba/AjubWb2ZdQNOBR5MJ5aIiIiIiIhAAee0ufsaM7sQmAZUAbe5+wupJYNbUlxWWmLMBHHmUqbcxZgrxkyxiHXbxJhLmXIXYy5lqjwxbh9lyl2MuZQpd0XNlfc5bSIiIiIiIlJ8BV1cW0RERERERIpLjTYREREREZGIqdEmIiIiIiISMTXaJDpmZuXOUE6Zv7+zbweR2HT276Rqk0icOvt3srPUpg7XaNvQG2ZmHe7v3JysD3CfcmfZnKysvc2sm5dpdJwYvuxmZll//xZlDbMJZlZV7gyVSPVJtamQHOWk2tSxqTYFlVKfYqlN2VnKuf7OUps61Bcy+40zs8PN7CAzG+zuH5ejkGd9qXY1s4GlXK+7u5l9HvidmfUr95dqY7KyHgvcBtxtZvsl1/4reY7k/o5m1rOU68/IyvAV4OdmdrGZfbYcWTbGzKrcfa2ZdTGz883sPDOrKXeu2Kk+qTYVkiO5r9q0CapN+VFtWrfeSqhPsdSm7CzJ/bLUp85UmzpUoy3rjbsQ+C4wBJhhZsOTjVXS4pN8qY4CpgD9S7HOrC/zwcC1wBXu/jbQvRTrb68k66HAfwAXJ7NvAT5fygLU5rNzO/AdM/tuOQq2mY0HzgBuAL4EXJAU5yhkCg8wFegLnATcZ2bDY/yBi0Vnr0+qTfnnANWmXKg25aez1yaorPoUS23KZIHy16fOUps6VKMNwMw+B5wKHEi4ePgK4Ekz2ytro5Uqy1DgeuB4d59rZp80s12LsJ5uZrYNrPsCAZ9K1v2umZ0FPJrsfeib9vrby8zqzezirFm7ARcBewKfACYSiubJZta7yFn6ZN0/AzgFOAHYHjiasNemqMUne/lmtjWwI3AssB+wHFgInG5mRxczx+a02Q7XAE+6+/eAbkAv4CpgSDn2zFaKzlafVJsKyqLalCPVpsJ1ttqUrKdi6lNMtSnJU9b61Glrk7tX9I3kAuFZ01sA2xJa3I8m834KfAwMLWKOLYH9gUFZ8+qAnwDfIOwR+QtwL3BqiuvtClwJ/C/wycz2AI4EHgGeAC4EvgJMBj5V5vdrAPA5YB9gQNb8bYDfATsl038AHga2LWKWHYErgO7J9DHJZ+eCZNsNAWYCt7b9nBXj8ws0AD2Sz/BOwB+S+f2AFwgFuXeZ3reqNtO9ktsdwFeTefMIe5HqyvkZi+nWmeuTalNBWVSbcs+p2lTg+5tMd5ralKyjYupTTLUpWU9Z61Nnrk0VfaQtczg7uX+JmV0BrHL3t4DtgN8nT50L3AesKmKcY4EzgS5m1t/MBgDNwOuEPVcLksefBtLcC9IFwN0vAAYBN5rZbcB84DzgRHe/kfCh2JHw4S4LM6sDngL+n7v/BbjfzG4GcPd3CHv2xprZPsBq4LvJe1ksa4CbgE+b2d7u/iDwAeEH5Fx3fx5oJHzJatNeeZvP7zcI3VJq3f0Dwvu0q5n1Az4LvApMcPcVaefIIWcXX9cX+4Gk28qq5FYNvJE8dTbwmLs3ljpjjFSfVJsKoNqUW07VpjyoNgEVUp8irE1QxvrU6WtTOVqfRWjNfpWwJ2bXrHlfJpygeTOhJbt9kTOcRPiAjgYeI+z9uIqwx6hr8pzhwBzg4JTXPQgYSShqg7PWX5U8fhzwHHBcmd+nvYEHgdMIe2S2BZ4BfpI8fjhwJ/AycHQRc2TvpRkKXEbYI7RXMm86cDZwLjAN6F/k7TKGUJS3bjP/2uTzMo8i7unMdZsR9jb+uM388YQf+L8CN29oG3f2W2euT6pN7c6h2pTHNlNtynvbddralCw3+voUS21K1hVNfeqstalsf0yBG6I+u5AAPweGJfczh2u3Ak4kHFrfvQSZagh7Y2YRDvf/R/LF2hKoSr54M9P68hP2XhydNf1NQl/eY4E/k3Q1ILTo9wcOyPVDUcRt1A2YBLwFnJ/M6598wb6X9bxM9mIfVh9P6BoxMik+NyefrQOAuwmH+YcVIUMDcHXW9LHAHcn9rmQdUgcGFrPwbSbnyUCf5P72hD2uvTLvZfLvDoQf1HM2tI07462z1yfVprwzqDblnlO1Kb/t1qlrU7K+iqpPMdSmtsstR31SbQq3rlSQ5IS+LYEvAv9lZj3d/X3CiaNjgHnu/mHy9MHu/ptSZPJgVXJI9veEgnckof/1e2a2C6Fv7RfdfVH24d0819kFOAw4wsIwolOAtcC3CYXmLHd/3cxOIvQtvjqzvkLWm4KPgfcI/a4HmdkId59jZocBfzazPu5+sbu/nnZWM6tx91WZZZrZAcARwFfc/U0ze5/QL/vfgJ+6+2lm1j3r85Sm14CbzWykuz+bTFeZ2SDgDQ/DLJ9O2Pv483K8Z2a2I7Da3Zcnn+vlhD7inwBedfePkqeOdPf7Cd1oMl0CPi513hioPqk25UO1qX1Um9pPtallnZVYn8pWmyCq+qTaBJV1pA3okvxbQ2h1X0koRPsQDsWenjx+BvASsF2Jcu1J6B/9CcKh9b+y7sTQIwndD1Jt9RP2JJxLODR9MGEI0ZeA7yePj0qmDyvze5Y5uXe75N8tkg/xdwl7aoYm8/sD+xcpw9bA2ZnPEGFP4l2Ebg9HZD1vMGF0nx8DNcX6/Cb3qwl7Dycm0z8G/g+4FDgf+BuwSznfuyTXFwh7Y2sIe9T+TvgxGwD8Brip3Bljuak+taxPtSn3DKpN+WdWbWrn+9vZa1Oy3OjrUwy1KVl+2euTalPrW+aDET0Lw4tu6e5vmNlgwsmyXyYcRv8D4XDjTYTD20MJRejFEmW7i3Boen8z+yqhb/YfCSdmfhv4d3d/KKV1Za4lUkM4GfQcQuH9GbCYcGi6kbA9rnf3329sWaViZkcSRhqaCbxNOJReS/hg7wDc6O7zirj+IcBJ7n6VmW3h7h+Y2VbA95M8k939heS5OwPvuPvSlDNUufva5P5Id3/WzLYlvG+vu/vXkr1EQwk/pjeW6vO7sZzJ9D6EvZ8fufsVyYm/BwMfAYvd/cJSZ4yR6pNqU57rV23KI2cyrdqUA9WmlnVVVH0qd21KMpS1Pqk2bWBdldBoSw5pHwwMI/QRHeXuI8xsL8KJmfOAXxJO+qsBPnb3N4uYJ/PlbzmkaWa/IpwQebSZnUjYg9Qb+K27P1LoYf026/00YSjeKwh/+znAvoTC+wyhD3Rfd29KY70FZt6XsMfheMLekGGEk32vIuxd+yIwKfPFL1KGKsKeqqOB0wmjLU0D7gH+m1Cwf+vuzxVh3XWeNSqQhQtQfhU4xN3/YWb9CSd9v575IptZtbuvTjtLDllbPtfAOMLoRtOBnQnDHq8E/jP5d5tMce7M3Y5A9anNOlWb2pdBtSm3rKpNeVBtWm+9FVGfYqhNSY6y1CfVpk3wMh9G3NQN2IXkpFnCCakPA+8QhmLNPGdvwpt3FbBDCbPtSRgWtlfWvLsJH+ZMY7hbEdb7ecI1Hp4m9AHfO9k25yXrPzF5XjlP6s/8/dsThurdDTiUMLTpcYSRkH4E9KQIXX3aZMl0CxlL6D+8F2Fv3mLCj1Zvwkm+/1ak9+vLQI/k/nHJNqhNpncm/EhsS/iS31Su947WXRAeJgxzfDvh2jhVhO4P/0XYw7VN2/e6M95Un9Zbp2pT+7KoNrVjOyX3VZty22aqTeuvN+r6FFNtSnKUrT6pNm1ineX4cLZjg5wNDMmaPoywl+RawoUGM8PBjiLsKdmmRLmqkg/wX4EvsW5kmN0J1xaZmnleyuvdkXDy5QjCyDNfTYpPQ/Ih/goRDHGa/HsQYU/R9oQ9eBOBzySP/Topkp8pYo7ubaZPBS7Nmq5LCtFAwsnYqffhT/7uycBuyfTRhJGqvkD4oVxE2Ms5gHCRzE+W6T3LHnVpV+DCrPy3Avcnn/ndSPHippV+U31qtU7VptxzqDblnlO1Kb/tptrUer1R16dYalOynrLWJ9WmTd9iv7j2lkCtmR1pZp8DGt39YsAJ/UV3NbMvEUaL+YaHCw0WhZlZ8u9gQl/r+YQTH88mXNiwK2EPyO2EEzLxrD6uaayb8GF42d3nuPtc4LeEvWc/AD7t7je7+/w01pkvd/ekP++hwH3u/g9C14sdgcPNbA/Cl/4/3f2lYmQwsy2B/c1skJmdbmGUpd7ACVk5GwmFZyt3/7u7/zPlDNu7+yrCCd+fNbPjgWcJPxpHEq4vsj/hPd3N3d9x9zc2usCUZX2eq33dBSAfIhTD081sSJL/AuBdQr/6l9x9UvbrO7lOX59Um9pHtSmnfKpNhev0tSl73URen2KoTVD++qTalINytFBzaMFmXw/ix8DjhD1EUwhDi1YB3yO0Zl+nBNcSSbIcRtg78zpwNaEwjkzm3Qa8SboXps3sfemZNW8m4Yr3memvJev+SZKn7F1CgF8l22JU1rw9Cd0SHgVOLvL6+yZfmseAV4Heyfz7gScIe9fOIxSe1PfSAEcR+sf3I+wJOoNwcvXn2jzvSEJBqivDe3QqsGPmc0boc/0twh6juwl7tnZOHq8Bzi335yqWm+qTalMB61dt2nxG1ab8t12nr03Z26GS6lO5a1OyvrLVJ9WmHDOU+o9u5wY6AHgwuf8d4E/JhjkmmVdL0s+1BFmGAQsJo9R8Afgfwt6ALQgnhg4lXHsh7fUeQRjh6RrCoejhhCFX7yAcNn6RsMfqdmCLMr9fnwJ2Te5PJAzh2y3r8V6EvTMtRTXl9Wf/YH0O+BdheNxds+b/GJiQ/FjsVoQMhxN+KI/IzgSclqwz02/+DMJwxkPSzpBjzgtILjCafJ4/BvZJpkcSfsy+RZuuGMV43yr11tnrk2pTu9av2pR7TtWmwrdhp65NyXoroj6Vuza1XW456pNqUzsylOMP38QG6dJmekfCIeHTCCdh9k8+SLMJF0EsZbZjgClZ0wcR9gpcQ/p7HDL9zfciXFn+tKTQ/IBwIcxawrUpfpIUon2BWYRRj0r9nmW+XPsTTgqdRrIHhHAy5v2U5qTZ7KJzCuFw+u7JF+g6kr1XyY9Ed4pzovM2yZf4uGR6p+QHYUugD2EvzRTgWEJ3kAGlfr+ysh4CHJU1fSswB+iXTI9Isn6+XBlju6k+qTYVkiO5r9q0+ayqTe3fZp2+NiXLroj6FEttys6S3C95fVJtat8tqnPafN0QsPuaWS2wxEP/2U8CN7v7EuD/EUZomV6KTGa2S3J3NtDFzI5Jsk4nHLbekXAIu+D+qmZWb+Hq9mvMrJ4w6swsd7+bcIE+CHuPBrn7+e7+dcKH+mbgIk/5+j25cHc3s4MJe2DuInypLjWzwe5+MuFLfl+xzzPwTPUxuwy4BPjQ3RcA9wFrgSPM7CbCnpAaX3dl+jQzvEPYg/cdMxtKGDFonru/5+7LgYcIX+gzkshNaWfYnGQIX9z9j8ChZva4mdW4+zmELhi/MbNt3X0OcImndI2cjqAz1yfVpsJygGrT5qg25a8z16bk9RVVn2KpTZksUL76pNrUTsVqDeZ7I/SXXUzo3/sD1g1/uoLQF/s1kj6lRc7RhTCq0FOEAtCb8IH+IfDvwH6EwvOfhJFuuqSwzoOBZYSrzlcRroPxMrBH8nh/wiHqnxCuawKh5V9fpvfKgK6EL9n5yby+hD16UwgFEqChiBnqgW2T+7sSCjWEovc5QjeRvoRD2XdRghGiCIf6Pwa+lUxXsW7PWi/ChU5L/n5l5evCuu4WvyYMVVuTTP8o+cxtmfV8dTtaty06ZX1Sbcorg2pT+/OpNuW/7TplbUrWWTH1KYbalCw/qvqk2pRjjnJuhLZ/GLAd4Urr2xMOM16TfMmqCCcfXkpWH9si58q8GQMJh88vT3IdRbg2xYNJxgMIh7N7pLTew4FXgD7J9LeBBzJfmKT4DC73+9Ym80WE4YQz19Hol/wNPwCq277PKa53a8I1QvoQfiR2SL44lwO3AL8hXNDwyOT5qQ4jvJlshyRZMu9jdQTvU6YAXkHofpB5v+4m9BvPfOZPK3fWWG6qT63WqdqU+3pVm9qXSbUpz22W3O/UtSlZX0XVp3LVpmS5UdYn1aYc8sSwMZL7FwA3Ek4y3CaZtydhb8zPKcK1ajaRa2fCKDCDkuntCX2wb8h6Tk1ShOaS7M1Jcf1HEkbu6UPYK/MtQv/sVNdTyHtG6PN8UPJlH004zD82KTyfSvLOA84pUo5PZX1ZhhFObO5C6JP9P8Bnk8e+BnyjWMVvMxmPIJyAXZJr4GwiR6aff+a960sYova/2xSgZ7KLZDm2WUw31acNrlu1afM5VJtyz6HalN92U23a8PqjrE+x1KYkQ9T1SbVp07eyntPmmb/Q7ETCCaP3E04+/E7y+F8JLdlGwmHTojCzGjPbKbm/A+HQ8KeA481skIdrZpwDfNPMrk6yrSKc6Hu6uz+XZh53fxi4mDCs6ZbA9YSRdbqmuZ58uLub2ZGE65ycTdgjsy3QRDipdnLyWOaHpFvaGcxsa8L2ucbMehH2Gn2SUGSmuvtF7v60mZ1D6DLy+8xnrZTc/Q+EvVl/Sq7nUZbrB7n7muTuxWY20kP//W8QPlvXmtnW7n4aMNHdV2e9ruTbLCaqT+tTbdo01aZ251BtyoNq04bFWp9iqE1QGfVJtWnTrNy1z8yGEfY23OPuN5nZNoSTZf/i7pckz6lJvujFWL8R9n58ntAfehhwOmH0oUMJ1xX5JeHQ+tXAz9z98WJk2UC2w5N1f9rDCZlllWyrPoRhaX/o7k9auPjhoYRuD38hFOzlwBBC94wTPaWLQZqZJcXPCF0h9gNWEa5D81nC+9YI3ANUJ5m+5OGk2rIxs17u3lyG9V5H+Nx2BZqBfybTt7r7/KSAzwGeBC5w92XJ66yz/6coQ/Vpo7lUm9qsX7WpXetVbSqQatMms0VTn8pdmzIZKq0+qTZtWMmPtG2g1fwBsICwZ2akh5FkDiWMWPNDaNkzUxTJRn6R0P/6S8CT7r40ae0/Cgwi9Lv+I3Cbuz9eqpa/u08FziUMTVt2HiwD3gc+k8x7APgboS+0J4VmDWFPySlpFh5C/3wIOxv+ADwPHE84uflZwqHqAcA4wkUqDyz3f4oAylR4fsm6Cz7eTxjueEfCCGLjzGxXd3+XcMHR5zOFJ8nbaf9TpPqUcy7VptZUm3Kk2pQf1aZ2ZYumPkVQm6AC65Nq00YylrIGZrdEzewIQot+HuDAWYQPza3uPsfM+hD6tC4qdh4z2xv4D0IB6g782d0nJ8/5FGEvSbW7P12sLLlmLdd6zWwQ4WKYLwPnEw6rT3X3Z82sgXDIfby7f5i8roe7r0wxRz9CcdnL3d8ys+0JPwjPAe8RfsCuA/Yh9Je/LvkR63TM7BDgMnc/NGveJwlD9i4l9Jv/EuF7N9/dL0ie06n3Yqs+FZazXOtVbaocqk35UW0qPGs51lnu2pQsU/UpB5VSm8rSPdLMzicMRfsgYU/DAYR+10cT9kT8l6fc13kTWfYgDLV6FuEw6BeABsKH+g3gAHf/eSmyxMrMjiWMnPMG4cN7H+EQ+ycIe4cagCuTvUeZ16T+QTazowmH808lnAx6v7v/r5mNJnx2VhP69FelXfgqSVJ8TnP3L5lZNWEn0JqkWD9M6L/+MbCTu9+WvKZT/6com+pT5VBtqiyqTYVRbaocsdSmZLmqT5tRKbWppCdnJofGPwUcRriuxunAHHefnzy+EjiRcHi2FHl2IOzpWJXZK2VmDxHemK8QCuKZpcgSEzPrTdg79o6ZDSZso4MJX/iL3P3LZjabMALSZ4D/SfYctXyAi/FBdvffmdlqYD5whbv/b/LQ46y7tsiW7v522uuuMG8ADWa2t7s/BWBmPd39H2Y2H1js7q8ShmPGzLp4cnHWzkz1KX6qTRVPtSkPqk3xi7U2JctVfdq8iqhNRT/Slhyqd3d/L/miryCMSlNP6Cv6eXdfbWYXAHcA77v72iLmye5m0J1wlfVTgDuBSUmW3oQLQvYv1V6rWCTF5gbCxS/vAowwOtUKwg/Fme7+dzNrcPfZZcp4COuGpl2eNX8Ld/+gHJlikvzA/xthj96v3H1u1mN/IHSJmEjoptGp92CrPlUO1abKp9qUO9WmylEJtSnJqfq0EZVSm4p6pM3MugEjCa3XLQgjsPyAMMrQZ4B9ky/6KcB44EF3f6+IeTL9jEcThpw1d78tebP2BFab2X3uvoLwZftHsbLEyMx2JVw88GbC4fN3zWxbwrVXhgBnJYXnYOAGMzvBi9hvfmPc/Y9m9nXgGTPbx5P+15296GQkn/FfEYYPviIpOH8Fvgv0IJyE/Jr+U6T6VClUmzoG1abcqDZVjkqpTaD6tCmVUptKcaRtZ+BXhGtBnOruT1g4QfVXhJGPehJGaznbizhaTVbROZTQp/fnhMPWCwhv0ljCIf1H3f3XxcoRq2QP2RTg1+5+a9b8kwgn0L5IGBL2Q0L3h39394dKn3QdC33GryL8uHm5v0yxsTA07WGEbhrPAR+4+zfLmyouqk/xU23qeFSbNk+1KX6VWJtA9WlTYq9NRWm0ZR9GT6YnEFqqS4A73H2hmfUlnIhZDbzg7o2pBwnr/gRh9J5VHvqm3gL81ZMTZM1sGvC6u483swsJhefFYmSJmZl1BW4FLs4cNrdwgcULCZeGWEm46ONuwB/d/dG273M5WJmu5VFJzKybu3+UNd2pzxNRfaosqk0dl2pTa6pNlaVSaxOoPm1OrLUp9UZb9gfSzEYQTu57h9BP9GuE60VcCQwGatz9yVQDtM7yacJeqeVJjt8m6303q/D0IfQHPw4ghjel1JIuDn0IJ6X+u7s/nMw7n3CxRQceAI71cI0KqSBtvpNR/GCUi+pTZVFt6thUm9ZRbaosqk0dW6y1KfWLa2f9kRcB/wd8i3Ci6hLgJsJQpw8Trrj+z7TXn5H0M76bcPHALwKzgdGEK5x/2cxGmFkX4NNALeH6GVG8KaXmwTLCHqGTzGxE8j7e7GE0oV0IJ2FWlzGm5Cm72MRSeMpF9amyqDZ1bKpN66g2VRbVpo4t1tqU2pE2M9s6szfBzE4m7Bk6gnDthxMJ/XoPJ1wP4nDgbx6GzywKM9sfmOXuXZLpzDVMjrRwrZPjgCbCib1XufuDxcpSKcysP+F96wtMJgxtujehH/u33X1q+dKJ5E/1qbKpNklHpdpU2VSbpJRSabQlJ6j+gHD9h0eSQ/v/BI4hFJ2jgamE60Ec7CUaqcbMDgf+z913NLPjCf2Mj3X35mRv0seE62o8H9Phz3Iys1rCicVfJZyEWQ9c5+5TyplLJF+qTx2DapN0NKpNHYNqk5RKWkP+DwZ2By41s+4eLuTXBRgB/MDdPzSzJ5Lp/sDrKa13k9x9qpldaGbNwMvAKHf/ICkyL7Z5bqcuOhnu/ibwP2Y2GVgLdHf3JhVmqWCqTx2AapN0QKpNHYBqk5RKWkfa+gHfJpywuj9wj7vfY2G0oSbCYf2DgVPcfUnBK2x/vgMJF8sbmEx3dfc1pc4hIqWn+iQiMVJtEpH2yHsgEjMbamZDk8l3gI8I1wy5CTjDzMYQDvtXA3sAXy9H0QFw90eBc83sraT/uIqOSAem+iQiMVJtEpF85XWkzcJ1QpYQ9gR9nXDIfi7hxMsHCaMJnUEYRef3Zlbl7mtTS50nMzuScKG8meXOIiLFofokIjFSbRKRQuR1Tpu7LzWzg4E/AUOBzxAKUBPQ393vNLMewNlmNoswVGzZufvDENc1F0QkXapPIhIj1SYRKURB57SZ2UHAbYSTZE8CTif0zf4SYbQj3H1F4TFFRNpH9UlEYqTaJCL5KHggkuSw+fXAPslwsPXuviiVdCIiBVB9EpEYqTaJSHsVPOS/uz9sZgB/NbP9MkVHh9FFpNxUn0QkRqpNItJeqVynLSk+1cCfzGxkmKWiIyLlp/okIjFSbRKR9kjlOm0tCzPr5e5RnDgrIpJN9UlEYqTaJCK5SLXRJiIiIiIiIunK++LaIiIiIiIiUnxqtImIiIiIiERMjTYpCjP7spltXe4cIiJtqT6JSIxUm2RT1GgTzMzN7M6s6a5mtsTMHtrM64Yl15ppO/87wLvu/u5GXre9md1XcHAR6fBUn0QkRqpNUmqpDPkvFe99YHcz6+HuK4FDgKYcXjcMGAk8nD3T3b+7sReYWVd3/wdwUv5xRaQTUX0SkRipNklJ6UibZDwMHJXcPw24O/OAme1lZn8xs7lm9qSZDTazbsB3gVPMbJ6ZnWJmPc3sNjN7Jnnuscnrx5nZg2b2KDDdzOrMbEHyWJ2ZPW5mc5Lbvsn87cxsVrLsBWZ2QCk3hohERfVJRGKk2iQlo0abZEwCTjWzGmAo8HTWYy8DB7j7cOA7wA/c/aPk/j3uPszd7wG+DTzq7nsBY4AbzKxnsowRwEnu/rk2630LOMTdRwCnAD9N5p8OTHP3YcAewLxU/1oRqSSqTyISI9UmKRl1jxQA3H2+mdUR9hQ93ObhPsDtZrYz4ED1RhZzKHCMmV2aTNcAOyT3/+ju72zgNdXAjWY2DFgL7JLM/ytwm5lVA1PcfV67/ygR6RBUn0QkRqpNUko60ibZHgR+RNbh/cT3gBnuvjtwNKGgbIgBJyZ7j4a5+w7u/lLy2Psbec3XgTcJe4RGAt0A3H0WMIrQP3yimZ2d598kIh2D6pOIxEi1SUpCjTbJdhtwjbs/32Z+H9adXDsua/4KoHfW9DTgIjMzADMbnsM6+wD/dPePgbOAquS1g4A33f3nwC8IXQREpPNSfRKRGKk2SUmo0SYt3H2xu/90Aw/9ELjWzObSukvtDGDXzMm0hL1K1cB8M3shmd6c/wO+YGbPAZ9m3V6l0cBzyTpPAf47n79JRDoG1ScRiZFqk5SKuXu5M4iIiIiIiMhG6EibiIiIiIhIxNRoExERERERiZgabSIiIiIiIhFTo01ERERERCRiarSJiIiIiIhETI02ERERERGRiKnRJiIiIiIiEjE12kRERERERCL2/wFEv1CWGLdY8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(style=['o','x','*'], rot=45, \n",
