eleicao_2022.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "temporal-spank",
   "metadata": {},
   "source": [
    "## Eleições 2022\n",
    "\n",
    "#### Notebook para analisar rapidamente os dados disponibilizados pelo TSE das eleições de 2022\n",
    "\n",
    "Portal de Dados Abertos do TSE: https://dadosabertos.tse.jus.br/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "d8066b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rodar para deixar as células da largura da janela do navegador\n",
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "9d7a9374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "sep = ';' # separador do arquivo CSV\n",
    "dec = ',' # separador de decimais\n",
    "enc = 'latin1' # codificação dos caracteres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9781ed32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# arquivos CSV\n",
    "f_vot_secao = './tse_2022/votacao_secao_2022_BR/votacao_secao_2022_BR.csv'\n",
    "f_hist_pres_1t = './tse_2022/Historico_Totalizacao_Presidente_BR_1T_2022/Historico_Totalizacao_Presidente_BR_1T_2022.csv'\n",
    "f_hist_pres_2t = './tse_2022/Historico_Totalizacao_Presidente_BR_2T_2022/Historico_Totalizacao_Presidente_BR_2T_2022.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1403db49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "municipal-oregon",
   "metadata": {},
   "source": [
    "### Primeiro turno - Totalização "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2f1e99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DT_TOTALIZACAO     ', 'QT_SECOES_TOTAL', 'QT_APTOS_TOTAL',\n",
       "       'QT_SECOES_TOT', 'QT_SECOES_TOT_ACUMULADO',\n",
       "       'PE_SECOES_TOT_ACUMULADO                      ', 'QT_APTOS_TOT',\n",
       "       'QT_APTOS_TOT_ACUMULADO', 'QT_VOTOS_TOTAL', 'QT_VOTOS_TOTAL_ACUMULADO',\n",
       "       'QT_VOTOS_CONCORRENTES', 'QT_VOTOS_CONCORRENTES_ACUMULADO',\n",
       "       'CIRO_GOMES_QT_VOTOS_TOT', 'CIRO_GOMES_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'CIRO_GOMES_PE_VOTOS_TOT_ACUMULADO            ', 'LULA_QT_VOTOS_TOT',\n",
       "       'LULA_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'LULA_PE_VOTOS_TOT_ACUMULADO                  ',\n",
       "       'PADRE_KELMON_QT_VOTOS_TOT', 'PADRE_KELMON_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'PADRE_KELMON_PE_VOTOS_TOT_ACUMULADO          ',\n",
       "       'SIMONE_TEBET_QT_VOTOS_TOT', 'SIMONE_TEBET_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'SIMONE_TEBET_PE_VOTOS_TOT_ACUMULADO          ', 'VERA_QT_VOTOS_TOT',\n",
       "       'VERA_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'VERA_PE_VOTOS_TOT_ACUMULADO                  ',\n",
       "       'SOFIA_MANZANO_QT_VOTOS_TOT', 'SOFIA_MANZANO_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'SOFIA_MANZANO_PE_VOTOS_TOT_ACUMULADO         ',\n",
       "       'JAIR_BOLSONARO_QT_VOTOS_TOT', 'JAIR_BOLSONARO_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'JAIR_BOLSONARO_PE_VOTOS_TOT_ACUMULADO        ',\n",
       "       'CONSTITUINTE_EYMAEL_QT_VOTOS_TOT',\n",
       "       'CONSTITUINTE_EYMAEL_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'CONSTITUINTE_EYMAEL_PE_VOTOS_TOT_ACUMULADO   ',\n",
       "       'FELIPE_DAVILA_QT_VOTOS_TOT', 'FELIPE_DAVILA_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'FELIPE_DAVILA_PE_VOTOS_TOT_ACUMULADO         ',\n",
