estudos_votos_presidente.ipynb

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   "cell_type": "markdown",
   "id": "4de72873",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:3rem;color:orange;\">Eleições 2022 - Análise dos Votos Para Presidente</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a84159",
   "metadata": {},
   "source": [
    "AVISO de Licença de Uso e Reserva de Direitos Autorais BSD 3 Clause. Copyright (c) 2022 por John Robson <john.robson@msn.com> (PIX)\n",
    "\n",
    "Todo o Código fonte e demais arquivos estão sobre a Licença BSD 3 Clause. Modificações, Redistribuição, uso Comercial são permitidos, sempre informando\n",
    "esse aviso de direitos autorais e o repositório oficial: https://github.com/JohnRobson/Eleicoes2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7542de9c",
   "metadata": {
    "tags": []
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   "outputs": [
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       "<IPython.core.display.Javascript object>"
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   "source": [
    "%%javascript\n",
    "IPython.OutputArea.auto_scroll_threshold = 9999;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ba82b44",
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    "tags": []
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       "<style>div.output_scroll { height: 100%; }</style>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "pd.options.display.float_format = '{:0,.2f}'.format\n",
    "\n",
    "import timeit\n",
    "import ast\n",
    "from sqlalchemy import select, func\n",
    "\n",
    "import ipympl\n",
    "import matplotlib.pylab as plt\n",
    "# %matplotlib inline\n",
    "%matplotlib widget\n",
    "\n",
    "from IPython.display import Markdown, display, HTML\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "display(HTML(\"<style>div.output_scroll { height: 100%; }</style>\"))\n",
    "\n",
    "from bd_urnas import engine, Base, Session, Urnas\n",
    "from src_config import federacao, estados_federacao\n",
    "session = Session()\n",
    "\n",
    "def printmd(string): # convert markdown para HTML\n",
    "    display(Markdown(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eba5f60e",
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    "tags": []
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   "source": [
    "# Importando Banco de Dados para Python Pandas Dataframe\n",
    "bd = pd.read_sql_query('SELECT id, turno, regiao, estado, municipio, zona, local, secao, vPresA22, vPresB13, vPresC15, vPresD12, vPresNulo, vPresBranco, qEleitAptos, qComparecimento, modelUrna FROM urnas', session.bind)\n",
    "\n",
    "# Criação de Colunas Extas para Análises\n",
    "bd['vPresBN'] = bd['vPresBranco'] + bd['vPresNulo'] # presidente votos Nulos e Brancos\n",
    "bd['vPresOutros'] = bd['qComparecimento'].sub(bd['vPresA22'] + bd['vPresB13'] + bd['vPresC15'] + bd['vPresD12'] + bd['vPresBN']) # coluna total votos governado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03d84088",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def modelos_pais(turno=1):\n",
    "  bdt = bd[bd['turno'] == turno]\n",
    "  # conta a quantidade de urnas de cada modelo\n",
    "  modelos_urna = bdt.groupby(['modelUrna'])['modelUrna'].count().rename('qtd Urnas', inplace=True)\n",
    "  # modelos_urna.drop(['UE0000', 'UE1111'], inplace=True) # remove modelos não informados\n",
    "  # conta o total de votos recebido em cada modelo\n",
    "  if turno == 1: total_votos_modelo = bdt.groupby('modelUrna')[['qComparecimento', 'vPresA22', 'vPresB13', 'vPresC15', 'vPresD12', 'vPresOutros', 'vPresBN']].sum()\n",
    "  else: total_votos_modelo = bdt.groupby('modelUrna')[['qComparecimento', 'vPresA22', 'vPresB13', 'vPresBN']].sum()\n",
    "  # total_votos_modelo.drop(['UE0000', 'UE1111'], inplace=True) # remove modelos não informados\n",
    "  total_votos_modelo.rename(columns={'qComparecimento': 'Votos Totais', 'vPresA22': 'Votos Bolsonaro', 'vPresB13': 'Votos Lula', 'vPresC15': 'Votos Simone', 'vPresD12': 'Votos Ciro', 'vPresOutros': 'Votos Outros', 'vPresBN': 'Votos BN'}, inplace=True) # renomeia as colunas\n",
    "  # calcula o percentual de votos que cada modelo de urna recebeu no país\n",
    "  perc_votos_modelo = total_votos_modelo['Votos Totais'].div(total_votos_modelo['Votos Totais'].sum()).multiply(100).round(2).rename('% Votos Totais', inplace=True)\n",
    "  \n",
    "  tabela_modelos_urna = pd.concat([modelos_urna, perc_votos_modelo, total_votos_modelo], axis=1)\n",
    "  # tabela_modelos_urna.rename(columns={'qComparecimento': 'Votos Totais'}, inplace=True) # renomeia as colunas\n",
    "  tabela_grupos_urna = tabela_modelos_urna.copy()\n",
    "  \n",
    "   # calcula a soma de cada coluna\n",
    "  tabela_modelos_urna.loc['TOTAL'] = tabela_modelos_urna.sum(axis=0)\n",
    "  \n",
    "  # calcula o percentual de votos de cada presidente de acoardo com o model de urna\n",
    "  tabela_modelos_urna['% Bolsonaro'] = tabela_modelos_urna['Votos Bolsonaro'].div(tabela_modelos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  tabela_modelos_urna['% Lula'] = tabela_modelos_urna['Votos Lula'].div(tabela_modelos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  if turno == 1:\n",
    "    tabela_modelos_urna['% Simone'] = tabela_modelos_urna['Votos Simone'].div(tabela_modelos_urna['Votos Totais']).multiply(100).round(2)\n",
    "    tabela_modelos_urna['% Ciro'] = tabela_modelos_urna['Votos Ciro'].div(tabela_modelos_urna['Votos Totais']).multiply(100).round(2)\n",
    "    tabela_modelos_urna['% Outros'] = tabela_modelos_urna['Votos Outros'].div(tabela_modelos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  tabela_modelos_urna['% BrancoNulo'] = tabela_modelos_urna['Votos BN'].div(tabela_modelos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  \n",
    "  # retira decimal dos números inteiros\n",
    "  tabela_modelos_urna['qtd Urnas'] = tabela_modelos_urna['qtd Urnas'].map('{:,.0f}'.format)\n",
    "  tabela_modelos_urna['Votos Totais'] = tabela_modelos_urna['Votos Totais'].map('{:,.0f}'.format)\n",
    "  tabela_modelos_urna['Votos Bolsonaro'] = tabela_modelos_urna['Votos Bolsonaro'].map('{:,.0f}'.format)\n",
    "  tabela_modelos_urna['Votos Lula'] = tabela_modelos_urna['Votos Lula'].map('{:,.0f}'.format)\n",
    "  tabela_modelos_urna['Votos BN'] = tabela_modelos_urna['Votos BN'].map('{:,.0f}'.format)\n",
    "  \n",
    "  if turno == 1:\n",
    "    tabela_modelos_urna['Votos Simone'] = tabela_modelos_urna['Votos Simone'].map('{:,.0f}'.format)\n",
    "    tabela_modelos_urna['Votos Ciro'] = tabela_modelos_urna['Votos Ciro'].map('{:,.0f}'.format)\n",
    "    tabela_modelos_urna['Votos Outros'] = tabela_modelos_urna['Votos Outros'].map('{:,.0f}'.format) \n",
    "\n",
    "  # Grupo de Urnas\n",
    "  tabela_grupos_urna.loc['NÃO-UE2020'] = tabela_grupos_urna.drop('UE2020').sum(axis=0)\n",
    "  tabela_grupos_urna = tabela_grupos_urna.tail(2) # mantém as 2 últimas linhas \"UE2020\" e \"NÃO-UE20202\"\n",
    "  tabela_grupos_urna = tabela_grupos_urna.iloc[::-1] # flipa as linhas\n",
    "  tabela_grupos_urna.loc['TOTAL'] = tabela_grupos_urna.sum(axis=0)\n",
    " \n",
    "  # calcula o percentual de votos de cada presidente de acoardo com o grupo de modelo de urna\n",
    "  tabela_grupos_urna['% Bolsonaro'] = tabela_grupos_urna['Votos Bolsonaro'].div(tabela_grupos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  tabela_grupos_urna['% Lula'] = tabela_grupos_urna['Votos Lula'].div(tabela_grupos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  if turno == 1:\n",
    "    tabela_grupos_urna['% Simone'] = tabela_grupos_urna['Votos Simone'].div(tabela_grupos_urna['Votos Totais']).multiply(100).round(2)\n",
    "    tabela_grupos_urna['% Ciro'] = tabela_grupos_urna['Votos Ciro'].div(tabela_grupos_urna['Votos Totais']).multiply(100).round(2)\n",
    "    tabela_grupos_urna['% Outros'] = tabela_grupos_urna['Votos Outros'].div(tabela_grupos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  tabela_grupos_urna['% BrancoNulo'] = tabela_grupos_urna['Votos BN'].div(tabela_grupos_urna['Votos Totais']).multiply(100).round(2)\n",
    "  \n",
    "  # calcula a diferença percentual entre as urnas \"NÃO-UE2020\" e as \"UE2020\"\n",
    "  tabela_grupos_urna.loc['DIFERENÇA %'] = tabela_grupos_urna.loc['NÃO-UE2020'].div(tabela_grupos_urna.loc['UE2020']).sub(1).multiply(100).round(2)\n",
    "  # limpa as primeiras colunas da linha \"Diferença %\"\n",
    "  if turno == 1: tabela_grupos_urna.loc['DIFERENÇA %'][0:9] = np.nan\n",
    "  else: tabela_grupos_urna.loc['DIFERENÇA %'][0:6] = np.nan\n",
    "  \n",
    "  tabela_grupos_urna['qtd Urnas'] = tabela_grupos_urna['qtd Urnas'].map('{:,.0f}'.format)\n",
    "  tabela_grupos_urna['Votos Totais'] = tabela_grupos_urna['Votos Totais'].map('{:,.0f}'.format)\n",
