Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import display, HTML\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MINAS = pd.read_pickle('enem_2016_minas.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NU_INSCRICAO',\n",
       " 'NU_ANO',\n",
       " 'CO_MUNICIPIO_RESIDENCIA',\n",
       " 'NO_MUNICIPIO_RESIDENCIA',\n",
       " 'CO_UF_RESIDENCIA',\n",
       " 'SG_UF_RESIDENCIA',\n",
       " 'NU_IDADE',\n",
       " 'TP_SEXO',\n",
       " 'TP_ESTADO_CIVIL',\n",
       " 'TP_COR_RACA',\n",
       " 'TP_NACIONALIDADE',\n",
       " 'CO_MUNICIPIO_NASCIMENTO',\n",
       " 'NO_MUNICIPIO_NASCIMENTO',\n",
       " 'CO_UF_NASCIMENTO',\n",
       " 'SG_UF_NASCIMENTO',\n",
       " 'TP_ST_CONCLUSAO',\n",
       " 'TP_ANO_CONCLUIU',\n",
       " 'TP_ESCOLA',\n",
       " 'TP_ENSINO',\n",
       " 'IN_TREINEIRO',\n",
       " 'CO_ESCOLA',\n",
       " 'CO_MUNICIPIO_ESC',\n",
       " 'NO_MUNICIPIO_ESC',\n",
       " 'CO_UF_ESC',\n",
       " 'SG_UF_ESC',\n",
       " 'TP_DEPENDENCIA_ADM_ESC',\n",
       " 'TP_LOCALIZACAO_ESC',\n",
       " 'TP_SIT_FUNC_ESC',\n",
       " 'IN_BAIXA_VISAO',\n",
       " 'IN_CEGUEIRA',\n",
       " 'IN_SURDEZ',\n",
       " 'IN_DEFICIENCIA_AUDITIVA',\n",
       " 'IN_SURDO_CEGUEIRA',\n",
       " 'IN_DEFICIENCIA_FISICA',\n",
       " 'IN_DEFICIENCIA_MENTAL',\n",
       " 'IN_DEFICIT_ATENCAO',\n",
       " 'IN_DISLEXIA',\n",
       " 'IN_DISCALCULIA',\n",
       " 'IN_AUTISMO',\n",
       " 'IN_VISAO_MONOCULAR',\n",
       " 'IN_OUTRA_DEF',\n",
       " 'IN_SABATISTA',\n",
       " 'IN_GESTANTE',\n",
       " 'IN_LACTANTE',\n",
       " 'IN_IDOSO',\n",
       " 'IN_ESTUDA_CLASSE_HOSPITALAR',\n",
       " 'IN_SEM_RECURSO',\n",
       " 'IN_BRAILLE',\n",
       " 'IN_AMPLIADA_24',\n",
       " 'IN_AMPLIADA_18',\n",
       " 'IN_LEDOR',\n",
       " 'IN_ACESSO',\n",
       " 'IN_TRANSCRICAO',\n",
       " 'IN_LIBRAS',\n",
       " 'IN_LEITURA_LABIAL',\n",
       " 'IN_MESA_CADEIRA_RODAS',\n",
       " 'IN_MESA_CADEIRA_SEPARADA',\n",
       " 'IN_APOIO_PERNA',\n",
       " 'IN_GUIA_INTERPRETE',\n",
       " 'IN_MACA',\n",
       " 'IN_COMPUTADOR',\n",
       " 'IN_CADEIRA_ESPECIAL',\n",
       " 'IN_CADEIRA_CANHOTO',\n",
       " 'IN_CADEIRA_ACOLCHOADA',\n",
       " 'IN_PROVA_DEITADO',\n",
       " 'IN_MOBILIARIO_OBESO',\n",
       " 'IN_LAMINA_OVERLAY',\n",
       " 'IN_PROTETOR_AURICULAR',\n",
       " 'IN_MEDIDOR_GLICOSE',\n",
       " 'IN_MAQUINA_BRAILE',\n",
       " 'IN_SOROBAN',\n",
       " 'IN_MARCA_PASSO',\n",
       " 'IN_SONDA',\n",
       " 'IN_MEDICAMENTOS',\n",
       " 'IN_SALA_INDIVIDUAL',\n",
       " 'IN_SALA_ESPECIAL',\n",
       " 'IN_SALA_ACOMPANHANTE',\n",
       " 'IN_MOBILIARIO_ESPECIFICO',\n",
       " 'IN_MATERIAL_ESPECIFICO',\n",
       " 'IN_NOME_SOCIAL',\n",
       " 'IN_CERTIFICADO',\n",
       " 'NO_ENTIDADE_CERTIFICACAO',\n",
       " 'CO_UF_ENTIDADE_CERTIFICACAO',\n",
       " 'SG_UF_ENTIDADE_CERTIFICACAO',\n",
       " 'CO_MUNICIPIO_PROVA',\n",
       " 'NO_MUNICIPIO_PROVA',\n",
       " 'CO_UF_PROVA',\n",
       " 'SG_UF_PROVA',\n",
       " 'TP_PRESENCA_CN',\n",
       " 'TP_PRESENCA_CH',\n",
       " 'TP_PRESENCA_LC',\n",
       " 'TP_PRESENCA_MT',\n",
       " 'CO_PROVA_CN',\n",
       " 'CO_PROVA_CH',\n",
       " 'CO_PROVA_LC',\n",
       " 'CO_PROVA_MT',\n",
       " 'NU_NOTA_CN',\n",
       " 'NU_NOTA_CH',\n",
       " 'NU_NOTA_LC',\n",
       " 'NU_NOTA_MT',\n",
       " 'TX_RESPOSTAS_CN',\n",
       " 'TX_RESPOSTAS_CH',\n",
       " 'TX_RESPOSTAS_LC',\n",
       " 'TX_RESPOSTAS_MT',\n",
       " 'TP_LINGUA',\n",
       " 'TX_GABARITO_CN',\n",
       " 'TX_GABARITO_CH',\n",
       " 'TX_GABARITO_LC',\n",
       " 'TX_GABARITO_MT',\n",
       " 'TP_STATUS_REDACAO',\n",
       " 'NU_NOTA_COMP1',\n",
       " 'NU_NOTA_COMP2',\n",
       " 'NU_NOTA_COMP3',\n",
       " 'NU_NOTA_COMP4',\n",
       " 'NU_NOTA_COMP5',\n",
       " 'NU_NOTA_REDACAO',\n",
       " 'Q001',\n",
       " 'Q002',\n",
       " 'Q003',\n",
       " 'Q004',\n",
       " 'Q005',\n",
       " 'Q006',\n",
       " 'Q007',\n",
       " 'Q008',\n",
       " 'Q009',\n",
       " 'Q010',\n",
       " 'Q011',\n",
       " 'Q012',\n",
       " 'Q013',\n",
       " 'Q014',\n",
       " 'Q015',\n",
       " 'Q016',\n",
       " 'Q017',\n",
       " 'Q018',\n",
       " 'Q019',\n",
       " 'Q020',\n",
       " 'Q021',\n",
       " 'Q022',\n",