       "       'SORAYA_THRONICKE_QT_VOTOS_TOT',\n",
       "       'SORAYA_THRONICKE_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'SORAYA_THRONICKE_PE_VOTOS_TOT_ACUMULADO      ',\n",
       "       'LEO_PERICLES_QT_VOTOS_TOT', 'LEO_PERICLES_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'LEO_PERICLES_PE_VOTOS_TOT_ACUMULADO          ', 'BRANCO_QT_VOTOS_TOT',\n",
       "       'BRANCO_QT_VOTOS_TOT_ACUMULADO',\n",
       "       'BRANCO_PE_VOTOS_TOT_ACUMULADO                ', 'NULO_QT_VOTOS_TOT',\n",
       "       'NULO_QT_VOTOS_TOT_ACUMULADO', 'NULO_PE_VOTOS_TOT_ACUMULADO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe totalização primeiro turno\n",
    "df_tot_1t = pd.read_csv(f_hist_pres_1t, sep=sep, encoding=enc, decimal=dec)\n",
    "df_tot_1t.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7e69dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tot_1t['data_hora'] = pd.to_datetime(df_tot_1t['DT_TOTALIZACAO     '], format='%d/%m/%Y %H:%M:%S') #\n",
    "df_tot_1t.set_index('data_hora', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "closing-punch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-10-02 17:04:47')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot_1t.index.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dried-australia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-10-04 10:27:34')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot_1t.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "greek-response",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1 days 17:22:47')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot_1t.index.max() - df_tot_1t.index.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "rising-bottle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_tot_1t\n",
    "y1 = 'JAIR_BOLSONARO_PE_VOTOS_TOT_ACUMULADO        '\n",
    "y2 = 'LULA_PE_VOTOS_TOT_ACUMULADO                  '\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.plot(df.index, df[y1]*100, 'o', label='Bolsonaro')\n",
    "ax.plot(df.index, df[y2]*100, label='Lula')\n",
    "\n",
    "datefmt = mdates.DateFormatter(\"%H:%M\")\n",
    "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))\n",
    "ax.xaxis.set_major_formatter(datefmt)\n",
    "ax.xaxis.set_tick_params(labelrotation=90)\n",
    "\n",
    "ax.set_title('Eleições 2022 - Primeiro Turno - Porcentagem de votos válidos')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "expired-collect",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_tot_1t\n",
    "y1 = 'JAIR_BOLSONARO_PE_VOTOS_TOT_ACUMULADO        '\n",
    "y2 = 'LULA_PE_VOTOS_TOT_ACUMULADO                  '\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.plot(df.index, df[y1]*100, label='Bolsonaro')\n",
    "ax.plot(df.index, df[y2]*100, label='Lula')\n",
    "\n",
    "ax.set_xlim([pd.to_datetime('2022-10-02 17:00:00'), pd.to_datetime('2022-10-03 00:10:00')]);\n",
    "datefmt = mdates.DateFormatter(\"%H:%M\")\n",
    "ax.xaxis.set_major_locator(mdates.MinuteLocator(interval=10))\n",
    "ax.xaxis.set_major_formatter(datefmt)\n",
    "ax.xaxis.set_tick_params(labelrotation=90)\n",
    "\n",
    "ax.set_title('Eleições 2022 - Primeiro Turno - Porcentagem de votos válidos')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alpine-multiple",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "crazy-binary",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fancy-intention",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "german-oliver",