    "  tabela_grupos_urna['Votos Bolsonaro'] = tabela_grupos_urna['Votos Bolsonaro'].map('{:,.0f}'.format)\n",
    "  tabela_grupos_urna['Votos Lula'] = tabela_grupos_urna['Votos Lula'].map('{:,.0f}'.format)\n",
    "  tabela_grupos_urna['Votos BN'] = tabela_grupos_urna['Votos BN'].map('{:,.0f}'.format)\n",
    "  if turno == 1:\n",
    "    tabela_grupos_urna['Votos Simone'] = tabela_grupos_urna['Votos Simone'].map('{:,.0f}'.format)\n",
    "    tabela_grupos_urna['Votos Ciro'] = tabela_grupos_urna['Votos Ciro'].map('{:,.0f}'.format)\n",
    "    tabela_grupos_urna['Votos Outros'] = tabela_grupos_urna['Votos Outros'].map('{:,.0f}'.format) \n",
    "\n",
    "  # substitui NaN por vazio\n",
    "  tabela_grupos_urna.replace(np.nan, '', regex=True, inplace=True); tabela_grupos_urna.replace(str(np.nan), '', regex=True, inplace=True) # substitui NaN por vazio\n",
    "\n",
    "  return tabela_modelos_urna, tabela_grupos_urna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909f006c-70b5-4238-ba4d-6ea08613d59e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Relação dos Modelos de Urnas Usadas nas Eleições 2022, quantidade de Votos Válidos por modelo e Percentual de Votos Recebido por cada Candidato a Presidente:\n",
    "\n",
    "A Tabela 1 mostra a distribuição do percentual de votos recebidos em cada modelo de urna e para cada candidato a presidência. Nas eleições de 2022 foram utilizados 6 modelos de urnas e os dados do TSE não permitiram identicar 2 modelos de urna que representam menos de 0.02% dos votos, portanto serão desconsiderados na análise da distribuição dos modelos.\n",
    "\n",
    "Tabela 2 mostra o Modelo \"UE2020\" e o agrupamento dos modelos anteriores com o nome de \"Não-UE2020\"\n",
    "\n",
    "obs: Foram listados apenas os primeiros 4 melhores colocados na votação de primeiro turno, pois todos os demais candidatos tiveram menos votos que Brancos e Nulos.\n",
    "\n",
    "obs: Os códigos \"UE0000\" (BUs Sem Log da urna) e \"UE1111\" (BUs Com Log da urna, mas que não informa o modelo), serão desconsiderados nos próximos cálculos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6119c9c7-dd94-400a-8048-e59157742be7",
   "metadata": {},
   "source": [
    "#### Análise da Distribuição dos Votos no 1° Turno de Acordo com o Modelo de Urna"
   ]
  },
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   "execution_count": 5,
   "id": "fd2b2865-ca92-4236-9be3-aed3735c6d76",
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   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Na Tabela 1 vemos que todos os candidatos (incluindo Outros e Brancos/Nulos) receberam, de forma bem distribuída, votos de todos os modelos de urnas, portanto não há concentração de votos oriundos de apenas um único modelo de urna."
      ],
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       "<IPython.core.display.Markdown object>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qtd Urnas</th>\n",
       "      <th>% Votos Totais</th>\n",
       "      <th>Votos Totais</th>\n",
       "      <th>Votos Bolsonaro</th>\n",
       "      <th>Votos Lula</th>\n",
       "      <th>Votos Simone</th>\n",
       "      <th>Votos Ciro</th>\n",
       "      <th>Votos Outros</th>\n",
       "      <th>Votos BN</th>\n",
       "      <th>% Bolsonaro</th>\n",
       "      <th>% Lula</th>\n",
       "      <th>% Simone</th>\n",
       "      <th>% Ciro</th>\n",
       "      <th>% Outros</th>\n",
       "      <th>% BrancoNulo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelUrna</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UE0000</th>\n",
       "      <td>57</td>\n",
       "      <td>0.02</td>\n",
       "      <td>19,423</td>\n",
       "      <td>5,836</td>\n",
       "      <td>11,626</td>\n",
       "      <td>509</td>\n",
       "      <td>718</td>\n",
       "      <td>245</td>\n",
       "      <td>489</td>\n",
       "      <td>30.05</td>\n",
       "      <td>59.86</td>\n",
       "      <td>2.62</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE1111</th>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2,692</td>\n",
       "      <td>955</td>\n",
       "      <td>1,407</td>\n",
       "      <td>102</td>\n",
       "      <td>90</td>\n",
       "      <td>21</td>\n",
       "      <td>117</td>\n",
       "      <td>35.48</td>\n",
       "      <td>52.27</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.78</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2009</th>\n",
       "      <td>46,636</td>\n",
       "      <td>9.47</td>\n",
       "      <td>11,710,715</td>\n",
       "      <td>4,854,836</td>\n",
       "      <td>5,515,362</td>\n",
       "      <td>405,614</td>\n",
       "      <td>321,521</td>\n",
       "      <td>119,220</td>\n",
       "      <td>494,162</td>\n",
       "      <td>41.46</td>\n",
       "      <td>47.10</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.02</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2010</th>\n",
       "      <td>94,365</td>\n",
       "      <td>19.02</td>\n",
       "      <td>23,521,476</td>\n",
       "      <td>9,209,857</td>\n",
       "      <td>11,622,458</td>\n",
       "      <td>822,118</td>\n",
       "      <td>609,726</td>\n",
       "      <td>234,068</td>\n",
       "      <td>1,023,249</td>\n",
       "      <td>39.16</td>\n",
       "      <td>49.41</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2011</th>\n",
       "      <td>27,536</td>\n",
       "      <td>5.51</td>\n",
       "      <td>6,811,846</td>\n",
       "      <td>2,656,740</td>\n",
       "      <td>3,346,511</td>\n",
       "      <td>246,282</td>\n",
       "      <td>187,638</td>\n",
       "      <td>68,434</td>\n",
       "      <td>306,241</td>\n",
       "      <td>39.00</td>\n",
       "      <td>49.13</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2013</th>\n",
       "      <td>24,986</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6,185,168</td>\n",
       "      <td>2,577,434</td>\n",
       "      <td>2,920,183</td>\n",
       "      <td>214,174</td>\n",
       "      <td>154,499</td>\n",
       "      <td>58,488</td>\n",
       "      <td>260,390</td>\n",
       "      <td>41.67</td>\n",
       "      <td>47.21</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.95</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2015</th>\n",
       "      <td>85,794</td>\n",
       "      <td>17.67</td>\n",
       "      <td>21,860,566</td>\n",
       "      <td>8,940,299</td>\n",
       "      <td>10,321,716</td>\n",
       "      <td>833,392</td>\n",
       "      <td>571,836</td>\n",
       "      <td>225,575</td>\n",
       "      <td>967,748</td>\n",
       "      <td>40.90</td>\n",
       "      <td>47.22</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2.62</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2020</th>\n",
       "      <td>192,640</td>\n",
       "      <td>43.31</td>\n",
       "      <td>53,570,481</td>\n",
       "      <td>22,826,388</td>\n",
       "      <td>23,520,241</td>\n",
       "      <td>2,393,232</td>\n",
       "      <td>1,753,259</td>\n",
       "      <td>677,109</td>\n",
       "      <td>2,400,252</td>\n",
       "      <td>42.61</td>\n",
       "      <td>43.91</td>\n",
       "      <td>4.47</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.26</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>472,024</td>\n",
       "      <td>100.00</td>\n",
       "      <td>123,682,367</td>\n",
       "      <td>51,072,345</td>\n",
       "      <td>57,259,504</td>\n",
       "      <td>4,915,423</td>\n",
       "      <td>3,599,287</td>\n",
       "      <td>1,383,160</td>\n",
       "      <td>5,452,648</td>\n",
       "      <td>41.29</td>\n",
       "      <td>46.30</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          qtd Urnas  % Votos Totais Votos Totais Votos Bolsonaro  Votos Lula  \\\n",
       "modelUrna                                                                      \n",
       "UE0000           57            0.02       19,423           5,836      11,626   \n",
       "UE1111           10            0.00        2,692             955       1,407   \n",
       "UE2009       46,636            9.47   11,710,715       4,854,836   5,515,362   \n",
       "UE2010       94,365           19.02   23,521,476       9,209,857  11,622,458   \n",
       "UE2011       27,536            5.51    6,811,846       2,656,740   3,346,511   \n",
       "UE2013       24,986            5.00    6,185,168       2,577,434   2,920,183   \n",
       "UE2015       85,794           17.67   21,860,566       8,940,299  10,321,716   \n",
       "UE2020      192,640           43.31   53,570,481      22,826,388  23,520,241   \n",
       "TOTAL       472,024          100.00  123,682,367      51,072,345  57,259,504   \n",
       "\n",