       " 'Q023',\n",
       " 'Q024',\n",
       " 'Q025',\n",
       " 'Q026',\n",
       " 'Q027',\n",
       " 'Q028',\n",
       " 'Q029',\n",
       " 'Q030',\n",
       " 'Q031',\n",
       " 'Q032',\n",
       " 'Q033',\n",
       " 'Q034',\n",
       " 'Q035',\n",
       " 'Q036',\n",
       " 'Q037',\n",
       " 'Q038',\n",
       " 'Q039',\n",
       " 'Q040',\n",
       " 'Q041',\n",
       " 'Q042',\n",
       " 'Q043',\n",
       " 'Q044',\n",
       " 'Q045',\n",
       " 'Q046',\n",
       " 'Q047',\n",
       " 'Q048',\n",
       " 'Q049',\n",
       " 'Q050']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(MINAS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sort = MINAS.sort_values(by=['NU_NOTA_MT'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_INSCRICAO</th>\n",
       "      <th>NU_ANO</th>\n",
       "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>CO_UF_RESIDENCIA</th>\n",
       "      <th>SG_UF_RESIDENCIA</th>\n",
       "      <th>NU_IDADE</th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th>TP_ESTADO_CIVIL</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>...</th>\n",
       "      <th>Q041</th>\n",
       "      <th>Q042</th>\n",
       "      <th>Q043</th>\n",
       "      <th>Q044</th>\n",
       "      <th>Q045</th>\n",
       "      <th>Q046</th>\n",
       "      <th>Q047</th>\n",
       "      <th>Q048</th>\n",
       "      <th>Q049</th>\n",
       "      <th>Q050</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1254190</th>\n",
       "      <td>160000172519</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809767</th>\n",
       "      <td>160001983942</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4649479</th>\n",
       "      <td>160004587353</td>\n",
       "      <td>2016</td>\n",
       "      <td>3122306</td>\n",
       "      <td>Divinópolis</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799655</th>\n",
       "      <td>160002377124</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549642</th>\n",
       "      <td>160002128788</td>\n",
       "      <td>2016</td>\n",
       "      <td>3170206</td>\n",
       "      <td>Uberlândia</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389924</th>\n",
       "      <td>160001227818</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8097502</th>\n",
       "      <td>160007962000</td>\n",
       "      <td>2016</td>\n",
       "      <td>3113305</td>\n",
       "      <td>Carangola</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8021825</th>\n",
       "      <td>160008037035</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>43.0</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936806</th>\n",
       "      <td>160000178736</td>\n",
       "      <td>2016</td>\n",
       "      <td>3129806</td>\n",
       "      <td>Ibirité</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930967</th>\n",
       "      <td>160002588065</td>\n",
       "      <td>2016</td>\n",
       "      <td>3118304</td>\n",
       "      <td>Conselheiro Lafaiete</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5080899</th>\n",
       "      <td>160004961464</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3976671</th>\n",
       "      <td>160003793644</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>19.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920109</th>\n",
       "      <td>160002586677</td>\n",
       "      <td>2016</td>\n",
       "      <td>3122306</td>\n",
       "      <td>Divinópolis</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938226</th>\n",
       "      <td>160002762316</td>\n",
       "      <td>2016</td>\n",
       "      <td>3170107</td>\n",
       "      <td>Uberaba</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830467</th>\n",
       "      <td>160002379239</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8509533</th>\n",
       "      <td>160008480997</td>\n",
       "      <td>2016</td>\n",
       "      <td>3136207</td>\n",
       "      <td>João Monlevade</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705073</th>\n",
       "      <td>160002716418</td>\n",
       "      <td>2016</td>\n",
       "      <td>3136702</td>\n",
       "      <td>Juiz de Fora</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8587004</th>\n",