   "metadata": {},
   "source": [
    "### Segundo turno - Totalização "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cardiovascular-departure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 07:17:13')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe totalização primeiro turno\n",
    "df_tot_2t = pd.read_csv(f_hist_pres_2t, sep=sep, encoding=enc, decimal=dec)\n",
    "df_tot_2t['data_hora'] = pd.to_datetime(df_tot_2t['DT_TOTALIZACAO     '], format='%d/%m/%Y %H:%M:%S') #\n",
    "df_tot_2t.set_index('data_hora', inplace=True)\n",
    "df_tot_2t.index.max() - df_tot_2t.index.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "chief-restriction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_tot_2t\n",
    "y1 = 'JAIR_BOLSONARO_PE_VOTOS_TOT_ACUMULADO        '\n",
    "y2 = 'LULA_PE_VOTOS_TOT_ACUMULADO                  '\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax.plot(df.index, df[y1]*100,label='Bolsonaro')\n",
    "ax.plot(df.index, df[y2]*100, label='Lula')\n",
    "\n",
    "# ax.set_xlim([pd.to_datetime('2022-10-02 17:00:00'), pd.to_datetime('2022-10-03 00:10:00')]);\n",
    "datefmt = mdates.DateFormatter(\"%H:%M\")\n",
    "ax.xaxis.set_major_locator(mdates.MinuteLocator(interval=10))\n",
    "ax.xaxis.set_major_formatter(datefmt)\n",
    "ax.xaxis.set_tick_params(labelrotation=90)\n",
    "\n",
    "ax.set_title('Eleições 2022 - Primeiro Turno - Porcentagem de votos válidos')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rotary-destruction",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5752ffdb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "compact-mistake",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "atomic-librarian",
   "metadata": {},
   "source": [
    "### Votos por Seção - Primeiro Turno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "practical-still",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DT_GERACAO', 'HH_GERACAO', 'ANO_ELEICAO', 'CD_TIPO_ELEICAO',\n",
       "       'NM_TIPO_ELEICAO', 'NR_TURNO', 'CD_ELEICAO', 'DS_ELEICAO', 'DT_ELEICAO',\n",
       "       'TP_ABRANGENCIA', 'SG_UF', 'SG_UE', 'NM_UE', 'CD_MUNICIPIO',\n",
       "       'NM_MUNICIPIO', 'NR_ZONA', 'NR_SECAO', 'CD_CARGO', 'DS_CARGO',\n",
       "       'NR_VOTAVEL', 'NM_VOTAVEL', 'QT_VOTOS', 'NR_LOCAL_VOTACAO',\n",
       "       'SQ_CANDIDATO', 'NM_LOCAL_VOTACAO', 'DS_LOCAL_VOTACAO_ENDERECO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe \n",
    "f_vot_secao = './tse_2022/votacao_secao_2022_BR/votacao_secao_2022_BR.csv'\n",
    "df_secao = pd.read_csv(f_vot_secao, sep=sep, encoding=enc, decimal=dec)\n",
    "df_secao.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "israeli-hazard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5380736"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_secao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "active-beast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LUIZ INÁCIO LULA DA SILVA', 'JOSE MARIA EYMAEL',\n",
       "       'LUIZ FELIPE CHAVES D AVILA', 'KELMON LUIS DA SILVA SOUZA',\n",
       "       'VOTO BRANCO', 'SORAYA VIEIRA THRONICKE', 'CIRO FERREIRA GOMES',\n",
       "       'SOFIA PADUA MANZANO', 'JAIR MESSIAS BOLSONARO',\n",
       "       'SIMONE NASSAR TEBET', 'LEONARDO PÉRICLES VIEIRA ROQUE',\n",