       "          Votos Simone Votos Ciro Votos Outros   Votos BN  % Bolsonaro  \\\n",
       "modelUrna                                                                \n",
       "UE0000             509        718          245        489        30.05   \n",
       "UE1111             102         90           21        117        35.48   \n",
       "UE2009         405,614    321,521      119,220    494,162        41.46   \n",
       "UE2010         822,118    609,726      234,068  1,023,249        39.16   \n",
       "UE2011         246,282    187,638       68,434    306,241        39.00   \n",
       "UE2013         214,174    154,499       58,488    260,390        41.67   \n",
       "UE2015         833,392    571,836      225,575    967,748        40.90   \n",
       "UE2020       2,393,232  1,753,259      677,109  2,400,252        42.61   \n",
       "TOTAL        4,915,423  3,599,287    1,383,160  5,452,648        41.29   \n",
       "\n",
       "           % Lula  % Simone  % Ciro  % Outros  % BrancoNulo  \n",
       "modelUrna                                                    \n",
       "UE0000      59.86      2.62    3.70      1.26          2.52  \n",
       "UE1111      52.27      3.79    3.34      0.78          4.35  \n",
       "UE2009      47.10      3.46    2.75      1.02          4.22  \n",
       "UE2010      49.41      3.50    2.59      1.00          4.35  \n",
       "UE2011      49.13      3.62    2.75      1.00          4.50  \n",
       "UE2013      47.21      3.46    2.50      0.95          4.21  \n",
       "UE2015      47.22      3.81    2.62      1.03          4.43  \n",
       "UE2020      43.91      4.47    3.27      1.26          4.48  \n",
       "TOTAL       46.30      3.97    2.91      1.12          4.41  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "Na Tabela 1 também percebemos que, por alguma razão, **com a exceção do candidato Lula**, todos os demais candidatos (incluindo Outros e Brancos/Nulos) **reberam menos votos dos modelos de urna Anteriores ao modelo \"UE2020\"**."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>qtd Urnas</th>\n",
       "      <th>% Votos Totais</th>\n",
       "      <th>Votos Totais</th>\n",
       "      <th>Votos Bolsonaro</th>\n",
       "      <th>Votos Lula</th>\n",
       "      <th>Votos Simone</th>\n",
       "      <th>Votos Ciro</th>\n",
       "      <th>Votos Outros</th>\n",
       "      <th>Votos BN</th>\n",
       "      <th>% Bolsonaro</th>\n",
       "      <th>% Lula</th>\n",
       "      <th>% Simone</th>\n",
       "      <th>% Ciro</th>\n",
       "      <th>% Outros</th>\n",
       "      <th>% BrancoNulo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelUrna</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>NÃO-UE2020</th>\n",
       "      <td>279,384</td>\n",
       "      <td>56.69</td>\n",
       "      <td>70,111,886</td>\n",
       "      <td>28,245,957</td>\n",
       "      <td>33,739,263</td>\n",
       "      <td>2,522,191</td>\n",
       "      <td>1,846,028</td>\n",
       "      <td>706,051</td>\n",
       "      <td>3,052,396</td>\n",
       "      <td>40.29</td>\n",
       "      <td>48.12</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.01</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2020</th>\n",
       "      <td>192,640</td>\n",
       "      <td>43.31</td>\n",
       "      <td>53,570,481</td>\n",
       "      <td>22,826,388</td>\n",
       "      <td>23,520,241</td>\n",
       "      <td>2,393,232</td>\n",
       "      <td>1,753,259</td>\n",
       "      <td>677,109</td>\n",
       "      <td>2,400,252</td>\n",
       "      <td>42.61</td>\n",
       "      <td>43.91</td>\n",
       "      <td>4.47</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.26</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>472,024</td>\n",
       "      <td>100.00</td>\n",
       "      <td>123,682,367</td>\n",
       "      <td>51,072,345</td>\n",
       "      <td>57,259,504</td>\n",
       "      <td>4,915,423</td>\n",
       "      <td>3,599,287</td>\n",
       "      <td>1,383,160</td>\n",
       "      <td>5,452,648</td>\n",
       "      <td>41.29</td>\n",
       "      <td>46.30</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIFERENÇA %</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",
       "      <td></td>\n",
       "      <td>-5.44</td>\n",
       "      <td>9.59</td>\n",
       "      <td>-19.46</td>\n",
       "      <td>-19.57</td>\n",
       "      <td>-19.84</td>\n",
       "      <td>-2.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            qtd Urnas % Votos Totais Votos Totais Votos Bolsonaro  Votos Lula  \\\n",
       "modelUrna                                                                       \n",
       "NÃO-UE2020    279,384          56.69   70,111,886      28,245,957  33,739,263   \n",
       "UE2020        192,640          43.31   53,570,481      22,826,388  23,520,241   \n",
       "TOTAL         472,024         100.00  123,682,367      51,072,345  57,259,504   \n",
       "DIFERENÇA %                                                                     \n",
       "\n",
       "            Votos Simone Votos Ciro Votos Outros   Votos BN  % Bolsonaro  \\\n",
       "modelUrna                                                                  \n",
       "NÃO-UE2020     2,522,191  1,846,028      706,051  3,052,396        40.29   \n",
       "UE2020         2,393,232  1,753,259      677,109  2,400,252        42.61   \n",
       "TOTAL          4,915,423  3,599,287    1,383,160  5,452,648        41.29   \n",
       "DIFERENÇA %                                                        -5.44   \n",
       "\n",
       "             % Lula  % Simone  % Ciro  % Outros  % BrancoNulo  \n",
       "modelUrna                                                      \n",
       "NÃO-UE2020    48.12      3.60    2.63      1.01          4.35  \n",
       "UE2020        43.91      4.47    3.27      1.26          4.48  \n",
       "TOTAL         46.30      3.97    2.91      1.12          4.41  \n",
       "DIFERENÇA %    9.59    -19.46  -19.57    -19.84         -2.90  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "Na Tabela 2, todas as urnas anteriores ao modelo \"UE2020\" foram agrupadas em um grupo chamado \"NÃO-UE2020\" que recebeu quase 57% dos votos nas Eleições 2022. Os dados mostram que **com a exceção do candidato Lula**, todos os demais receberam menos votos nesse **modelo antigo de urna**. A Tabela 2 também apresenta a Diferença Percentual de votos recebidas no modelo \"NÃO-UE2020\", que foi quase **10% favorável para o candidato Lula**, e desfavorável em -5.5% para Bolsonaro, sendo que os demais canditados, Simone, Ciro e Outros, tudos obtiveram praticamente o mesmo valor de -20% dos votos. Os votos Brancos/Nulos também foram -2.90 menores nessas urnas."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Ou seja, **os modelos antigos de urnas \"NÃO-UE2020\" (de 2009 até 2015, época do governo PT), apresentaram uma vantagem de 19.45% para o candidato Lula em Relação ao canditado Bolsonaro. Enquanto os modelos atuais e auditados \"UE-2020\" apresentaram uma vantagem 3% do Lula contra Bolsonaro**."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tabela_modelos_urna, tabela_grupos_urna = modelos_pais(turno=1)\n",
    "\n",
    "printmd('Na Tabela 1 vemos que todos os candidatos (incluindo Outros e Brancos/Nulos) receberam, de forma bem distribuída, votos de todos os modelos de urnas, portanto não há concentração de votos oriundos de apenas um único modelo de urna.')\n",
    "\n",
    "tabela_modelos_urna\n",
    "\n",
    "printmd('Na Tabela 1 também percebemos que, por alguma razão, **com a exceção do candidato Lula**, todos os demais candidatos (incluindo Outros e Brancos/Nulos) **reberam menos votos dos modelos de urna Anteriores ao modelo \"UE2020\"**.')\n",
    "\n",
    "tabela_grupos_urna\n",
    "\n",
    "printmd('Na Tabela 2, todas as urnas anteriores ao modelo \"UE2020\" foram agrupadas em um grupo chamado \"NÃO-UE2020\" que recebeu quase 57% dos votos nas Eleições 2022. Os dados mostram que **com a exceção do candidato Lula**, todos os demais receberam menos votos nesse **modelo antigo de urna**. A Tabela 2 também apresenta a Diferença Percentual de votos recebidas no modelo \"NÃO-UE2020\", que foi quase **10% favorável para o candidato Lula**, e desfavorável em -5.5% para Bolsonaro, sendo que os demais canditados, Simone, Ciro e Outros, tudos obtiveram praticamente o mesmo valor de -20% dos votos. Os votos Brancos/Nulos também foram -2.90 menores nessas urnas.')\n",
    "\n",
    "printmd('Ou seja, **os modelos antigos de urnas \"NÃO-UE2020\" (de 2009 até 2015, época do governo PT), apresentaram uma vantagem de 19.45% para o candidato Lula em Relação ao canditado Bolsonaro. Enquanto os modelos atuais e auditados \"UE-2020\" apresentaram uma vantagem 3% do Lula contra Bolsonaro**.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2945de5-dca3-4aab-b8bc-bdf8f62aeb5c",