       "      <td>160008473305</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296857</th>\n",
       "      <td>160000211336</td>\n",
       "      <td>2016</td>\n",
       "      <td>3127701</td>\n",
       "      <td>Governador Valadares</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5059488</th>\n",
       "      <td>160005059343</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903052</th>\n",
       "      <td>160002176358</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543002</th>\n",
       "      <td>160001967154</td>\n",
       "      <td>2016</td>\n",
       "      <td>3136702</td>\n",
       "      <td>Juiz de Fora</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540447</th>\n",
       "      <td>160003747899</td>\n",
       "      <td>2016</td>\n",
       "      <td>3127701</td>\n",
       "      <td>Governador Valadares</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022969</th>\n",
       "      <td>160005203427</td>\n",
       "      <td>2016</td>\n",
       "      <td>3131307</td>\n",
       "      <td>Ipatinga</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794722</th>\n",
       "      <td>160001965794</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4224251</th>\n",
       "      <td>160004048146</td>\n",
       "      <td>2016</td>\n",
       "      <td>3168705</td>\n",
       "      <td>Timóteo</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557112</th>\n",
       "      <td>160002532168</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3756205</th>\n",
       "      <td>160003674704</td>\n",
       "      <td>2016</td>\n",
       "      <td>3151800</td>\n",
       "      <td>Poços de Caldas</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101131</th>\n",
       "      <td>160003341875</td>\n",
       "      <td>2016</td>\n",
       "      <td>3147105</td>\n",
       "      <td>Pará de Minas</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241743</th>\n",
       "      <td>160000633108</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626826</th>\n",
       "      <td>160008482043</td>\n",
       "      <td>2016</td>\n",
       "      <td>3143302</td>\n",
       "      <td>Montes Claros</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626874</th>\n",
       "      <td>160008411480</td>\n",
       "      <td>2016</td>\n",
       "      <td>3171303</td>\n",
       "      <td>Viçosa</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626887</th>\n",
       "      <td>160008483874</td>\n",
       "      <td>2016</td>\n",
       "      <td>3132404</td>\n",
       "      <td>Itajubá</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>39.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626889</th>\n",
       "      <td>160008622532</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626897</th>\n",
       "      <td>160008609339</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626914</th>\n",
       "      <td>160008378159</td>\n",
       "      <td>2016</td>\n",
       "      <td>3127701</td>\n",
       "      <td>Governador Valadares</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626958</th>\n",
       "      <td>160008574946</td>\n",
       "      <td>2016</td>\n",
       "      <td>3168705</td>\n",
       "      <td>Timóteo</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626979</th>\n",
       "      <td>160008497528</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626992</th>\n",
       "      <td>160008489314</td>\n",
       "      <td>2016</td>\n",
       "      <td>3105608</td>\n",
       "      <td>Barbacena</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626994</th>\n",
       "      <td>160008624291</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627029</th>\n",
       "      <td>160008458977</td>\n",
       "      <td>2016</td>\n",
       "      <td>3121605</td>\n",
       "      <td>Diamantina</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627038</th>\n",
       "      <td>160008616424</td>\n",
       "      <td>2016</td>\n",
       "      <td>3171204</td>\n",
       "      <td>Vespasiano</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>29.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627056</th>\n",
       "      <td>160008523876</td>\n",
       "      <td>2016</td>\n",
       "      <td>3101607</td>\n",