       "       'VOTO NULO', 'VERA LUCIA PEREIRA DA SILVA SALGADO'], dtype=object)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_secao['NM_VOTAVEL'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "chubby-hometown",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3529844"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# primeiro turno\n",
    "df_secao_1t = df_secao.query('NR_TURNO == 1')\n",
    "len(df_secao_1t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "another-reliance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LUIZ INÁCIO LULA DA SILVA', 'JOSE MARIA EYMAEL',\n",
       "       'LUIZ FELIPE CHAVES D AVILA', 'KELMON LUIS DA SILVA SOUZA',\n",
       "       'VOTO BRANCO', 'SORAYA VIEIRA THRONICKE', 'CIRO FERREIRA GOMES',\n",
       "       'SOFIA PADUA MANZANO', 'JAIR MESSIAS BOLSONARO',\n",
       "       'SIMONE NASSAR TEBET', 'LEONARDO PÉRICLES VIEIRA ROQUE',\n",
       "       'VOTO NULO', 'VERA LUCIA PEREIRA DA SILVA SALGADO'], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_secao_1t['NM_VOTAVEL'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "greatest-tobago",
   "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>QT_VOTOS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM_VOTAVEL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LUIZ INÁCIO LULA DA SILVA</th>\n",
       "      <td>57259504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAIR MESSIAS BOLSONARO</th>\n",
       "      <td>51072345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMONE NASSAR TEBET</th>\n",
       "      <td>4915423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIRO FERREIRA GOMES</th>\n",
       "      <td>3599287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOTO NULO</th>\n",
       "      <td>3487874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOTO BRANCO</th>\n",
       "      <td>1964779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SORAYA VIEIRA THRONICKE</th>\n",
       "      <td>600955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUIZ FELIPE CHAVES D AVILA</th>\n",
       "      <td>559708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KELMON LUIS DA SILVA SOUZA</th>\n",
       "      <td>81129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEONARDO PÉRICLES VIEIRA ROQUE</th>\n",
       "      <td>53519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOFIA PADUA MANZANO</th>\n",
       "      <td>45620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VERA LUCIA PEREIRA DA SILVA SALGADO</th>\n",
       "      <td>25625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOSE MARIA EYMAEL</th>\n",
       "      <td>16604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     QT_VOTOS\n",
       "NM_VOTAVEL                                   \n",
       "LUIZ INÁCIO LULA DA SILVA            57259504\n",
       "JAIR MESSIAS BOLSONARO               51072345\n",
       "SIMONE NASSAR TEBET                   4915423\n",
       "CIRO FERREIRA GOMES                   3599287\n",
       "VOTO NULO                             3487874\n",
       "VOTO BRANCO                           1964779\n",
       "SORAYA VIEIRA THRONICKE                600955\n",
       "LUIZ FELIPE CHAVES D AVILA             559708\n",
       "KELMON LUIS DA SILVA SOUZA              81129\n",
       "LEONARDO PÉRICLES VIEIRA ROQUE          53519\n",
       "SOFIA PADUA MANZANO                     45620\n",
       "VERA LUCIA PEREIRA DA SILVA SALGADO     25625\n",