   "metadata": {},
   "source": [
    "#### Análise da Distribuição dos Votos no 2° Turno de Acordo com o Modelo de Urna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b345361d-9d85-40a6-8d26-a68c3471eecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Na Tabela 1 continua presente a relação de maior percentual de votos para o candidato Lula nas urnas antigas \"NÃO-UE2020\"."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>qtd Urnas</th>\n",
       "      <th>% Votos Totais</th>\n",
       "      <th>Votos Totais</th>\n",
       "      <th>Votos Bolsonaro</th>\n",
       "      <th>Votos Lula</th>\n",
       "      <th>Votos BN</th>\n",
       "      <th>% Bolsonaro</th>\n",
       "      <th>% Lula</th>\n",
       "      <th>% BrancoNulo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelUrna</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>UE0000</th>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,071</td>\n",
       "      <td>221</td>\n",
       "      <td>836</td>\n",
       "      <td>14</td>\n",
       "      <td>20.63</td>\n",
       "      <td>78.06</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2009</th>\n",
       "      <td>48,245</td>\n",
       "      <td>9.82</td>\n",
       "      <td>12,203,589</td>\n",
       "      <td>5,722,673</td>\n",
       "      <td>5,963,741</td>\n",
       "      <td>517,175</td>\n",
       "      <td>46.89</td>\n",
       "      <td>48.87</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2010</th>\n",
       "      <td>93,790</td>\n",
       "      <td>18.92</td>\n",
       "      <td>23,508,155</td>\n",
       "      <td>10,379,046</td>\n",
       "      <td>12,069,548</td>\n",
       "      <td>1,059,561</td>\n",
       "      <td>44.15</td>\n",
       "      <td>51.34</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2011</th>\n",
       "      <td>26,889</td>\n",
       "      <td>5.38</td>\n",
       "      <td>6,679,909</td>\n",
       "      <td>2,943,287</td>\n",
       "      <td>3,432,852</td>\n",
       "      <td>303,770</td>\n",
       "      <td>44.06</td>\n",
       "      <td>51.39</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2013</th>\n",
       "      <td>24,672</td>\n",
       "      <td>4.93</td>\n",
       "      <td>6,128,515</td>\n",
       "      <td>2,857,302</td>\n",
       "      <td>3,001,068</td>\n",
       "      <td>270,145</td>\n",
       "      <td>46.62</td>\n",
       "      <td>48.97</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2015</th>\n",
       "      <td>85,735</td>\n",
       "      <td>17.61</td>\n",
       "      <td>21,880,318</td>\n",
       "      <td>10,114,104</td>\n",
       "      <td>10,766,404</td>\n",
       "      <td>999,810</td>\n",
       "      <td>46.22</td>\n",
       "      <td>49.21</td>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2020</th>\n",
       "      <td>192,691</td>\n",
       "      <td>43.34</td>\n",
       "      <td>53,851,239</td>\n",
       "      <td>26,189,721</td>\n",
       "      <td>25,111,550</td>\n",
       "      <td>2,549,968</td>\n",
       "      <td>48.63</td>\n",
       "      <td>46.63</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>472,027</td>\n",
       "      <td>100.00</td>\n",
       "      <td>124,252,796</td>\n",
       "      <td>58,206,354</td>\n",
       "      <td>60,345,999</td>\n",
       "      <td>5,700,443</td>\n",
       "      <td>46.85</td>\n",
       "      <td>48.57</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          qtd Urnas  % Votos Totais Votos Totais Votos Bolsonaro  Votos Lula  \\\n",
       "modelUrna                                                                      \n",
       "UE0000            5            0.00        1,071             221         836   \n",
       "UE2009       48,245            9.82   12,203,589       5,722,673   5,963,741   \n",
       "UE2010       93,790           18.92   23,508,155      10,379,046  12,069,548   \n",
       "UE2011       26,889            5.38    6,679,909       2,943,287   3,432,852   \n",
       "UE2013       24,672            4.93    6,128,515       2,857,302   3,001,068   \n",
       "UE2015       85,735           17.61   21,880,318      10,114,104  10,766,404   \n",
       "UE2020      192,691           43.34   53,851,239      26,189,721  25,111,550   \n",
       "TOTAL       472,027          100.00  124,252,796      58,206,354  60,345,999   \n",
       "\n",
       "            Votos BN  % Bolsonaro  % Lula  % BrancoNulo  \n",
       "modelUrna                                                \n",
       "UE0000            14        20.63   78.06          1.31  \n",
       "UE2009       517,175        46.89   48.87          4.24  \n",
       "UE2010     1,059,561        44.15   51.34          4.51  \n",
       "UE2011       303,770        44.06   51.39          4.55  \n",
       "UE2013       270,145        46.62   48.97          4.41  \n",
       "UE2015       999,810        46.22   49.21          4.57  \n",
       "UE2020     2,549,968        48.63   46.63          4.74  \n",
       "TOTAL      5,700,443        46.85   48.57          4.59  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "Na Tabela 2, as urnas antigas \"NÃO-UE2020\" mostram uma vantagem de 50% dos votos para Lula e 45.5% dos votos para Bolsonaro, enquanto as urnas novas,  \"UE2020\" mostram um resultado de 48.63% para Bolsonaro e 46.63% para Lula, ou seja, 2 pontos percentuais correspondendo a 1.078.171 votos."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>qtd Urnas</th>\n",
       "      <th>% Votos Totais</th>\n",
       "      <th>Votos Totais</th>\n",
       "      <th>Votos Bolsonaro</th>\n",
       "      <th>Votos Lula</th>\n",
       "      <th>Votos BN</th>\n",
       "      <th>% Bolsonaro</th>\n",
       "      <th>% Lula</th>\n",
       "      <th>% BrancoNulo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelUrna</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>NÃO-UE2020</th>\n",
       "      <td>279,336</td>\n",
       "      <td>56.66</td>\n",
       "      <td>70,401,557</td>\n",
       "      <td>32,016,633</td>\n",
       "      <td>35,234,449</td>\n",
       "      <td>3,150,475</td>\n",
       "      <td>45.48</td>\n",
       "      <td>50.05</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2020</th>\n",
       "      <td>192,691</td>\n",
       "      <td>43.34</td>\n",
       "      <td>53,851,239</td>\n",
       "      <td>26,189,721</td>\n",
       "      <td>25,111,550</td>\n",
       "      <td>2,549,968</td>\n",
       "      <td>48.63</td>\n",
       "      <td>46.63</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>472,027</td>\n",
       "      <td>100.00</td>\n",
       "      <td>124,252,796</td>\n",
       "      <td>58,206,354</td>\n",
       "      <td>60,345,999</td>\n",
       "      <td>5,700,443</td>\n",
       "      <td>46.85</td>\n",
       "      <td>48.57</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIFERENÇA %</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>-6.48</td>\n",
       "      <td>7.33</td>\n",
       "      <td>-5.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            qtd Urnas % Votos Totais Votos Totais Votos Bolsonaro  Votos Lula  \\\n",
       "modelUrna                                                                       \n",
       "NÃO-UE2020    279,336          56.66   70,401,557      32,016,633  35,234,449   \n",
       "UE2020        192,691          43.34   53,851,239      26,189,721  25,111,550   \n",
       "TOTAL         472,027         100.00  124,252,796      58,206,354  60,345,999   \n",
       "DIFERENÇA %                                                                     \n",
       "\n",
       "              Votos BN  % Bolsonaro  % Lula  % BrancoNulo  \n",
       "modelUrna                                                  \n",
       "NÃO-UE2020   3,150,475        45.48   50.05          4.48  \n",
       "UE2020       2,549,968        48.63   46.63          4.74  \n",
       "TOTAL        5,700,443        46.85   48.57          4.59  \n",
       "DIFERENÇA %                   -6.48    7.33         -5.49  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "O grupo de urnas antigas \"NÃO-UE-2020\", apresentaram uma vantagem de 10.05% para o candidato Lula em Relação ao canditado Bolsonaro. Enquanto os modelos atuais e auditados \"UE-2020\" apresentaram uma vantagem 4.29% para o candidato Bolsonaro em reação ao Lula**."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tabela_modelos_urna, tabela_grupos_urna = modelos_pais(turno=2)\n",
    "\n",
    "printmd('Na Tabela 1 continua presente a relação de maior percentual de votos para o candidato Lula nas urnas antigas \"NÃO-UE2020\".')\n",
    "\n",
    "tabela_modelos_urna\n",
    "\n",
    "printmd('Na Tabela 2, as urnas antigas \"NÃO-UE2020\" mostram uma vantagem de 50% dos votos para Lula e 45.5% dos votos para Bolsonaro, enquanto as urnas novas,  \"UE2020\" mostram um resultado de 48.63% para Bolsonaro e 46.63% para Lula, ou seja, 2 pontos percentuais correspondendo a 1.078.171 votos.')\n",
    "\n",
    "tabela_grupos_urna\n",
    "\n",
    "printmd('O grupo de urnas antigas \"NÃO-UE-2020\", apresentaram uma vantagem de 10.05% para o candidato Lula em Relação ao canditado Bolsonaro. Enquanto os modelos atuais e auditados \"UE-2020\" apresentaram uma vantagem 4.29% para o candidato Bolsonaro em reação ao Lula**.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e4448fb-f94b-40f9-856f-8fb35f0352a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "bd = bd.drop(bd[bd['modelUrna'] == 'UE0000'].index) # remove as urnas sem identificação de modelo (57 no 1o turno e 5 no 2o turno)\n",
    "bd = bd.drop(bd[bd['modelUrna'] == 'UE1111'].index) # remove as urnas sem identificação de modelo (10 no 1o turno e 0 no 2o turno)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534f4ccb-139f-4e9e-8840-e64c3ef8b979",
   "metadata": {},
   "source": [
    "### Analisando a distribuição de cada modelo de urna pelas Regiões do Brasil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "02d976e3-a94e-4cbc-a648-6b882d738fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "A primeira parte da tabela abaixo mostra a quantidade de cada modelo de urna por Região, em seguida a coluna \"TOTAL\" mostra o total de urnas usadas na região e a segunda parte da tabela mostra os percentuais de cada modelo de urna em relação ao total da região."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>UE2009</th>\n",
       "      <th>UE2010</th>\n",
       "      <th>UE2011</th>\n",
       "      <th>UE2013</th>\n",
       "      <th>UE2015</th>\n",
       "      <th>UE2020</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>UE2009</th>\n",
       "      <th>UE2010</th>\n",
       "      <th>UE2011</th>\n",
       "      <th>UE2013</th>\n",
       "      <th>UE2015</th>\n",
       "      <th>NÃO-UE2020</th>\n",
       "      <th>UE2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiao</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>CO</th>\n",
       "      <td>3966</td>\n",
       "      <td>5933</td>\n",
       "      <td>1401</td>\n",
       "      <td>2097</td>\n",
       "      <td>6019</td>\n",
       "      <td>16516</td>\n",
       "      <td>35932</td>\n",
       "      <td>11.04</td>\n",
       "      <td>16.51</td>\n",
       "      <td>3.90</td>\n",
       "      <td>5.84</td>\n",
       "      <td>16.75</td>\n",
       "      <td>54.04</td>\n",
       "      <td>45.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EX</th>\n",
       "      <td>833</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>1017</td>\n",
       "      <td>81.91</td>\n",
       "      <td>6.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.31</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>3668</td>\n",
       "      <td>7305</td>\n",
       "      <td>2330</td>\n",
       "      <td>1986</td>\n",
       "      <td>6765</td>\n",
       "      <td>16916</td>\n",
       "      <td>38970</td>\n",
       "      <td>9.41</td>\n",
       "      <td>18.75</td>\n",
       "      <td>5.98</td>\n",
       "      <td>5.10</td>\n",
       "      <td>17.36</td>\n",
       "      <td>56.60</td>\n",
       "      <td>43.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>13746</td>\n",
       "      <td>27236</td>\n",
       "      <td>8041</td>\n",
       "      <td>7418</td>\n",
       "      <td>21740</td>\n",
       "      <td>54397</td>\n",
       "      <td>132578</td>\n",
       "      <td>10.37</td>\n",
       "      <td>20.54</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.60</td>\n",
       "      <td>16.40</td>\n",
       "      <td>58.98</td>\n",
       "      <td>41.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>5974</td>\n",
       "      <td>14373</td>\n",
       "      <td>3459</td>\n",
       "      <td>5163</td>\n",
       "      <td>12301</td>\n",
       "      <td>27894</td>\n",
       "      <td>69164</td>\n",
       "      <td>8.64</td>\n",
       "      <td>20.78</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.46</td>\n",
       "      <td>17.79</td>\n",
       "      <td>59.67</td>\n",
       "      <td>40.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>20058</td>\n",
       "      <td>38874</td>\n",
       "      <td>11658</td>\n",
       "      <td>8008</td>\n",
       "      <td>38795</td>\n",
       "      <td>76968</td>\n",
       "      <td>194361</td>\n",
       "      <td>10.32</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.12</td>\n",
       "      <td>19.96</td>\n",
       "      <td>60.40</td>\n",
       "      <td>39.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UE2009  UE2010  UE2011  UE2013  UE2015  UE2020   TOTAL  UE2009  \\\n",
       "regiao                                                                   \n",
       "CO        3966    5933    1401    2097    6019   16516   35932   11.04   \n",
       "EX         833      69       0       0     115       0    1017   81.91   \n",
       "N         3668    7305    2330    1986    6765   16916   38970    9.41   \n",
       "NE       13746   27236    8041    7418   21740   54397  132578   10.37   \n",
       "S         5974   14373    3459    5163   12301   27894   69164    8.64   \n",
       "SE       20058   38874   11658    8008   38795   76968  194361   10.32   \n",
       "\n",
       "        UE2010  UE2011  UE2013  UE2015  NÃO-UE2020  UE2020  \n",
       "regiao                                                      \n",
       "CO       16.51    3.90    5.84   16.75       54.04   45.96  \n",
       "EX        6.78    0.00    0.00   11.31      100.00    0.00  \n",
       "N        18.75    5.98    5.10   17.36       56.60   43.41  \n",
       "NE       20.54    6.07    5.60   16.40       58.98   41.03  \n",
       "S        20.78    5.00    7.46   17.79       59.67   40.33  \n",
       "SE       20.00    6.00    4.12   19.96       60.40   39.60  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "A princípio percebe-se que todos os modelos de urna tem a mesma Distribuição Percentual em todas regiões, com exceção da região \"EX\" (exterior), onde a maior parte é do modelo UE2009 e todas são \"NÃO-UE2020\". Como formam um pequeno número de urnas, vamos temporariamente remover essa região (e seu \"estado\" \"ZZ\") e calcular a média de cada modelo usado no país."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# para cada região do país, conta o número de urnas\n",
    "bdt = bd[bd['turno'] == 2]\n",
    "modelos_urna = bdt.groupby(['regiao', 'modelUrna'])['modelUrna'].count().reset_index(name='qtd')\n",
    "modelos_urna = modelos_urna.pivot(index='regiao', columns='modelUrna', values='qtd').fillna(0).astype(int) # reorganiza tabela\n",
    "\n",
    "total_urnas = modelos_urna.sum(axis=1) # calcula o total de urnas para cada região\n",
    "modelos_urna_pct = modelos_urna.div(total_urnas, axis=0).multiply(100).round(2)\n",
    "modelos_urna['TOTAL'] = modelos_urna.sum(axis=1)\n",
    "\n",
    "modelos_urna_pct_UE2020 = modelos_urna_pct.pop('UE2020') # temporatiamente remove o modelo \"UE2020\"\n",
    "modelos_urna_pct['NÃO-UE2020'] = modelos_urna_pct.iloc[:, 0:5].sum(axis=1) # cria coluna \"NÃO-UE2020\"\n",
    "modelos_urna_pct = pd.concat([modelos_urna_pct, modelos_urna_pct_UE2020], axis=1) # reinsere o modelo \"UE2020\"\n",
    "\n",
    "tabela_modelos_regiao = pd.concat([modelos_urna, modelos_urna_pct], axis=1)\n",
    "\n",
    "printmd('A primeira parte da tabela abaixo mostra a quantidade de cada modelo de urna por Região, em seguida a coluna \"TOTAL\" mostra o total de urnas usadas na região e a segunda parte da tabela mostra os percentuais de cada modelo de urna em relação ao total da região.')\n",
    "\n",
    "tabela_modelos_regiao\n",
    "\n",
    "printmd('A princípio percebe-se que todos os modelos de urna tem a mesma Distribuição Percentual em todas regiões, com exceção da região \"EX\" (exterior), onde a maior parte é do modelo UE2009 e todas são \"NÃO-UE2020\". Como formam um pequeno número de urnas, vamos temporariamente remover essa região (e seu \"estado\" \"ZZ\") e calcular a média de cada modelo usado no país.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cec730ef-c836-44f1-b554-0d7f1d4c2a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>UE2009</th>\n",
       "      <th>UE2010</th>\n",
       "      <th>UE2011</th>\n",
       "      <th>UE2013</th>\n",
       "      <th>UE2015</th>\n",
       "      <th>NÃO-UE2020</th>\n",
       "      <th>UE2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiao</th>\n",
       "      <th></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>CO</th>\n",