    "        subplots=True, figsize=(15,4), \n",
    "        sharey=True, layout=(1, 3), \n",
    "        yticks=range(0,11), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "42aae55a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alunos\n",
       "Fulano      7.714286\n",
       "Beltrano    5.142857\n",
       "Sicrano     7.714286\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7f464fb9",
   "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>Alunos</th>\n",
       "      <th>Fulano</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matérias</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Alunos      Fulano\n",
       "Matérias          \n",
       "Matemática     8.0\n",
       "Português     10.0\n",
       "Inglês         4.0\n",
       "Geografia      8.0\n",
       "História       6.0\n",
       "Física        10.0\n",
       "Química        8.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano = df[['Fulano']]\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854f4dfb",
   "metadata": {},
   "source": [
    "- Calculando a nota média do Fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8089d30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.714285714285714"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nota_media_fulano = df['Fulano'].mean()\n",
    "nota_media_fulano"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6eaa6d6",
   "metadata": {},
   "source": [
    "- Calculando o Desvio (diferença entre as notas de Fulano e sua nota média)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69d84f7c",
   "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>Alunos</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Desvio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matérias</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4.0</td>\n",
       "      <td>-3.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Alunos      Fulano    Desvio\n",
       "Matérias                    \n",
       "Matemática     8.0  0.285714\n",
       "Português     10.0  2.285714\n",
       "Inglês         4.0 -3.714286\n",
       "Geografia      8.0  0.285714\n",
       "História       6.0 -1.714286\n",
       "Física        10.0  2.285714\n",
       "Química        8.0  0.285714"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['Desvio'] = notas_fulano['Fulano'] - nota_media_fulano\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa23663f",
   "metadata": {},
   "source": [
    "- Calculando o módulo (remoção do sinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b1553e3b",
   "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>Alunos</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Desvio</th>\n",
       "      <th>|Desvio|</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matérias</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4.0</td>\n",
       "      <td>-3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.714286</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Alunos      Fulano    Desvio  |Desvio|\n",
       "Matérias                              \n",
       "Matemática     8.0  0.285714  0.285714\n",
       "Português     10.0  2.285714  2.285714\n",
       "Inglês         4.0 -3.714286  3.714286\n",
       "Geografia      8.0  0.285714  0.285714\n",
       "História       6.0 -1.714286  1.714286\n",
       "Física        10.0  2.285714  2.285714\n",
       "Química        8.0  0.285714  0.285714"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['|Desvio|'] = notas_fulano['Desvio'].abs()\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5186116b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Matemática', 'Português', 'Inglês', 'Geografia', 'História', 'Física',\n",
       "       'Química'],\n",
       "      dtype='object', name='Matérias')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0e6b8ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "notas_fulano['Média'] = nota_media_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ff112776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Matérias'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEtCAYAAAD6PoAVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuyElEQVR4nO3de5xVdb3/8dfHcYQBLyCiBahgGaGIXEYkFSMlQSTFO2imktkpFesUpXkUpV9a0VXr5CVRK8NMESlNtMzUkzcQBEs5XQ4KaF5QLHFMLp/fH5/vhs3IZWbvPbP2rHk/H495zN5r79n7s/as/Vnf9b2auyMiIm3bNlkHICIi5VMyFxHJASVzEZEcUDIXEckBJXMRkRxQMhcRyYFtW/PNdtllF+/du3drvqWISJs3b968V929+5ae06rJvHfv3sydO7c131JEpM0zs+e29hxVs4iI5MBWS+ZmNh0YC7zs7v3Ttp2BXwC9gSXASe7+esuFmQ+z5i9n2pzFvLCygR5d6pg8qi/jBvXMOiyRqqPvSvM1pWR+IzC60bYLgN+5+97A79J92YJZ85dz4cxFLF/ZgAPLVzZw4cxFzJq/POvQRKqKviul2Woyd/cHgdcabT4GuCndvgkYV9mw8mfanMU0rF670baG1WuZNmdxRhGJVCd9V0pTap35bu7+Yrr9D2C3zT3RzM42s7lmNveVV14p8e3avhdWNjRru0h7pe9KacpuAPWYdnGzUy+6+