       "JOSE MARIA EYMAEL                       16604"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total de votos - primeiro turno\n",
    "df_secao_1t[['NM_VOTAVEL','QT_VOTOS']].groupby(['NM_VOTAVEL']).sum().sort_values(by='QT_VOTOS',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "parallel-debate",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "martial-promise",
   "metadata": {},
   "source": [
    "### Votos por Seção - Segundo Turno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "medieval-japanese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1850892"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# segundo turno\n",
    "df_secao_2t = df_secao.query('NR_TURNO == 2')\n",
    "len(df_secao_2t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7ba2afad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LUIZ INÁCIO LULA DA SILVA', 'JAIR MESSIAS BOLSONARO', 'VOTO NULO',\n",
       "       'VOTO BRANCO'], dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_secao_2t['NM_VOTAVEL'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2bdd6171",
   "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>QT_VOTOS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM_VOTAVEL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LUIZ INÁCIO LULA DA SILVA</th>\n",
       "      <td>60345999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAIR MESSIAS BOLSONARO</th>\n",
       "      <td>58206354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOTO NULO</th>\n",
       "      <td>3930765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOTO BRANCO</th>\n",
       "      <td>1769678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           QT_VOTOS\n",
       "NM_VOTAVEL                         \n",
       "LUIZ INÁCIO LULA DA SILVA  60345999\n",
       "JAIR MESSIAS BOLSONARO     58206354\n",
       "VOTO NULO                   3930765\n",
       "VOTO BRANCO                 1769678"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total de votos - primeiro turno\n",
    "df_secao_2t[['NM_VOTAVEL','QT_VOTOS']].groupby(['NM_VOTAVEL']).sum().sort_values(by='QT_VOTOS',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bigger-diagram",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "previous-speech",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "portuguese-syracuse",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fifteen-sherman",
   "metadata": {},
   "source": [
    "### Votos por Seção - estatística descritiva - Primeiro Turno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "strong-horizontal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    471863.000000\n",
       "mean        108.235537\n",
       "std          48.747558\n",
       "min           1.000000\n",
       "25%          73.000000\n",
       "50%         110.000000\n",
       "75%         143.000000\n",
       "max         535.000000\n",
       "Name: QT_VOTOS, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# JMB\n",
    "df_secao_1t_jmb = df_secao.query('NR_TURNO==1 and NR_VOTAVEL==22')\n",
    "df_secao_1t_jmb['QT_VOTOS'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "metric-scale",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    472023.000000\n",
       "mean        121.306597\n",
       "std          47.155582\n",
       "min           1.000000\n",
       "25%          86.000000\n",
       "50%         116.000000\n",
       "75%         152.000000\n",
       "max         513.000000\n",