       "      <td>11.04</td>\n",
       "      <td>16.51</td>\n",
       "      <td>3.90</td>\n",
       "      <td>5.84</td>\n",
       "      <td>16.75</td>\n",
       "      <td>54.04</td>\n",
       "      <td>45.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>9.41</td>\n",
       "      <td>18.75</td>\n",
       "      <td>5.98</td>\n",
       "      <td>5.10</td>\n",
       "      <td>17.36</td>\n",
       "      <td>56.60</td>\n",
       "      <td>43.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>10.37</td>\n",
       "      <td>20.54</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.60</td>\n",
       "      <td>16.40</td>\n",
       "      <td>58.98</td>\n",
       "      <td>41.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>8.64</td>\n",
       "      <td>20.78</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.46</td>\n",
       "      <td>17.79</td>\n",
       "      <td>59.67</td>\n",
       "      <td>40.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>10.32</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4.12</td>\n",
       "      <td>19.96</td>\n",
       "      <td>60.40</td>\n",
       "      <td>39.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÉDIA</th>\n",
       "      <td>9.96</td>\n",
       "      <td>19.32</td>\n",
       "      <td>5.39</td>\n",
       "      <td>5.62</td>\n",
       "      <td>17.65</td>\n",
       "      <td>57.94</td>\n",
       "      <td>42.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DESVIO</th>\n",
       "      <td>0.94</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UE2009  UE2010  UE2011  UE2013  UE2015  NÃO-UE2020  UE2020\n",
       "regiao                                                            \n",
       "CO       11.04   16.51    3.90    5.84   16.75       54.04   45.96\n",
       "N         9.41   18.75    5.98    5.10   17.36       56.60   43.41\n",
       "NE       10.37   20.54    6.07    5.60   16.40       58.98   41.03\n",
       "S         8.64   20.78    5.00    7.46   17.79       59.67   40.33\n",
       "SE       10.32   20.00    6.00    4.12   19.96       60.40   39.60\n",
       "MÉDIA     9.96   19.32    5.39    5.62   17.65       57.94   42.07\n",
       "DESVIO    0.94    1.75    0.94    1.22    1.40        2.60    2.60"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "O cálculo da **média** e **desvio padrão** mostram que **todas as regiões do país tiveram a mesma dispersão, ou distribuição percentual dos modelos de urnas**, a variação entre as regiões é insignificante e despresível."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelos_urna_pct.drop('EX', inplace=True, errors='ignore') # desconsidere os modelos usados no Exterior (são poucos e concentrados)\n",
    "tabela_modelos_regiao_media = modelos_urna_pct.copy() # cria uma cópia para inserir média e desvio padrão\n",
    "tabela_modelos_regiao_media.loc['MÉDIA'] = modelos_urna_pct.mean(axis=0, numeric_only=True, skipna=True).round(2) # usa os dados originais\n",
    "tabela_modelos_regiao_media.loc['DESVIO'] = modelos_urna_pct.std(axis=0, numeric_only=True, skipna=True).round(2) # usa os dados originais\n",
    "tabela_modelos_regiao_media\n",
    "\n",
    "printmd('O cálculo da **média** e **desvio padrão** mostram que **todas as regiões do país tiveram a mesma dispersão, ou distribuição percentual dos modelos de urnas**, a variação entre as regiões é insignificante e despresível.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5328b6e2-8d8d-4c89-9cb6-24ed304d4bed",
   "metadata": {},
   "source": [
    "### Analisando a distribuição de cada modelo de urna pelos Estados do Brasil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "566d7bca-8483-422a-a13d-5267fddac0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UE2009</th>\n",
       "      <th>UE2010</th>\n",
       "      <th>UE2011</th>\n",
       "      <th>UE2013</th>\n",
       "      <th>UE2015</th>\n",
       "      <th>NÃO-UE2020</th>\n",
       "      <th>UE2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estado</th>\n",
       "      <th></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>AC</th>\n",
       "      <td>12.71</td>\n",
       "      <td>17.04</td>\n",
       "      <td>4.05</td>\n",
       "      <td>8.47</td>\n",
       "      <td>14.92</td>\n",
       "      <td>57.19</td>\n",
       "      <td>42.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>19.23</td>\n",
       "      <td>20.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.74</td>\n",
       "      <td>16.37</td>\n",
       "      <td>64.06</td>\n",
       "      <td>35.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>5.46</td>\n",
       "      <td>18.74</td>\n",
       "      <td>7.69</td>\n",
       "      <td>5.37</td>\n",
       "      <td>20.17</td>\n",
       "      <td>57.43</td>\n",
       "      <td>42.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>16.44</td>\n",
       "      <td>17.24</td>\n",
       "      <td>5.69</td>\n",
       "      <td>7.53</td>\n",
       "      <td>11.55</td>\n",
       "      <td>58.45</td>\n",
       "      <td>41.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>12.64</td>\n",
       "      <td>21.06</td>\n",
       "      <td>6.39</td>\n",
       "      <td>6.02</td>\n",
       "      <td>15.99</td>\n",
       "      <td>62.10</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CE</th>\n",
       "      <td>15.33</td>\n",
       "      <td>18.26</td>\n",
       "      <td>6.58</td>\n",
       "      <td>2.86</td>\n",
       "      <td>15.01</td>\n",
       "      <td>58.04</td>\n",
       "      <td>41.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DF</th>\n",
       "      <td>0.00</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.25</td>\n",
       "      <td>24.97</td>\n",
       "      <td>41.73</td>\n",
       "      <td>58.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>18.00</td>\n",
       "      <td>18.26</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.94</td>\n",
       "      <td>17.66</td>\n",
       "      <td>63.86</td>\n",
       "      <td>36.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO</th>\n",
       "      <td>11.91</td>\n",
       "      <td>20.85</td>\n",
       "      <td>3.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>15.40</td>\n",
       "      <td>57.52</td>\n",
       "      <td>42.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>10.37</td>\n",
       "      <td>19.08</td>\n",
       "      <td>8.43</td>\n",
       "      <td>4.43</td>\n",
       "      <td>16.20</td>\n",
       "      <td>58.51</td>\n",
       "      <td>41.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <td>11.87</td>\n",
       "      <td>22.43</td>\n",
       "      <td>6.35</td>\n",
       "      <td>4.08</td>\n",
       "      <td>15.92</td>\n",
       "      <td>60.65</td>\n",
       "      <td>39.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <td>11.62</td>\n",
       "      <td>22.22</td>\n",
       "      <td>2.46</td>\n",
       "      <td>5.83</td>\n",
       "      <td>13.70</td>\n",
       "      <td>55.83</td>\n",
       "      <td>44.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>18.58</td>\n",
       "      <td>10.99</td>\n",
       "      <td>8.99</td>\n",
       "      <td>3.19</td>\n",
       "      <td>14.83</td>\n",
       "      <td>56.58</td>\n",
       "      <td>43.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>6.53</td>\n",
       "      <td>21.23</td>\n",
       "      <td>6.93</td>\n",
       "      <td>4.61</td>\n",
       "      <td>17.98</td>\n",
       "      <td>57.28</td>\n",
       "      <td>42.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PB</th>\n",
       "      <td>0.15</td>\n",
       "      <td>19.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.19</td>\n",
       "      <td>19.27</td>\n",
       "      <td>52.90</td>\n",
       "      <td>47.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE</th>\n",
       "      <td>1.96</td>\n",
       "      <td>26.01</td>\n",
       "      <td>7.13</td>\n",
       "      <td>5.74</td>\n",
       "      <td>18.80</td>\n",
       "      <td>59.64</td>\n",
       "      <td>40.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PI</th>\n",
       "      <td>10.53</td>\n",
       "      <td>19.63</td>\n",
       "      <td>8.60</td>\n",
       "      <td>3.67</td>\n",
       "      <td>15.83</td>\n",
       "      <td>58.26</td>\n",
       "      <td>41.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <td>15.14</td>\n",
       "      <td>18.92</td>\n",
       "      <td>1.98</td>\n",
       "      <td>7.00</td>\n",
       "      <td>16.44</td>\n",
       "      <td>59.48</td>\n",
       "      <td>40.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ</th>\n",
       "      <td>10.26</td>\n",
       "      <td>18.47</td>\n",
       "      <td>2.42</td>\n",