7XuXu/u9d27b7FnTa716FLXrO0i7ZW+K6UpNZm/ZGbvBUi/X65cSPk0eVRf6mprNtpWV1vD5FF9M4pIpDrpu1KaUpP5bOD0dPt04M7KhJNf4wb15Irj9qNnlzoM6NmljiuO208t9CKN6LtSGtva4hRmNgMYAewCvARMAWYBtwJ7AM8RXRMbN5K+S319vWvQkIhI85jZPHev39JzttrP3N0nbOahw0uKSkREKk4jQEVEckDJXEQkB5TMRURyQMlcRCQHlMxFRHJAyVxEJAeUzEVEckDJXEQkB5TMRURyQMlcRCQHlMxFRHJAyVxEJAfKSuZmdr6ZPW1mfzKzz1UoJhERaaaSk7mZ9Qc+BQwF9gfGmtn7KxWYiIg0XTkl837AY+7+lruvAf4AHFeZsEREpDnKSeZPA8PNrJuZdQLGALs3fpIWdBYRaXklJ3N3fwb4BnAvcA+wAFi7iedpQWcRkRZWVgOou1/v7kPc/VDgdeB/KxOWiIg0x1aXjdsSM9vV3V82sz2I+vJhlQlLRESao6xkDtxuZt2A1cA57r6y/JBERKS5ykrm7j68UoGIiEjpNAJURCQHlMxFRHJAyVxEJAeUzEVEckDJXEQkB5TMRURyQMlcRCQHlMxFRHJAyVxEJAeUzEVEckDJXEQkB5TMRURyoNwpcD8PnAU4sAg4093frkRgBbPmL2fanMW8sLKBHl3qmDyqL+MG9azkW0g7puNLWlJrHl/lLOjcE5gE1Lt7f6AGGF+pwCA+iAtnLmL5ygYcWL6ygQtnLmLW/OWVfBtpp3R8SUtq7eOr3GqWbYE6M9sW6AS8UH5IG0ybs5iG1RuvRNewei3T5iyu5NtIO6XjS1pSax9f5awBuhz4FvA88CLwhrvf2/h55Szo/MLKhmZtF2kOHV/Sklr7+CqnmqUrcAzQB+gBdDazjzd+XjkLOvfoUtes7SLNoeNLWlJrH1/lVLOMBP7P3V9x99XATOCgyoQVJo/qS11tzUbb6mprmDyqbyXfRtopHV/Sklr7+CqnN8vzwDAz6wQ0AIcDcysSVVJo9VVvA2kJOr6kJbX28WXuXvofm10GnAysAeYDZ7n7vzf3/Pr6ep87t6L5XkQk98xsnrvXb+k55S7oPAWYUs5rNMlvLoB/LGrxtxERaRHv2Q+O/HqLvoVGgIqI5EBZJfNW08JnNBGRtk4lcxGRHFAyFxHJASVzEZEcUDIXEckBJXMRkRxQMhcRyQElcxGRHFAyFxHJASVzEZEcUDIXEcmBchan6GtmC4p+/mlmn6tgbCIi0kQlz83i7ouBgQBmVgMsB+6oTFgiItIclapmORz4m7s/V6HXExGRZqhUMh8PzNjUA+Us6CwiIk1TdjI3s+2Ao4FfburxchZ0FhGRpqlEyfxI4El3f6kCryUiIiWoRDKfwGaqWEREpHWUlczNrDPwUWBmZcIREZFSlLug8yqgW4ViERGREmkEqIhIDiiZi4jkgJK5iEgOKJmLiOSAkrmISA4omYuI5ICSuYhIDiiZi4jkgJK5iEgOKJmLiOSAkrmISA4omYuI5EBZE22ZWRfgx0B/wIGJ7v5IBeISkWaaNX850+Ys5oWVDfToUsfkUX0ZN6hn1mFJKykrmQPfB+5x9xPSikOdKhCTiDTTrPnLuXDmIhpWrwVg+coGLpy5CEAJvZ0ouZrFzHYCDgWuB3D3d9x9ZYXiEpFmmDZn8fpEXtCwei3T5izOKCJpbeXUmfcBXgFuMLP5ZvbjtFjFRrSgs0jLe2FlQ7O2S/6Uk8y3BQYDP3L3QcAq4ILGT9KCziItr0eXumZtl/wpJ5kvA5a5+2Pp/m1EcheRVjZ5VF/qams22lZXW8PkUX0zikhaW8nJ3N3/ASw1s8LRcjjw54pEJSLNMm5QT644bj96dqnDgJ5d6rjiuP3U+NmOlNub5Tzg5tST5e/AmeWHJCKlGDeop5J3O1bugs4LgPrKhCIiIqXSCFARkRxQMhcRyQElcxGRHFAyFxHJASVzEZEcUDIXEckBJXMRkRxQMhcRyQElcxGRHFAyFxHJASVzEZEcUDIXEcmBchd0XgL8C1gLrHF3TbolIpKBcqfABfiIu79agdcREZESqZpFRCQHyk3mDtxrZvPM7OxKBCQiIs1XbjXLIe6+3Mx2Be4zs2fd/cHiJ6QkfzbAHnvsUebbiYjIppRVMnf35en3y8AdwNBNPOdad6939/ru3buX83YiIrIZJSdzM+tsZjsUbgNHAE9XKjAREWm6cqpZdgPuMLPC6/zc3e+pSFQiItIsJSdzd/87sH8FYxERkRKpa6KISA4omYuI5ICSuYhIDiiZi4jkgJK5iEgOKJmLiOSAkrmISA4omYuI5ICSuYhIDiiZi4jkgJK5iEgOKJmLiORA2cnczGrMbL6Z/boSAYmISPNVYkHn84FngB0r8FrSBsyav5xpcxbzwsoGenSpY/Kovowb1DPrsETatbJK5mbWCzgK+HFlwpFqN2v+ci6cuYjlKxtwYPnKBi6cuYhZ85dnHZpIu1ZuNcv3gC8B68oPRdqCaXMW07B67UbbGlavZdqcxRlFJCJQ3rJxY4GX3X3eVp53tpnNNbO5r7zySqlvJ1XihZUNzdouIq2jnJL5wcDRZrYEuAU4zMx+1vhJWtA5X3p0qWvWdhFpHSUnc3e/0N17uXtvYDxwv7t/vGKRSVWaPKovdbU1G22rq61h8qi+GUUkIlCZ3izSjhR6rag3i0h1MXdvtTerr6/3uXPnttr7iYjkgZnNc/f6LT1HI0BFRHJAyVxEJAeUzEVEckDJXEQkB5TMRURyQMlcRCQHlMxFRHJAyVxEJAeUzEVEckDJXEQkB5TMRURyQMlcRCQHylmcoqOZPW5mT5nZn8zsskoGJiIiTVfOFLj/Bg5z9zfNrBZ42Mx+4+6PVig2ERFpopKTucfcuW+mu7Xpp/Xm0xURkfXKqjM3sxozWwC8DNzn7o9VJCoREWmWspK5u69194FAL2ComfVv/Bwt6Cwi0vIq0pvF3VcCvwdGb+IxLegsItLCyunN0t3MuqTbdcBHgWcrFJeIiDRDOb1Z3gvcZGY1xEnhVnf/dWXCEhGR5iinN8tCYFAFYxERkRJpBKiISA4omYuI5ICSuYhIDiiZi4jkgJK5iEgOlNM1sSJWr17NsmXLePvtt7MOpSp07NiRXr16UVtbm3UoItKGZJ7Mly1bxg477EDv3r0xs6zDyZS7s2LFCpYtW0afPn2yDkdE2pDMq1nefvttunXr1u4TOYCZ0a1bN12liEizZZ7MASXyIvosRKQUVZHMRUSkPLlL5rNmzcLMePbZmPNryZIl9O//rpl5RURyJXfJfMaMGRxyyCHMmDEj61BERFpNOVPg7m5mvzezP6cFnc+vZGClePPNN3n44Ye5/vrrueWWW971+I033si55567/v7YsWN54IEHANh+++256KKL2H///Rk2bBgvvfQSECX7ww47jAEDBnD44Yfz/PPPA/DLX/6S/v37s//++3PooYe2/M6JiGxBOSXzNcAX3H0fYBhwjpntU5mwSnPnnXcyevRoPvCBD9CtWzfmzZvX5L9dtWoVw4YN46mnnuLQQw/luuuuA+C8887j9NNPZ+HChZx66qlMmjQJgKlTpzJnzhyeeuopZs+e3SL7IyLSVCUnc3d/0d2fTLf/BTwD9KxUYKWYMWMG48ePB2D8+PHNqmrZbrvtGDt2LABDhgxhyZIlADzyyCOccsopAJx22mk8/PDDABx88MGcccYZXHfddaxdu7aCeyEi0nwVGTRkZr2Juc0zW9D5tdde4/7772fRokWYGWvXrsXMOOecc9Y/Z9ttt2XdunXr7xf3566trV3fLbCmpoY1a9Zs8f2uvvpqHnvsMe666y6GDBnCvHnz6NatW4X3SkSkacpuADWz7YHbgc+5+z838XirLOh82223cdppp/Hcc8+xZMkSli5dSp8+fVi6dOn65/Tu3ZsFCxawbt06li5dyuOPP77V1z3ooIPW17/ffPPNDB8+HIC//e1vHHjggUydOpXu3btv9D4iIq2trJK5mdUSifxmd5+5qee4+7XAtQD19fVezvttyYwZM/jyl7+80bbjjz+eK664Yv39gw8+mD59+rDPPvvQr18/Bg8evNXXveqqqzjzzDOZNm0a3bt354YbbgBg8uTJ/OUvf8HdOfzww9l///0ru0MiIs1g7qXlV4s6iZuA19z9c035m/r6ep87d+5G25555hn69etXUgx5pc9ERIqZ2Tx3r9/Sc8qpZjkYOA04zMwWpJ8xZbyeiIiUqJwFnR8GNJGIiEgVyN0IUBGR9kjJXEQkB5TMRURyQMlcRCQHlMyJEZ8DBw5c/1MYyr8pjSfrEhGpBpmvAdpcs+YvZ9qcxbywsoEeXeqYPKov4waVNyVMXV0dCxYsqEyAIiIZaFMl81nzl3PhzEUsX9mAA8tXNnDhzEXMmr+84u/Vu3dvXn31VQDmzp3LiBEj3vWcX/3qVxx44IEMGjSIkSNHrp8299JLL2XixImMGDGCvfbaiyuvvHL933znO9+hf//+9O/fn+9973sVj1tE2qc2lcynzVlMw+qNZyhsWL2WaXMWl/W6DQ0N66tYjj322Cb/3SGHHMKjjz7K/PnzGT9+PN/85jfXP/bss88yZ84cHn/8cS677DJWr17NvHnzuOGGG3jsscd49NFHue6665g/f35ZsYuIQBurZnlhZUOztjdVqdUsy5Yt4+STT+bFF1/knXfeoU+fPusfO+qoo+jQoQMdOnRg11135aWXXuLhhx/m2GOPpXPnzgAcd9xxPPTQQwwaNKis+EVE2lTJvEeXumZtL0fxdLnFU+UWO++88zj33HNZtGgR11xzzUbP69Chw/rbTZlSV0SkHG0qmU8e1Ze62pqNttXV1jB5VN+Kv1fv3r3Xr1R0++23b/I5b7zxBj17RuPrTTfdtNXXHD58OLNmzeKtt95i1apV3HHHHeun1BURKUebSubjBvXkiuP2o2eXOgzo2aWOK47br+zeLJsyZcoUzj//fOrr66mpqdnkcy699FJOPPFEhgwZwi677LLV1xw8eDBnnHEGQ4cO5cADD+Sss85SFYuIVETJU+CWQlPgNo0+ExEp1tJT4GJm083sZTN7upzXERGR8pRbzXIjMLoCcYiISBnKSubu/iDwWoViERGRErV4A2hrLegsItKetXgyd/dr3b3e3eu7d+/e0m8nItIutamuidXgmmuu4fXXX886DBGRjSiZA2bGxz/+8fX316xZQ/fu3Rk7duxGz5s6dSpdu3ala9eum3ydESNGUOh6OWbMGFauXNliMYuIFCtrbhYzmwGMAHYxs2XAFHe/vhKBtabOnTvz9NNP09DQQF1dHffdd9/6kZ3FLrnkkia/5t13313JEEVEtqjc3iwT3P297l7r7r3aYiIvGDNmDHfddRcAM2bMYMKECesfW7VqFRMnTmTo0KEMGjSIO++8E4jZFsePH0+/fv049thjaWjYMOFX8RS648aNY8iQIey7775ce+21rbhXItJeVNesib+5AP6xqLKv+Z794Mivb/Vp48ePZ+rUqYwdO5aFCxcyceJEHnroIQC+9rWvcdhhhzF9+nRWrlzJ0KFDGTlyJNdccw2dOnXimWeeYeHChQwePHiTrz19+nR23nlnGhoaOOCAAzj++OPp1q1bRXdTRNq36krmGRowYABLlixhxowZjBkzZqPH7r33XmbPns23vvUtIGZRfP7553nwwQeZNGnS+r8fMGDAJl/7yiuv5I477gBg6dKl/OUvf1EyF5GKqq5k3oQSdEs6+uij+eIXv8gDDzzAihUr1m93d26//Xb69m3+7IwPPPAAv/3tb3nkkUfo1KkTI0aM2OyUuiIipVJvliITJ05kypQp7LfffhttHzVqFFdddRWFSckKqwMdeuih/PznPwfg6aefZuHChe96zTfeeIOuXbvSqVMnnn32WR599NEW3gsRaY+UzIv06tVrfbVJsYsvvpjVq1czYMAA9t13Xy6++GIAPvOZz/Dmm2/Sr18/LrnkEoYMGfKuvx09ejRr1qyhX79+XHDBBQwbNqzF90NE2h9NgVuF9JmISLEWnwJXRESqg5K5iEgOVEUyb82qnmqnz0JESpF5Mu/YsSMrVqxQEiMS+YoVK+jYsWPWoYhIG5N5P/NevXqxbNkyNNd56NixI7169co6DBFpYzJP5rW1tfTp0yfrMERE2rRyF3QebWaLzeyvZnZBpYISEZHmKTmZm1kN8EPgSGAfYIKZ7VOpwEREpOnKKZkPBf7q7n9393eAW4BjKhOWiIg0Rzl15j2BpUX3lwEHNn6SmZ0NnJ3uvmlmi0t8v12AV0v822qTl33Jy36A9qVa5WVfyt2PPbf2hBZvAHX3a4GyV2Qws7lbG87aVuRlX/KyH6B9qVZ52ZfW2I9yqlmWA7sX3e+VtomISCsrJ5k/AextZn3MbDtgPDC7MmGJiEhzlFzN4u5rzOxcYA5QA0x39z9VLLJ3y9PimXnZl7zsB2hfqlVe9qXF96NVp8AVEZGWkfncLCIiUj4lcxGRHFAyFxHJASVzkTKZmWUdQ3tR+Kz1mb9b7pP5pv7pZpb7/c5S0Rdup6xjaQlF+7eDmW3nbagXQVtOgmZmRZ91p0yDqbA011VZcp3Uiv/5aYbHw82sr7uvq8SH11qKksc+ZlbVk50XPnMzGwv8ysx2acsJpLGi/TsGmA7MMLOD01iLqtbo+7CXmXXOOqbmKIr9P4DrzGySmb1rCpG2xsxq3H2tmW1jZp81s0+ZWbNXqMl1Mi/6558LTAX2A35vZoPSh9cmEnpKHkcBs4DuGYezWUWJbiRwBfAVd38V6JBxaBWT9u8I4L+ASWnztcDYak/ojb4PNwGXmNnUtnSyTXM9nQpMAyYC56QTa5tVSOTAPUA34ATgNjMb1Jz/Ta6TOYCZfZgYnXoYMUjqX8AfzWxo0YdY1cxsAPAN4Fh3n29mu1fLdMNmtp2Z7QwbkgXwPiLe183sNOD+VIrqllWc5UijnCcVbdoXOA84AHgPcCNx8jrRzHZo/Qi3rLi6y8xOBU4GjgN6AB8jSrlVmdCL4zKzrsBexOysBwNvAIuBU8zsY9lEWLpGn/llwB/d/avAdsD2wBRgv6YWOqs+kTXXJg7KJ4gD9xhgjLv3Ba4DHjWzAe6+rrVj3BIz29HMDjGz4lnS/gncB4wys/8CbgUuM7PxmQSZmNm2wJeBr6YTTOGzXwp8ArgG2An4CXAI0CWLOMthZj2BPYAn0m3c/bvA/wKfAk5392nA34kSY11WsW6Kme1FlF4LV0f/Ao4HTgJ2I/5P7wd+XG0JvVG10BDgbeIKe2fgKHf/MHGM9QcOqsYT6eakqpXitpZvAN8ys58Ct7r7CKA38E02ngNrs3KVzBv98883s68Ab7v7y8B7gbvSU+cDtxEHR7U5Bvg4sI2ZdU8J5E3gOeLq4un0+GNA1gfvNgDufg4xRecPzGw6sJBIdMe7+w+Iy8e9qLJEtzVm1ht4FHje3R8BZprZ1QDu/hqRGE8ysw8Bq4Gp6VirJmuAHwEfNLNh7j4beIs4uZ7l7ouAJURJcLfMomyk0Xf5P4kkvpu7v0UcR/uY2S7EtNt/Bb7n7v/KLOBmMLNtiurI70hVqG+nn1o2TC0+D/iDuy9p0gu7e+5+gM8AjwD7FG37NNFgdTWwAOiRdZybif0E4os1AvgD8Cvicqs3sG16ziDgSWBkFcS7J1BPnFz6FsVckx4fBzwFjMs61hL2bRgxedwE4BxgV+Bx4Lvp8dHAz4BngY9lHW+j2K3o9gBgMnA9MDRt+x1RKj+LmF+pe9Yxb2Y/PkKcULs22n5F+g4sAAZkHWcp/x/iCvs7jbafTRQ6nwCu3tT/c7OvmfVOVeiD6VOcnIlqlIHpdof0uwtxeflfQP+sY97CvnQkSrEPAjumeB9Pt2tSgnkgy+RIlOA+VnT/C0Qd5jHA/wB7pu21RAlweLq/1QOymn6IustbgJeBz6Zt3VMS+WrR8wr7WxX71yiRn00s71ifEvrV6fsyHJgB3Fv4rlTDDzAEuLTo/jHAT9PtbUmFhHS/V7WehDazbycCO6XbPYjage0Lx1r6vQdRWPvkpv6fW/pp8cUpWlKq49sROBP4tpl1dvdVRAPcR4AF7v7v9PS+7n57RqFuVeGy0t3fTpePdxEnnzHAeHf/p5l9APgTcKa7/1/xpWgrxrkNMAo4MtX7zQLWAhcRyfs0d3/OzE4geg9dWoixtWOtgHVEe8VvgD3NbLC7P2lmo4D/MbOd3H2Suz8H2e+fmXV097cLcZjZcGKN3v9w95fMbBVwNPAl4Ep3n2BmHYq+I9Xg78DVZlbv7nPT/ZrUhrTUo1vxKcTV63VZf+ZNldouVrv7G+n7/Qax+tB7iOU330lPrXf3mURVcKFKpmntelmfrco8022TfnckzugXE8n9Q8Sl4ynp8VOBZ4D3Zh3zVvbnAKIe+j1EVcUTwPvTY2OIqqPMSyJEiegs4rJ9JNGd6hnga+nxQ9P9UVnHWsK+FWYSfW/63Sl96aYSJdwBaXt34JCs4y2KuyvwiXR7G+JK9GaiiuvIouf1JXpOfAfomHXcRXFtU3S7lrj6vDHd/w7w38AXgc8Sjc8fyDrmEvfzdKLmoCNxpfQ3otDTE7gd+FGpr91mp8BN3a12dPelZtaXaOD8NFEl8RvicuVHxGX/ACKx/zmreJvCzG4mLtkPMbPPEPXm9xENVhcBX3b3X2cYX6EfeUeiYe2TxInzGmIN2BlEY9oewDfc/a7NvVY1M7MxwFeIhPIqUTWxG/FF3AP4gbsvyCq+TTGz/YAT3H2KmXVy97fMrAvwNWIfbvW03oCZ7Q285u4rsot4g3SFtzbdrnf3uWa2K3FcPefun0ul8QFEYe0H1f5dLijet3T/Q8QV9zvu/pXUuDsSeAdY5u7nlvxebTGZp0v9kcBAou7pUHcfbGZDiYaqBcANRCNDR2Cdu7+UTbSbV5Qc119KmdlPiMaej5nZ8URpfQfgTne/N4uqlUaxfhC4kkh2C4iEfhBx4nycqGfu5u7Ls4q1HGZ2EFFyOpYoCQ4kGnWnEFdMZwK3eMsuxNJsqS9yJ6Lf+ClET5s5wC+A7xMn2zvd/anMgmzEzHp7UU8Ni8FMnwE+6u4vmFl3otPCc4UkZ2a17r46k4Cbqfj7DZxB9FL5HbA38B9AA/D/0u+dCyfXZlWtFMv6sqOZlygfIDV0Eo2BdwOvEV3gCs8ZRhwAU4A9so65Cft0ANGNb/uibTOIL2HhZLtd1nGmOMYCPyV6rtyVPuuaFP+Mwv+hEHdb+Sn6nHsQ3T73BY4guoaNI3q0fAvoTBVVTRTFX6huPImoax1KXNUtIwo3OxANuV+qlmMpxftpoC7dHpc+793S/b2JgsGuRAL8UVs6tti42uhuonvuTcAv03emL/Bt4upj58bHYik/ba2f+TCifhaPS5fvE13D6s3sw2a2rbs/SozIew/RP7tqpdKUET0OTjKz7dNDXyP29Tfp/tpN/HmrSg04VwLfJUoVvybaKAYSB+kfgL9A9g2BzVFUejqcqEu+n6jHPAX4uEcD75tEneYe7l41YxMKA4F8QyluG+Bmd3/c3R8g9SUnBm5dRPQKeWdTr9XaUlXd4UTPLYhj/OfAaDObQvSyuYaoPz8RuBzaxrGVqlYKV9r7AHe7+2ji5PVPIqH/lSh0/t5jzAJQ3v61tWS+I7CbmY2xGKa/xN0nAU7UQ+1jZhOJlu7/LP6QqkVhlF2q57+IGGDzWaLP70lpVGVnIkFeButPXJkoGhXYEXjW3Z909/nAncRV0eXAB939andfmFWcpUqJ/ENESfw2d3+BOMHuRSSW/Yk+/v/P3Z/JLtKNmdmOwCFmtqeZnZJ62OxAjHYGwKMKYz7Qxd3/5u4vZhPtxsysRzopXgwcaGbHAnOBwURD/6PEiagjsK+7v+buSzf7glWg6Htd6xsGBP2aqO49xcz2S/t8DvA60R7zjLvfUvz3Zcn6cqSJlyzF/Wa/AzxEDBqYRXS1qgG+SvSueI4q7kee9mEUUU3xHHApcZKqT9umAy+R8YAgNlQ9dC7a9gAxyrFw/3Mp3u+mfWgTl8Cb2NefpM/80KJtBxDVSfcDJ2Yd4yZi7pYSwx+IUt4OaftM4GGid9eniGS+e9bxFsV9FNG2sgsxLP9UopH/w42eN4ZI8L2zjrmJ+zUe2CvdNqIu/AJgH6IK8gvA3unxjsTo28rGkPWH0MwPbDgwO92+BPht+qCOTtt2I9W5VesPUS2xmGiZPx24iiihdCKqhgYQfU2rIdYjiaqey4i++4OI7m4/JRra/kxcUdwEdMo63hL2732kUcJE1dyvKKpTJq7wuqTbVXGiYuOCzYeBfxBdJotHO38H+F4qHOybdcxFcY0mCmJHFu8LMcL2Lja0uZxKdMPdL+uYm7Fv55AG0qXv9TrgQ+l+PVHouQDot7n/Z9kxZP0hbOUD2qbR/b2IS94JRKNU93QgzyMGq2QecxP26WhgVtH9w4mSymVUQQmKDVMGDCXqLSek5H05MRBrN6LP73dTcj+IGK3aLevYm7h/hQRyCNGwNodU+iPqMmdShY2cxbGn2ycT1RL9U5L4OunKIhUKOlBdjZ07pwQ3Lt1/fyoE7EjU6Y8nrrSPIaoZe2YdczP376PE5F+F+9cTI4V3SfcHp/0b21IxVHWduW9oRDjIzHYDXvGoB9ydmLfgFeB5oqX4d5kF2gRp9CbEiWcbMzsawN1/R1zO70Vc2meyGozFNK87ufsaM+tDtLQ/6O4ziMENECX1Pd39s+7+eeJLeDVwnldJn+WtcV8/3/r3iKuMzsAXLRYtOZFIgrdl8T/YGi9kdLPJwPnAv939aWJY+FpiVO6PiFJgR6+Sxk5YPzHZx4g51AcQjZsL3P2f7v4G0aA+iyiVu7svzyzYZkidGHD3+4AjzOyhNBL3k0QV3e1mtqu7Pwmc7y05TiTrM1oTznifIrpY/YQoHRa6j/2LqCf/O6muqhp/iEbm7YhGnW8TjVTnE1NbfpmY0+Qxoo7tVhpdjbRinCOBlcTIwRqip9CzwP7p8e7E5ft3SZMeEaWRPll/xs3YRyPm97iGDXOtdCOu7maxYY6VIVnH2ijuPsCu6fY+xEkW4sTzYaL6sRtxeX8zVTzxFFHVsg64IN2vYcPV0vbEQMDM42zmPm3Dhuq4nxNdETum+99K36Mdi57fIlV2mX8Qm/hgii8l30t00+tBXKZclpJJDdFA8kWK6gqr8afon9qLqI64MO3PUUTf39lp34YTl/l1GcY6muheuFO6fxFwBxsPYe+b9Wdagf08j+hmWejTvEva78uB2sbHYcaxdiX6h+9EFAr2SMnhQmKFo9uJQSdj0vNrsoy3ifv00bQPheOsNuuYStyPwknoK0SVUeF4mkG0ARS++xNaJZ6sP5BNfTjp9jnAD4iGkJ3TtgOIEux1VPk8KynevYlW7D3T/R5E/fi0oud0TIl9PqkUnHHMY4jeETsRJdkLiLrzzGMr55gi6pYPT8lwBFHNclJK5O9L+7iAotnqsv5JcRUSwkCioXwbor78KuDA9NjngP+slhNQE/ftSKIjwM5Zx1JC7IV2pcKx1Y3ogvj9Rgn98eITVUv/f6qqztwLexzD2CcQjVE7Ej1XcPcniDPeEuJSraqYWUcze3+6vQdxCfw+4Fgz29OjD/MngS+Y2aUAHn1PexNzx2Q+1Nrd7ybWtpxLfPbfIHogtMkZNt3d01wrdxI9b24nRhUuJxpBb02PFQoPVbGOp8USaZOIFaW2J0rouxOJ+x53P8/dHzOzTxJVkXcVvj9tgbv/hrji+G3qk111bRSb4+5r0s1JaS6ZFcTJdEfgCjPr6u4TiInCVhf9XYv+f6pubhYzG0iUmn7h7j+yWF/yHuARdz8/PaejV9FIPFjfaNmfGPLehShJnUL0CjmC6FN+A1FVcSlwjbs/lEGoTWJmo4l4P+jRQNXmpP/JTkS3w2+6+x/TAJUjiCquR4iT7RvEzHXfJbrHZTY4qGhEqhHVXgcTK9BcQayqcwpRmPkFMTryFmCiR0Nom2Nm27t7VY/ULjCzrxPf322JUcEvpvvXu/vCdAJ+EvgjcI67r0x/1ypzFGVeMt/EGfktYmm0Y9NZ7zXiy3ekmX0T1pdmq0r6Z/2ZqBufSCzOuiKVQO4nVuT5JTFAYrq7P1TNpRF3v4cYCj4o61hK5WElsArol7bdQUyhemF6yjPEDJBfAk7OMpEnhcV7LR07i4hJv75MXC3NIKYWOIMY6HRYW03kAG0okd/AhgFAM4luunsRvenOMLN93P11YmDdokIih9abgiDTknnxGcvMjiRKGguI4fmnEQft9R4LAuxE1K/9X1bxbk5RaWoYsTLQn4meBv/j7rem57yPKCXWuvtj2UXbfG1p9sOi/8WexECsZ4npEroS1RNzLRYHngSc7WlhBjOrc/eGzAKPGHYhEvZQd3/ZzHoQBYCniDk93iL6k3+IaGf5ulfhlBV5Y2YfBSa7+xFF23YnuoCuINpbJhJ5a6HHmrit/r2pimoWM/ss0d1wNlFiGk7UiX+MKFF9uxrqk7ckzeHxQ+Ik9CLRTWwI8WVcSiyddl12EbYfZnYM0cNgKfFlu42osngPUQofAlycSumFv6mKE5aZfYyoUhlPNKjNdPcfmtkI4vuwmmhDqsn65NNepGQ+wd0nmlktUdhek062dxPtGOuIhWSmp79p9eMp00atVM3wPmKukpFEfeCTniZsMrMGYgKtqpuLvFhq7JwEvF24ckiT7KwjZhgcTpyspAWY2Q7EFc9raQKzScTxNJ4Y0PRpM5tH9GTpB1yVSujrv3DVkMgB3P1XZraamIDtK+7+w/TQQ2zoV76ju7+aVYzt0FJgiJkN85iVFYslKl8ws4XEohJ/Jboelz4feZlavWSeqkvcY03LPYjBP58iBkbsRQx3XW1m5xDDyFd5hrMGbk6jKqIOxMi1k4kpeW9J+7ADMUioe7VfWbRVKXlPIwZe3Ux0p5xAHFeFaWz/ZmZD3H1edpE2TyoNFrofvlG0vZO7v5VdZO1PKnR+ibiy+4nHrKGFx35DVIHdSFTjZVYoaNWSuZltR0w6M8TMOhEtwZcTPT/6AQelJHgyMcf3bHf/Z2vG2BRF9bIjiG6F5u7T0z/9AGC1md3m7v8iksoLmQWbYxZzRd9ETCkw091ft1hubG+id8ppKZGPBKaZ2XHV2OayKe5+n5l9HnjczD5UqBtXIm996bv+E6L76ldSAn+CWBe2jmik/nvWV3dZlMz3Jobm706sOv9wahz8CdGLpTPRavyJamylL0rkRxB1mtcRl/NPE//sk4hqlfvd/efZRZpv6apnFvBzd7++aPsJRIPnn4kufP8mqroyXT+1VKn+fwpRCPKsE0Z7lroejiKq8Z4C3nL3L2Qb1QatkswbNwaY2feIM9orxOoni82sG9EwVQv8yYvWBqwGZvYeonfE26mu7FrgiUKjppnNIdYqPNtiLcP7vY0sOtsWWSzicT0wqVANYTGA5lyiy20DMQhoX+A+d7+/Who5m6st9cVuD8xsOy+axCyrOvLGWjyZN6pbHkw0JrxG1D99juhXezGxJl5Hd/9jiwZUAotFjH9CDC5ZSowY7Au8XpTMdyLq+MfBRkt5SYWl6qydiEbBL7v73WnbZ4nBNE7MKXNM6vsrUjGNclrVFBBafNBQ0U6fR8yDfQHRSPgKsaL7GqJ7zy1El76qkuplZxCDNs4kprAdQYwA+7SZDbZYffuDxFzfXYlkIi3Ew0qi5H2CmQ1Ox9nVqZfHB4hGqdoMw5ScKk7e1ZLIoQVL5hbzE7yebp9IlMKPJPrIHk/UZ44m+s2OBv43de+pKmZ2CDHl6DbpfqHf+5jUP34cMc/HQGCKu8/OKtb2xsy6E8dVN2KOlQeJhbC/D1zkMYpVpF1okWSeGgcvJ/rJ3puqV14kVtk5nhj8cA/Rb3ZktbfQW8xT8t/uvpfF3B7nEpfwb6aS+zqin/Oiarrsag8sFi05CfgM0SjVhxgZOSvLuERaW0t1TexLTDr1RTPrkAZCbEPM2325u//bzB5O97sTk1BVLXe/x8zONbM3ieHhh7r7Wylx/7nRc5XIW5G7vwRcZWa3EqvtdHD35TqpSnvTUiXzXYiFDZYS04z+wt1/kXqALCeqVkYSExu9UvEAWoiZHUYMGuiV7m/rG6bDFBHJTMUaQM1sgMXafhC9Vd4h+ov/CDjVzD5CWskF2B/4fFtK5ADufj9wlpm9nNoElMhFpCpUpGSe+oi/QpS6P09Um8wnGqJmEz08TiV6G9xlZjXVOES/qSwWO3jL3R/IOhYREahQnbm7r0hDpn8LDCCG5n+eSO7d3f1nZlYHfMLMHiS69bVZHqvxVFUfUxFp3ypaZ25mhxNz/A4GTiAmOlpKzPXbASDNVyIiIhVU8QbQVAXxDeBDqeten7YyuZGISFtV8a6JaWg1wBNmdrBvmN9bVRIiIi2kRfqZp4ReS6y8rdneRERaWItOtKXZ3kREWkdVrAEqIiLlafFZE0VEpOUpmYuI5ICSuUhiZp9OS4OJtDlK5tImmZmb2c+K7m9rZq+Y2RbX+TSzgWksROPtlxArR21yZSIz62Fmt5UduEgLaakpcEVa2iqgv5nVuXsD8FFi+oitGUgsjnx38UZ3n7q5P0izY75AjGoWqUoqmUtbdjdwVLo9gVjeDwAzG2pmj5jZfDP7o5n1NbPtgKnAyWa2wMxONrPOZjbdzB5Pzz0m/f0ZZjbbzO4Hfmdmvc3s6fRYbzN7yMyeTD8Hpe3vNbMH02s/bWbDW/PDkPZNyVzasluA8WbWkZjg7bGix54Fhrv7IGKpwsvTiuqXEPPrD3T3XxDz7t/v7kOBjwDTzKxzeo3BwAnu/uFG7/sy8FF3HwycDFyZtp8CzHH3gcQ0zwsqurciW6BqFmmz3H2hmfUmSuV3N3p4J+AmM9ubWGB7c4s7HwEcbWZfTPc7Anuk2/e5+2ub+Jta4AdmNpBY3egDafsTwPQ0+nmWuy9o9k6JlEglc2nrZgPfoqiKJfkq8Ht370+sOdtxM39vwPGppD7Q3fdw92fSY6s28zefB14iSt/1wHYA7v4gcChRd3+jmX2ixH0SaTYlc2nrpgOXufuiRtt3YkOD6BlF2/8F7FB0fw5wnqXZ4cxsUBPecyfgRXdfB5wG1KS/3RN4yd2vA35MVNOItAolc2nT3H2Zu1+5iYe+CVxhZvPZuDrx98A+hQZQogRfCyw0sz+l+1vz38DpZvYU8EE2lOBHAE+l9zyZWGlLpFVobhYRkRxQyVxEJAeUzEVEckDJXEQkB5TMRURyQMlcRCQHlMxFRHJAyVxEJAeUzEVEcuD/A4T/butD9gm/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "notas_fulano[['Fulano', 'Média']].plot(style=['o','-'], rot=45, yticks=range(0,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00382885",
   "metadata": {},
   "source": [