       "Name: QT_VOTOS, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LILS\n",
    "df_secao_1t_lils = df_secao.query('NR_TURNO==1 and NR_VOTAVEL==13')\n",
    "df_secao_1t_lils['QT_VOTOS'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "continued-respondent",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEvCAYAAABojibwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVoUlEQVR4nO3dfayk1X0f8O+vEDtuXgqYDUK89DrNShWNGuKsMFb8B7EVvEBVXMmyTKt6ZaFspYDkSJGapZWKazfp+o/GCVGKSusVWEpMXCUWyEtKtsRR1D/ALDHhxYSycddiV5jFBuNWlqKSnv4xzyXj9b27d899mZlnPh9pNM9z5pmZc5jh3O+eOc95qrUWAADg3PytWVcAAAAWkSANAAAdBGkAAOggSAMAQAdBGgAAOgjSAADQ4fxZV6DXxRdf3FZWVmZdDYBz9sQTT3yztbZr1vXYSfpsYFGdqc9e2CC9srKSo0ePzroaAOesqr4+6zrsNH02sKjO1Geb2gEAAB0EaQAA6CBIAwBAB0EaAAA6CNIAANBBkAYAgA6CNAAAdBCkAQCggyANAAAdBGkAAOggSAMAQIfzZ10BABiLlQOH39w+fvCmGdYE2AlGpAEAoIMR6RExEgIAsHOMSAMAQAdBGgAAOgjSAADQQZAGAIAOgjQAAHQQpAEAoIMgDQAAHQRpAADo4IIsS8QFWwAAto4RaQAA6CBIAwBAB0EaAAA6CNIAANBBkAYAgA6CNAAAdBCkAQCggyANAAAdBGkAAOggSAMAQAdBGgAAOgjSAADQ4fxZVwAAlsXKgcNvbh8/eNMMawJsBSPSAADQQZAGAIAOZw3SVXVFVX2pqr5aVc9W1ceG8ouq6khVvTDcXziUV1XdVVXHquqpqnrn1GvtG45/oar2TZX/TFU9PTznrqqq7WgsAMzCyoHD3zOtAxiHjYxIv5Hkl1trVyW5NsltVXVVkgNJHmmt7U7yyLCfJDck2T3c9ie5O5kE7yR3JnlXkmuS3LkavodjfmHqeXs33zQAANg+Zw3SrbWXWmt/Nmz/7yTPJbksyc1J7hsOuy/JB4btm5N8tk08muSCqro0yfuTHGmtvdpaey3JkSR7h8d+tLX2aGutJfns1GsBAMBcOqc50lW1kuSnkzyW5JLW2kvDQ99IcsmwfVmSF6eedmIoO1P5iTXKAQBgbm04SFfVDyf5/SS/1Fr7zvRjw0hy2+K6rVWH/VV1tKqOvvLKK9v9dgBsgj4bGLsNBemq+oFMQvTvtNb+YCh+eZiWkeH+1FB+MskVU0+/fCg7U/nla5R/n9baPa21Pa21Pbt27dpI1UfFySrAIln2PhsYv7NekGVYQeMzSZ5rrf361EMPJtmX5OBw/8BU+e1VdX8mJxa+3lp7qaoeTvJrUycYXp/kjtbaq1X1naq6NpMpIx9J8ltb0DYAmFu9F2dxUReYHxu5suHPJvnnSZ6uqieHsn+VSYD+fFXdmuTrST40PPZQkhuTHEvy3SQfTZIhMH8yyePDcZ9orb06bP9iknuTvC3JHw43AACYW2cN0q21/5FkvXWd37fG8S3Jbeu81qEkh9YoP5rkJ89WFwBYFKbiwfi5siEAAHQQpAEAoIMgDQAAHTZysiEAsI2sxAGLyYg0AAB0MCLNm4yIAABsnBFpAADoIEgDwBxZOXDYGtSwIARpAADoYI70SBnNAADYXkakAQCggyANAAAdBGkAAOggSAMAQAdBGgAAOli1Y8FZnQNgnFxtFuafEWkAAOggSAMAQAdBGgAAOgjSAADQQZAGAIAOVu1YUs4GB9gaVk+C5WVEGgAAOgjSAADQQZAGAIAOgjQAAHRwsiEAzDknNMJ8EqQXkA4VAGD2TO0AAIAOgjQAAHQQpAEAoIMgDQAAHZxsiJMXAQA6GJEGAIAOgjQAAHQQpAEAoIM50gDQwfklgBFpAADoYER6TkyPbBw/eNMMawIAwEYYkQYAgA6CNAAAdBCkAQCggyANAAtq5cBhq4fADAnSAADQwaodc85IAwDAfDIiDQAAHQRpAADoIEgDAECHswbpqjpUVaeq6pmpso9X1cmqenK43Tj12B1Vdayqnq+q90+V7x3KjlXVganyd1TVY0P571XVW7aygQAAsB02MiJ9b5K9a5R/urV29XB7KEmq6qokH07yD4bn/MeqOq+qzkvy20luSHJVkluGY5PkU8Nr/USS15LcupkGAQDATjjrqh2ttT+tqpUNvt7NSe5vrf1Vkv9VVceSXDM8dqy19rUkqar7k9xcVc8leW+Sfzocc1+Sjye5e8MtAIAlN73C0/GDN82wJrBcNjNH+vaqemqY+nHhUHZZkhenjjkxlK1X/vYk326tvXFaOQAAzLXeIH13kr+X5OokLyX5D1tVoTOpqv1VdbSqjr7yyis78ZZLa/VqWdaxBnrps4Gx67ogS2vt5dXtqvrPSb447J5McsXUoZcPZVmn/FtJLqiq84dR6enj13rfe5LckyR79uxpPXVnc/x8CGyUPhsYu64R6aq6dGr3nyRZXdHjwSQfrqq3VtU7kuxO8uUkjyfZPazQ8ZZMTkh8sLXWknwpyQeH5+9L8kBPnQAAYCeddUS6qj6X5LokF1fViSR3Jrmuqq5O0pIcT/IvkqS19mxVfT7JV5O8keS21tpfD69ze5KHk5yX5FBr7dnhLX4lyf1V9e+SfCXJZ7aqcQAAsF02smrHLWsUrxt2W2u/muRX1yh/KMlDa5R/LX+zsgcAACyErjnSbC8n+AHMJ/0zMM0lwgEAoIMgDQAAHQRpAADoIEgDAEAHQRoAADpYtQMARsQVaGHnGJEGAIAOgjQAAHQQpAEAoIM50pyVK3kBAHw/I9IAANBBkAYAgA6mdgDASFkKD7aXIL1DdGYAAONiagcAAHQQpAEAoIMgDQAAHQRpAADoIEgDAEAHq3bMmKsGAgAsJkEaAM7AgAewHlM7AACggyANAAAdBGkAAOhgjjQALIHpud7HD940w5rAeBiRBgCADoI0AAB0EKQBAKCDOdIzYE1SAIDFJ0gDAE5GhA6mdgAAQAdBGgAAOgjSAADQQZDeIisHDjuJEICF4G8WbA1BGgAAOgjSAADQQZAGAIAOgjQAAHQQpAEAoIMgDQAAHQRpAADocP6sK8DiWl2D9PjBm2ZcEwB6WEsaNkeQBoDTCJjARpjaAQAAHYxIbyMjGgAA42VEGgAAOgjSAADQ4axBuqoOVdWpqnpmquyiqjpSVS8M9xcO5VVVd1XVsap6qqreOfWcfcPxL1TVvqnyn6mqp4fn3FVVtdWNBACArbaREel7k+w9rexAkkdaa7uTPDLsJ8kNSXYPt/1J7k4mwTvJnUneleSaJHeuhu/hmF+Yet7p7wUAAHPnrCcbttb+tKpWTiu+Ocl1w/Z9Sf4kya8M5Z9trbUkj1bVBVV16XDskdbaq0lSVUeS7K2qP0nyo621R4fyzyb5QJI/3EyjZskJhgAAy6F31Y5LWmsvDdvfSHLJsH1ZkhenjjsxlJ2p/MQa5XNlOhy7+AgAAMkWLH/XWmtV1baiMmdTVfszmTKSK6+8cifeknPkHx3AKn02MHa9q3a8PEzZyHB/aig/meSKqeMuH8rOVH75GuVraq3d01rb01rbs2vXrs6qA7AT9NnA2PWOSD+YZF+Sg8P9A1Plt1fV/ZmcWPh6a+2lqno4ya9NnWB4fZI7WmuvVtV3quraJI8l+UiS3+qsEzNiXjgAsIzOGqSr6nOZnCx4cVWdyGT1jYNJPl9Vtyb5epIPDYc/lOTGJMeSfDfJR5NkCMyfTPL4cNwnVk88TPKLmawM8rZMTjJc2BMNAQBYHhtZteOWdR563xrHtiS3rfM6h5IcWqP8aJKfPFs9AABgnriyIQAAdBCkAQCgw6aXvwOAMXDiNHCujEgDAEAHI9KbYPQCAGB5GZEGAIAORqQBgO8x/Yvr8YM3zbAmMN8E6XNkOgcAAImpHQAA0EWQBgCADoI0AAB0EKQBAKCDIA0AAB0EaQAA6CBIAwBAB0EaAAA6CNIAwLpWDhx2MTJYhyANAAAdBGkAAOggSAMAQAdBGgAAOgjSAADQQZAGAIAOgjQAAHQQpAEAoMP5s67APLMAPQAA6zEiDQAAHYxIA7DU/Pq4MdP/nY4fvGmGNYH5YUQaAAA6CNIAANDB1A62jZ8BAYAxMyINAAAdBGkAAOggSAMAQAdBGgAAOjjZEAA4J04mhwkj0gAA0MGI9Glc4Qpg/PT1wFYwIg0AAB0EaQAA6CBIAwBAB3OkY64cAADnzog0AAB0EKTZESsHDhv5BwBGRZAGAIAO5kgDAN1c5ZBlJkizo3S4AMBYmNoBAAAdNhWkq+p4VT1dVU9W1dGh7KKqOlJVLwz3Fw7lVVV3VdWxqnqqqt459Tr7huNfqKp9m2sSAABsv60Ykf651trVrbU9w/6BJI+01nYneWTYT5IbkuwebvuT3J1MgneSO5O8K8k1Se5cDd8AADCvtmNqx81J7hu270vyganyz7aJR5NcUFWXJnl/kiOttVdba68lOZJk7zbUCwAAtsxmg3RL8kdV9URV7R/KLmmtvTRsfyPJJcP2ZUlenHruiaFsvXIAAJhbm1214z2ttZNV9WNJjlTVX0w/2FprVdU2+R5vGsL6/iS58sort+plAdgG+mxg7DY1It1aOzncn0ryhUzmOL88TNnIcH9qOPxkkiumnn75ULZe+Vrvd09rbU9rbc+uXbs2U3UAtpk+Gxi77iBdVT9UVT+yup3k+iTPJHkwyerKG/uSPDBsP5jkI8PqHdcmeX2YAvJwkuur6sLhJMPrhzIAYIGsHDj8PdcLgLHbzNSOS5J8oapWX+d3W2v/raoeT/L5qro1ydeTfGg4/qEkNyY5luS7ST6aJK21V6vqk0keH477RGvt1U3UCwAAtl13kG6tfS3JT61R/q0k71ujvCW5bZ3XOpTkUG9dAABgp7myIQAAdBCkAQCggyANAAAdNruONADA91hv5Y7jB2/a4ZrA9jIiDQAAHQRpAADoIEgDAEAHQRoAADoI0gAA0EGQBgCADku7/N16S/Owc6Y/A0siAQCLxog0AAB0WNoRaQCWi18iga1mRBoAADoI0gAA0EGQBgCADoI0AAB0cLIhALAjLHvK2AjSzAWdKwCwaEztAACADoI0c2flwGHrvQIAc0+QBgCADoI0AAB0cLIhALDjnGTOGCxdkDb3FgCArWBqBwAAdFi6EWkAlodfIYHtZEQaAJgpy56yqARpAADoIEgDAEAHc6SZW5ZGAgDmmRFpAADoIEgDAEAHUzsAgLlgSh+Lxog0AAB0EKQBAKCDqR0shPUW6vfTH7AWF/dYfKZ5sAiMSAMAQAdBGgAAOpjaAQDMNdM8mFdGpAEAoIMgDQAAHUztAGAUrNSxHFY/Z1M8mAeCNAvNvDkAYFZM7QAAgA5GpBkNP/cBLA8X6mIeCNKMjukesDzMiwZmydQOAADoMDcj0lW1N8lvJjkvyX9prR2ccZUYAaPTMD5GoTkT/T47aS6CdFWdl+S3k/x8khNJHq+qB1trX51tzQCARbXWP7qEa7bSXATpJNckOdZa+1qSVNX9SW5OIkizZXSosNiMRLMVnKTIVpqXIH1Zkhen9k8kedeM6sIS2cgfZp0rzI7wzE7p/a75G7Hc5iVIb0hV7U+yf9j9P1X1/Dm+xMVJvrm1tZop7dkB9amup81lWzZBe7bW353he+8Yffa6tGuxnLFdnX8j5sFSfl6d1u2zq7W2xe917qrq3Uk+3lp7/7B/R5K01v79Fr/P0dbanq18zVnSnvk1prYk2sNsjPVz0q7Fol2LZafbNS/L3z2eZHdVvaOq3pLkw0kenHGdAABgXXMxtaO19kZV3Z7k4UyWvzvUWnt2xtUCAIB1zUWQTpLW2kNJHtrmt7lnm19/p2nP/BpTWxLtYTbG+jlp12LRrsWyo+2aiznSAACwaOZljjQAACyUpQnSVbW3qp6vqmNVdWDW9dmIqjpUVaeq6pmpsouq6khVvTDcXziUV1XdNbTvqap65+xq/v2q6oqq+lJVfbWqnq2qjw3li9qeH6yqL1fVnw/t+bdD+Tuq6rGh3r83nDybqnrrsH9seHxlpg1YQ1WdV1VfqaovDvuL3JbjVfV0VT1ZVUeHsoX8ri2rReyzV42p7542tn581Rj781Vj6tenzVMfvxRBuv7mEuQ3JLkqyS1VddVsa7Uh9ybZe1rZgSSPtNZ2J3lk2E8mbds93PYnuXuH6rhRbyT55dbaVUmuTXLb8Bksanv+Ksl7W2s/leTqJHur6tokn0ry6dbaTyR5Lcmtw/G3JnltKP/0cNy8+ViS56b2F7ktSfJzrbWrp5ZBWtTv2tJZ4D571b0ZT989bWz9+Kox9uerxtavT5uPPr61NvpbkncneXhq/44kd8y6Xhus+0qSZ6b2n09y6bB9aZLnh+3/lOSWtY6bx1uSB5L8/Bjak+RvJ/mzTK7G+c0k5w/lb37vMlmR5t3D9vnDcTXruk+14fJMOp73JvliklrUtgz1Op7k4tPKFv67tiy3Re6zp+o8yr77tDaOph+fquPC9+dTbRlVv35a2+amj1+KEemsfQnyy2ZUl826pLX20rD9jSSXDNsL08bhJ6OfTvJYFrg9w09mTyY5leRIkr9M8u3W2hvDIdN1frM9w+OvJ3n7jlb4zH4jyb9M8v+G/bdncduSJC3JH1XVEzW5ul6ywN+1JTTGz2RU37+x9OOrRtafr/qNjKtfnzY3ffzcLH/HuWuttapaqGVXquqHk/x+kl9qrX2nqt58bNHa01r76yRXV9UFSb6Q5O/PtkZ9quofJTnVWnuiqq6bcXW2yntaayer6seSHKmqv5h+cNG+a4zLon//xtSPrxpLf75qpP36tLnp45dlRPpkkium9i8fyhbRy1V1aZIM96eG8rlvY1X9QCad7++01v5gKF7Y9qxqrX07yZcy+Znsgqpa/QfqdJ3fbM/w+N9J8q2drem6fjbJP66q40nuz+RnwN/MYrYlSdJaOzncn8rkj+I1GcF3bYmM8TMZxfdvrP34qhH056tG169Pm6c+flmC9JguQf5gkn3D9r5M5qitln9kODv12iSvT/3EMXM1GbL4TJLnWmu/PvXQorZn1zBykap6WybzBJ/LpAP+4HDY6e1ZbecHk/xxGyZrzVpr7Y7W2uWttZVM/t/449baP8sCtiVJquqHqupHVreTXJ/kmSzod21JjanPXrXw37+x9eOrxtSfrxpbvz5t7vr4WU8Y36lbkhuT/M9M5j3961nXZ4N1/lySl5L830zm9NyayZylR5K8kOS/J7loOLYyOcv9L5M8nWTPrOt/Wlvek8mcpqeSPDncblzg9vzDJF8Z2vNMkn8zlP94ki8nOZbkvyZ561D+g8P+seHxH591G9Zp13VJvrjIbRnq/efD7dnV/98X9bu2rLdF7LOn6j6avvu0do2qH59q1yj786n2LXy/flp75qqPd2VDAADosCxTOwAAYEsJ0gAA0EGQBgCADoI0AAB0EKQBAKCDIA0AAB0EaQAA6CBIAwBAh/8P3j+pfzrGfu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(12,5))\n",
    "axs[0].hist(df_secao_1t_jmb['QT_VOTOS'], bins=100);\n",
    "axs[1].hist(df_secao_1t_lils['QT_VOTOS'], bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yellow-football",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "sonic-coupon",
   "metadata": {},
   "source": [
    "### Votos por Seção - estatística descritiva - Segundo Turno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ordinary-enlargement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    471884.000000\n",
       "mean        123.348861\n",
       "std          54.608497\n",
       "min           1.000000\n",
       "25%          84.000000\n",
       "50%         127.000000\n",
       "75%         162.000000\n",
       "max         549.000000\n",
       "Name: QT_VOTOS, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# JMB\n",
    "df_secao_2t_jmb = df_secao.query('NR_TURNO==2 and NR_VOTAVEL==22')\n",
    "df_secao_2t_jmb['QT_VOTOS'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "assigned-dining",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    472023.000000\n",
       "mean        127.845463\n",
       "std          48.249222\n",
       "min           1.000000\n",
       "25%          92.000000\n",
       "50%         123.000000\n",
       "75%         160.000000\n",
       "max         530.000000\n",
       "Name: QT_VOTOS, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LILS\n",
    "df_secao_2t_lils = df_secao.query('NR_TURNO==2 and NR_VOTAVEL==13')\n",
    "df_secao_2t_lils['QT_VOTOS'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "worse-local",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(12,5))\n",
    "axs[0].hist(df_secao_2t_jmb['QT_VOTOS'], bins=100);\n",
    "axs[1].hist(df_secao_2t_lils['QT_VOTOS'], bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "approximate-construction",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acquired-guatemala",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "liberal-plasma",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "severe-vegetable",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outstanding-motor",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "important-nevada",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "proper-beatles",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "reduced-utility",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f96390dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}