       "      <td>5.20</td>\n",
       "      <td>21.85</td>\n",
       "      <td>58.20</td>\n",
       "      <td>41.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <td>10.79</td>\n",
       "      <td>18.88</td>\n",
       "      <td>5.28</td>\n",
       "      <td>2.58</td>\n",
       "      <td>14.71</td>\n",
       "      <td>52.24</td>\n",
       "      <td>47.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>26.27</td>\n",
       "      <td>11.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.53</td>\n",
       "      <td>15.17</td>\n",
       "      <td>60.62</td>\n",
       "      <td>39.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>18.45</td>\n",
       "      <td>18.69</td>\n",
       "      <td>81.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>5.19</td>\n",
       "      <td>22.59</td>\n",
       "      <td>6.98</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16.54</td>\n",
       "      <td>60.32</td>\n",
       "      <td>39.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>4.11</td>\n",
       "      <td>20.69</td>\n",
       "      <td>6.47</td>\n",
       "      <td>5.60</td>\n",
       "      <td>22.00</td>\n",
       "      <td>58.87</td>\n",
       "      <td>41.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>13.37</td>\n",
       "      <td>16.50</td>\n",
       "      <td>5.75</td>\n",
       "      <td>6.91</td>\n",
       "      <td>14.61</td>\n",
       "      <td>57.14</td>\n",
       "      <td>42.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>8.87</td>\n",
       "      <td>19.47</td>\n",
       "      <td>7.30</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.53</td>\n",
       "      <td>60.69</td>\n",
       "      <td>39.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO</th>\n",
       "      <td>10.39</td>\n",
       "      <td>22.42</td>\n",
       "      <td>7.78</td>\n",
       "      <td>2.93</td>\n",
       "      <td>15.06</td>\n",
       "      <td>58.58</td>\n",
       "      <td>41.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÉDIA</th>\n",
       "      <td>10.66</td>\n",
       "      <td>18.15</td>\n",
       "      <td>4.81</td>\n",
       "      <td>5.78</td>\n",
       "      <td>17.07</td>\n",
       "      <td>56.48</td>\n",
       "      <td>43.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DESVIO</th>\n",
       "      <td>6.39</td>\n",
       "      <td>5.27</td>\n",
       "      <td>3.01</td>\n",
       "      <td>2.74</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UE2009  UE2010  UE2011  UE2013  UE2015  NÃO-UE2020  UE2020\n",
       "estado                                                            \n",
       "AC       12.71   17.04    4.05    8.47   14.92       57.19   42.80\n",
       "AL       19.23   20.72    0.00    7.74   16.37       64.06   35.93\n",
       "AM        5.46   18.74    7.69    5.37   20.17       57.43   42.57\n",
       "AP       16.44   17.24    5.69    7.53   11.55       58.45   41.55\n",
       "BA       12.64   21.06    6.39    6.02   15.99       62.10   37.91\n",
       "CE       15.33   18.26    6.58    2.86   15.01       58.04   41.96\n",
       "DF        0.00    7.51    0.00    9.25   24.97       41.73   58.27\n",
       "ES       18.00   18.26    3.00    6.94   17.66       63.86   36.14\n",
       "GO       11.91   20.85    3.71    5.65   15.40       57.52   42.47\n",
       "MA       10.37   19.08    8.43    4.43   16.20       58.51   41.49\n",
       "MG       11.87   22.43    6.35    4.08   15.92       60.65   39.35\n",
       "MS       11.62   22.22    2.46    5.83   13.70       55.83   44.17\n",
       "MT       18.58   10.99    8.99    3.19   14.83       56.58   43.41\n",
       "PA        6.53   21.23    6.93    4.61   17.98       57.28   42.72\n",
       "PB        0.15   19.29    0.00   14.19   19.27       52.90   47.10\n",
       "PE        1.96   26.01    7.13    5.74   18.80       59.64   40.35\n",
       "PI       10.53   19.63    8.60    3.67   15.83       58.26   41.74\n",
       "PR       15.14   18.92    1.98    7.00   16.44       59.48   40.52\n",
       "RJ       10.26   18.47    2.42    5.20   21.85       58.20   41.79\n",
       "RN       10.79   18.88    5.28    2.58   14.71       52.24   47.76\n",
       "RO       26.27   11.65    0.00    7.53   15.17       60.62   39.38\n",
       "RR        0.00    0.00    0.00    0.24   18.45       18.69   81.31\n",
       "RS        5.19   22.59    6.98    9.02   16.54       60.32   39.68\n",
       "SC        4.11   20.69    6.47    5.60   22.00       58.87   41.13\n",
       "SE       13.37   16.50    5.75    6.91   14.61       57.14   42.87\n",
       "SP        8.87   19.47    7.30    3.52   21.53       60.69   39.30\n",
       "TO       10.39   22.42    7.78    2.93   15.06       58.58   41.42\n",
       "MÉDIA    10.66   18.15    4.81    5.78   17.07       56.48   43.52\n",
       "DESVIO    6.39    5.27    3.01    2.74    3.00        8.63    8.63"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       "A distribuição dos modeos de urnas nos estados é geralmente próxima da média, porém se adotarmos um **grupamento** de modelos, usando como base as analises feitas no País e nas Regiões, podemos agrupar os modelos de urnas em 2 grupos: **\"UE2020\"** e **\"Não-UE2020\"**."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Analisando a distribuição do grupos **\"UE2020\"** e **\"Não-UE2020\"** constatamos que na maoria dos estados tem uma distribuição percentual próxima a média nacional. Existem variações significativas em RR e DF, porém são estados com pequeno eleitorado o que não influenciam significativamente a região, e mesmo que influenciasse,m a princípio, conforme observado, reduziriam a enorme diferença de votos entros os 2 grupos de modelos analisados."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# para cada região do país, conta o número de urnas\n",
    "bdt = bd[bd['turno'] == 2]\n",
    "modelos_urna = bdt.groupby(['estado', 'modelUrna'])['modelUrna'].count().reset_index(name='qtd')\n",
    "modelos_urna = modelos_urna.pivot(index='estado', columns='modelUrna', values='qtd').fillna(0).astype(int) # reorganiza tabela\n",
    "\n",
    "total_urnas = modelos_urna.sum(axis=1) # calcula o total de urnas para cada região\n",
    "modelos_urna_pct = modelos_urna.div(total_urnas, axis=0).multiply(100).round(2)\n",
    "modelos_urna_pct.drop('ZZ', inplace=True, errors='ignore')\n",
    "\n",
    "modelos_urna_pct_UE2020 = modelos_urna_pct.pop('UE2020') # temporatiamente remove o modelo \"UE2020\"\n",
    "modelos_urna_pct['NÃO-UE2020'] = modelos_urna_pct.iloc[:, 0:5].sum(axis=1) # cria coluna \"NÃO-UE2020\"\n",
    "modelos_urna_pct = pd.concat([modelos_urna_pct, modelos_urna_pct_UE2020], axis=1) # reinsere o modelo \"UE2020\"\n",
    "\n",
    "tabela_modelos_estado_media = modelos_urna_pct.copy()\n",
    "tabela_modelos_estado_media.loc['MÉDIA'] = modelos_urna_pct.mean(axis=0, numeric_only=True, skipna=True).round(2)\n",
    "tabela_modelos_estado_media.loc['DESVIO'] = modelos_urna_pct.std(axis=0, numeric_only=True, skipna=True).round(2)\n",
    "tabela_modelos_estado_media\n",
    "\n",
    "printmd('A distribuição dos modeos de urnas nos estados é geralmente próxima da média, porém se adotarmos um **grupamento** de modelos, usando como base as analises feitas no País e nas Regiões, podemos agrupar os modelos de urnas em 2 grupos: **\"UE2020\"** e **\"Não-UE2020\"**.')\n",
    "\n",
    "printmd('Analisando a distribuição do grupos **\"UE2020\"** e **\"Não-UE2020\"** constatamos que na maoria dos estados tem uma distribuição percentual próxima a média nacional. Existem variações significativas em RR e DF, porém são estados com pequeno eleitorado o que não influenciam significativamente a região, e mesmo que influenciasse,m a princípio, conforme observado, reduziriam a enorme diferença de votos entros os 2 grupos de modelos analisados.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd902fd1-5bf0-4213-96f7-9b890f0cd382",
   "metadata": {},
   "source": [
    "**Apesar de simplificarmos a análise, dispensando a aplicação de modelos ou testes estatísticos mais elaborados, é claramente perceptível a não existência de significativa variação na distribuição dos modelos de urnas nas Regiões e Estados, portanto, independente do número de eleitores podemos afirmar que nenhuma Região ou Estado influenciou no fato dos modelos \"Não-UE2020\" apresentarem um percentual maior de votos para o candidato Lula do que para o candidato Bolsonaro.** A distribuição dos modelos das urnas apresentou a mesma proporção tanto no primeiro quanto no segundo turno (visto que foram usadas as mesmas urnas, com mínimas substituições).\n",
    "\n",