    "- Calculando o Desvio Padrão através na média dos valores do módulo do Desvio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e79162ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5510204081632648"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['|Desvio|'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4074264",
   "metadata": {},
   "source": [
    "- Calculando o Desvio Padrão através da função mad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "969c6cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5510204081632648"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['Fulano'].mad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005c5e1c",
   "metadata": {},
   "source": [
    "## <font color=green> Correlação e covariância"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec0356d",
   "metadata": {},
   "source": [
    "Algumas estatísticas de resumo, como **correlação** e **covariância**, são calculadas a partir de pares de argumentos.    \n",
    "> - correlação: resume o grau de relacionamento entre duas variáveis (X e Y, por exemplo).\n",
    "> ***\n",
    "\n",
    "|Correlação|Negativa|Positiva|\n",
    "|---|---|---|\n",
    "|Baixa|de -0,5 à 0|de 0 à 0,5|\n",
    "|Alta|de -1 à -0,5|de 0,5 à 1|\n",
    "\n",
    "> - covariância: é uma medida do grau de interdependência (ou inter-relação) numérica entre duas variáveis.\n",
    "> ***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d2a58c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa272b4",
   "metadata": {},
   "source": [
    "- Vamos carregar um dataset com o preço de ações de algumas *Big Techs*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8ec4bf77",
   "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>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>6.57</td>\n",
       "      <td>91.51</td>\n",
       "      <td>23.99</td>\n",
       "      <td>312.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>6.58</td>\n",
       "      <td>90.40</td>\n",
       "      <td>24.00</td>\n",
       "      <td>310.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>6.48</td>\n",
       "      <td>89.81</td>\n",
       "      <td>23.86</td>\n",
       "      <td>302.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>6.47</td>\n",
       "      <td>89.50</td>\n",
       "      <td>23.61</td>\n",
       "      <td>295.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>6.51</td>\n",
       "      <td>90.40</td>\n",
       "      <td>23.77</td>\n",
       "      <td>299.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL    IBM   MSFT    GOOG\n",
       "Date                                  \n",
       "2010-01-04  6.57  91.51  23.99  312.20\n",
       "2010-01-05  6.58  90.40  24.00  310.83\n",
       "2010-01-06  6.48  89.81  23.86  302.99\n",
       "2010-01-07  6.47  89.50  23.61  295.94\n",
       "2010-01-08  6.51  90.40  23.77  299.89"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = pd.read_csv('datasets/price_stocks.csv',index_col='Date')\n",
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a73103eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price.plot(style=['r-','g-','b-','y-'], rot=45, figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "61f9063e",
   "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>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>493729600</td>\n",
       "      <td>6155300</td>\n",
       "      <td>38409100</td>\n",
       "      <td>3927065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>601904800</td>\n",
       "      <td>6841400</td>\n",
       "      <td>49749600</td>\n",
       "      <td>6031925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>552160000</td>\n",
       "      <td>5605300</td>\n",
       "      <td>58182400</td>\n",
       "      <td>7987226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>477131200</td>\n",
       "      <td>5840600</td>\n",
       "      <td>50559700</td>\n",
       "      <td>12876685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>447610800</td>\n",
       "      <td>4197200</td>\n",
       "      <td>51197400</td>\n",
       "      <td>9484016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL      IBM      MSFT      GOOG\n",
       "Date                                              \n",
       "2010-01-04  493729600  6155300  38409100   3927065\n",
       "2010-01-05  601904800  6841400  49749600   6031925\n",
       "2010-01-06  552160000  5605300  58182400   7987226\n",
       "2010-01-07  477131200  5840600  50559700  12876685\n",
       "2010-01-08  447610800  4197200  51197400   9484016"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume = pd.read_csv('datasets/volume_stocks.csv',index_col='Date')\n",
    "volume.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "29ce5046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "volume.plot(style=['r-','g-','b-','y-'], rot=45, figsize=(15,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd70f0a",
   "metadata": {},
   "source": [
    "- Vamos agora calcular as mudanças percentuais nos preços."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "55b9ef09",
   "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>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>-0.005582</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>-0.002564</td>\n",
       "      <td>-0.002546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>-0.011227</td>\n",
       "      <td>-0.004607</td>\n",
       "      <td>0.004944</td>\n",
       "      <td>0.018854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>0.018248</td>\n",
       "      <td>0.015736</td>\n",
       "      <td>0.010823</td>\n",
       "      <td>0.018478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>-0.013142</td>\n",
       "      <td>-0.003099</td>\n",
       "      <td>-0.004283</td>\n",
       "      <td>-0.007211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>-0.019370</td>\n",
       "      <td>-0.012341</td>\n",
       "      <td>-0.014858</td>\n",
       "      <td>-0.015720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       IBM      MSFT      GOOG\n",
       "Date                                              \n",
       "2015-12-24 -0.005582 -0.002115 -0.002564 -0.002546\n",
       "2015-12-28 -0.011227 -0.004607  0.004944  0.018854\n",
       "2015-12-29  0.018248  0.015736  0.010823  0.018478\n",
       "2015-12-30 -0.013142 -0.003099 -0.004283 -0.007211\n",
       "2015-12-31 -0.019370 -0.012341 -0.014858 -0.015720"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = price.pct_change()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ea1590",
   "metadata": {},
   "source": [
    "- O método corr de Series calcula a correlação entre os valores diferentes de NA de duas Series, alinhados pelo índice e que se sobrepõem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2e019a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5080444874087223"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns['MSFT'].corr(returns['IBM'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51d5d47",
   "metadata": {},
   "source": [
    "- De forma relaciona, cov calcula a covariância"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f47fd8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.901383608936152e-05"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns['MSFT'].cov(returns['IBM'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0c271f",
   "metadata": {},
   "source": [
    "- Os métodos corr e cov de DataFrame, por outro lado, devolvem uma matriz completa de correlação ou de covariância como um DataFrame, respectivamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2ecbff50",
   "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>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.397686</td>\n",
       "      <td>0.379402</td>\n",
       "      <td>0.405395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.397686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.508044</td>\n",
       "      <td>0.413306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.379402</td>\n",
       "      <td>0.508044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.444555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.405395</td>\n",
       "      <td>0.413306</td>\n",
       "      <td>0.444555</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL       IBM      MSFT      GOOG\n",
       "AAPL  1.000000  0.397686  0.379402  0.405395\n",
       "IBM   0.397686  1.000000  0.508044  0.413306\n",
       "MSFT  0.379402  0.508044  1.000000  0.444555\n",
       "GOOG  0.405395  0.413306  0.444555  1.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b0b80388",
   "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>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL       IBM      MSFT      GOOG\n",
       "AAPL  0.000282  0.000080  0.000093  0.000110\n",
       "IBM   0.000080  0.000143  0.000089  0.000080\n",
       "MSFT  0.000093  0.000089  0.000214  0.000105\n",
       "GOOG  0.000110  0.000080  0.000105  0.000263"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "926c8fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price[['IBM', 'MSFT']].plot(style=['-','r-'], rot=45, figsize=(15,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c60d49",
   "metadata": {},
   "source": [
    "- Ao usar o método corrwith de DataFrame, podemos calcular correlações de pares entre as colunas ou linhas de um DataFrame com outra Series ou um DataFrame. Passar uma Series devolve uma Series com o valor das correlações calculado para outra coluna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "029dfd21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.397686\n",
       "IBM     1.000000\n",
       "MSFT    0.508044\n",
       "GOOG    0.413306\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corrwith(returns['IBM'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779260eb",
   "metadata": {},
   "source": [
    "- Passar um DataFrame calcula as correlações entre os nomes de coluna correspondentes. Nesse caso, calculei as correlações entre as mudanças percentuais e o volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "48bf3e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.082692\n",
       "IBM    -0.202109\n",
       "MSFT   -0.083289\n",
       "GOOG   -0.000192\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corrwith(volume)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.8.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "4ef1bb06cfdb40804b9995cc70859f43ff54c1a5223e7f7279b6762f9929c1e2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}