    "Uma possível linha de investigação mais profunda pode ser feita pela comparação de municípios que possuem quantidade similar de votos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ee627f85-2023-4f2b-8aee-2170ce1ea7e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Frequency'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff4d0e1e7a0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/markdown": [
       " ###O gráfico de distribuição do número de cidades por **eleitores aptos a votarem**, mostra um maior número de cidades com 23 mil votantes (segundo o TSE o número de eleitores habilitados é de 156.451.836 e o número da população brasileira é de 215.350.000, ou seja, 72.65% está apto para as eleições 2022, portanto um município com a população eleitoral de 23 mil eleitores aptos, representa aproximadamente 32 mil habitantes, portanto analisando cidades entre 10 mil e 30 mil comparecimentos, que representariam cidades de 20 a 50 mil habitantes aproximadamente."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0200f05955af47728bbc10e953f6073e",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bdt = bd[bd['turno'] == 2]\n",
    "municipios = bdt.groupby('municipio')['qEleitAptos'].sum().reset_index(name='EleitAptos')\n",
    "\n",
    "municipios = municipios[municipios['EleitAptos'] <= 25000]\n",
    "# 5709\n",
    "municipios.plot.hist(column='EleitAptos', bins=100)\n",
    "plt.axvline(municipios['EleitAptos'].mean(), color='r', linestyle='dashed', linewidth=1)\n",
    "printmd(f' ###O gráfico de distribuição do número de cidades por **eleitores aptos a votarem**, mostra um maior número de cidades com 23 mil votantes (segundo o TSE o número de eleitores habilitados é de 156.451.836 e o número da população brasileira é de 215.350.000, ou seja, 72.65% está apto para as eleições 2022, portanto um município com a população eleitoral de 23 mil eleitores aptos, representa aproximadamente 32 mil habitantes, portanto analisando cidades entre 10 mil e 30 mil comparecimentos, que representariam cidades de 20 a 50 mil habitantes aproximadamente.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d3a70504-9b0d-4bb7-802c-957430ca7203",
   "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>modelUrna</th>\n",
       "      <th>vPresA22</th>\n",
       "      <th>vPresB13</th>\n",
       "      <th>qComparecimento</th>\n",
       "      <th>Perc Bolsonaro</th>\n",
       "      <th>Perc Lula</th>\n",
       "      <th>Perc Votos Favor Lula</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelUrna</th>\n",
       "      <th></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>UE2009</th>\n",
       "      <td>24267</td>\n",
       "      <td>2325020</td>\n",
       "      <td>3003380</td>\n",
       "      <td>5701420</td>\n",
       "      <td>40.78</td>\n",
       "      <td>52.68</td>\n",
       "      <td>29.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2010</th>\n",
       "      <td>53918</td>\n",
       "      <td>4580867</td>\n",
       "      <td>7035773</td>\n",
       "      <td>12451215</td>\n",
       "      <td>36.79</td>\n",
       "      <td>56.51</td>\n",
       "      <td>53.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2011</th>\n",
       "      <td>17148</td>\n",
       "      <td>1465851</td>\n",
       "      <td>2178151</td>\n",
       "      <td>3919468</td>\n",
       "      <td>37.40</td>\n",
       "      <td>55.57</td>\n",
       "      <td>48.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2013</th>\n",
       "      <td>16659</td>\n",
       "      <td>1413262</td>\n",
       "      <td>2146483</td>\n",
       "      <td>3827906</td>\n",
       "      <td>36.92</td>\n",
       "      <td>56.07</td>\n",
       "      <td>51.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2015</th>\n",
       "      <td>42696</td>\n",
       "      <td>3841170</td>\n",
       "      <td>5389004</td>\n",
       "      <td>9893933</td>\n",
       "      <td>38.82</td>\n",
       "      <td>54.47</td>\n",
       "      <td>40.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UE2020</th>\n",
       "      <td>31804</td>\n",
       "      <td>2812548</td>\n",
       "      <td>4068807</td>\n",
       "      <td>7423497</td>\n",
       "      <td>37.89</td>\n",
       "      <td>54.81</td>\n",
       "      <td>44.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           modelUrna  vPresA22  vPresB13  qComparecimento  Perc Bolsonaro  \\\n",
       "modelUrna                                                                   \n",
       "UE2009         24267   2325020   3003380          5701420           40.78   \n",
       "UE2010         53918   4580867   7035773         12451215           36.79   \n",
       "UE2011         17148   1465851   2178151          3919468           37.40   \n",
       "UE2013         16659   1413262   2146483          3827906           36.92   \n",
       "UE2015         42696   3841170   5389004          9893933           38.82   \n",
       "UE2020         31804   2812548   4068807          7423497           37.89   \n",
       "\n",
       "           Perc Lula  Perc Votos Favor Lula  \n",
       "modelUrna                                    \n",
       "UE2009         52.68                  29.18  \n",
       "UE2010         56.51                  53.60  \n",
       "UE2011         55.57                  48.58  \n",
       "UE2013         56.07                  51.87  \n",
       "UE2015         54.47                  40.31  \n",
       "UE2020         54.81                  44.66  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "municipios = bd.groupby('municipio')['qComparecimento'].sum()\n",
    "municipio_comparecimento = municipios[(municipios > 0) & (municipios < 25000)]\n",
    "#bd[bd['municipio'].isin(municipio_comparecimento)].groupby('modelUrna')['qComparecimento'].sum().reset_index(name='qtd')\n",
    "\n",
    "municipio_modelos_urna = bd[bd['municipio'].isin(municipio_comparecimento.index)].groupby('modelUrna')['modelUrna'].count()\n",
    "municipio_total_votos = bd[bd['municipio'].isin(municipio_comparecimento.index)].groupby('modelUrna')[['vPresA22', 'vPresB13', 'qComparecimento']].sum()\n",
    "\n",
    "#calcula o percentual de votos de cada presidente de acoardo com o model de urna\n",
    "municipio_total_votos['Perc Bolsonaro'] = municipio_total_votos['vPresA22'].div(municipio_total_votos['qComparecimento']).multiply(100).round(2)\n",
    "municipio_total_votos['Perc Lula'] = municipio_total_votos['vPresB13'].div(municipio_total_votos['qComparecimento']).multiply(100).round(2)\n",
    "\n",
    "# calcula os votos a favor do candidato Lula\n",
    "municipio_total_votos['Perc Votos Favor Lula'] = municipio_total_votos['Perc Lula'].div(municipio_total_votos['Perc Bolsonaro']).sub(1).multiply(100).round(2)\n",
    "\n",
    "tabela_municipio_modelos_urna = pd.concat([municipio_modelos_urna, municipio_total_votos], axis=1)\n",
    "# modelos_urna.rename(columns={'modelUrna': 'Qtd Urnas', 'vPresTotal': 'Perc Total Votos'}, inplace=True) # renomeia as colunas\n",
    "tabela_municipio_modelos_urna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be42237b-ebca-4e70-b325-6ab5b55f5968",
   "metadata": {},
   "source": [
    "Constata-se que a distribuição dos modelos de urnas nessas cidades ainda é similar aos planos Regional e Estadual.\n",
    "\n",
    "Mesmo em cidades pequenas/médias, onde existe menos heterogeneidade e geralmente a maioria dos habitantes do município compartilham de visões políticas muito semelhantes devido ao meio comum que se encontram, **é um fato nítido que as urnas do grupo \"UE2020\" continuam apresentando o menor percentual favorável ao candidato Lula quando comparado com as urnas do grupo \"Não-UE2020\", onde o percentual de votos a favor do candidato Lula varia entre 32% e 54%, muito acima do grupo \"UE2020\" que foi de 17.5%**.\n",
    "\n",
    "Portando indaga-se às autoridades eleitorais, **como pode em todos as localidades comparadas, Região, Estado, Cidades (pequenas/médias de ideologia política mais homogênea) TODAS as Urnas Eletrônicas do Modelo \"Não-UE2020\" tem um percentual Bastante SUPERIOR de VANTAGEM de VOTOS ao candidato Lula, nesse caso chegando a até 3.1 vezes maior que as urnas de model \"UE2020\"?**\n",
    "\n",
    "Ora, espera-se que uma moeda lançada milhares de vezes dê em média, aproximadamente 50% de Cara ou Coroa, mas se esta moeda, após milhões de lançamentos der um mínimo percentual de 0.1% de diferença (49.95% x 50.05%) já é considerada viciada, então o que dizer de um grupo de Urnas, que nos mesmos grupos analisados, sempre favorecem muito mais um canditado que o outro?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4266194f",
   "metadata": {},
   "source": [
    "## Estatísticas da Votação e Estados com Segundo Turno para Governador"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd87cf4-404b-490c-be78-01866237afdf",
   "metadata": {},
   "source": [
    "(em desenvolvimento)"
   ]
  }
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