       "      <td>Alfenas</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627068</th>\n",
       "      <td>160008482050</td>\n",
       "      <td>2016</td>\n",
       "      <td>3162500</td>\n",
       "      <td>São João del Rei</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627104</th>\n",
       "      <td>160008608857</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627117</th>\n",
       "      <td>160008458993</td>\n",
       "      <td>2016</td>\n",
       "      <td>3167202</td>\n",
       "      <td>Sete Lagoas</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627120</th>\n",
       "      <td>160008482052</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627149</th>\n",
       "      <td>160008458997</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627150</th>\n",
       "      <td>160008459003</td>\n",
       "      <td>2016</td>\n",
       "      <td>3136306</td>\n",
       "      <td>João Pinheiro</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627163</th>\n",
       "      <td>160008588490</td>\n",
       "      <td>2016</td>\n",
       "      <td>3115102</td>\n",
       "      <td>Cássia</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>29.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627172</th>\n",
       "      <td>160008612160</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627184</th>\n",
       "      <td>160008459006</td>\n",
       "      <td>2016</td>\n",
       "      <td>3154606</td>\n",
       "      <td>Ribeirão das Neves</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>44.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627193</th>\n",
       "      <td>160008523879</td>\n",
       "      <td>2016</td>\n",
       "      <td>3160702</td>\n",
       "      <td>Santos Dumont</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627196</th>\n",
       "      <td>160008497544</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>38.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627204</th>\n",
       "      <td>160008374674</td>\n",
       "      <td>2016</td>\n",
       "      <td>3101409</td>\n",
       "      <td>Albertina</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627223</th>\n",
       "      <td>160008462289</td>\n",
       "      <td>2016</td>\n",
       "      <td>3129806</td>\n",
       "      <td>Ibirité</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627238</th>\n",
       "      <td>160008483776</td>\n",
       "      <td>2016</td>\n",
       "      <td>3171303</td>\n",
       "      <td>Viçosa</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627282</th>\n",
       "      <td>160008462303</td>\n",
       "      <td>2016</td>\n",
       "      <td>3138401</td>\n",
       "      <td>Leopoldina</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627326</th>\n",
       "      <td>160008608862</td>\n",
       "      <td>2016</td>\n",
       "      <td>3127701</td>\n",
       "      <td>Governador Valadares</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627329</th>\n",
       "      <td>160008483779</td>\n",
       "      <td>2016</td>\n",
       "      <td>3106200</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>31</td>\n",
       "      <td>MG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>954219 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  \\\n",
       "1254190  160000172519    2016                  3106200   \n",
       "2809767  160001983942    2016                  3106200   \n",
       "4649479  160004587353    2016                  3122306   \n",
       "799655   160002377124    2016                  3106200   \n",
       "1549642  160002128788    2016                  3170206   \n",
       "389924   160001227818    2016                  3106200   \n",
       "8097502  160007962000    2016                  3113305   \n",
       "8021825  160008037035    2016                  3106200   \n",
       "936806   160000178736    2016                  3129806   \n",
       "1930967  160002588065    2016                  3118304   \n",
       "5080899  160004961464    2016                  3106200   \n",
       "3976671  160003793644    2016                  3106200   \n",
       "1920109  160002586677    2016                  3122306   \n",
       "2938226  160002762316    2016                  3170107   \n",
       "830467   160002379239    2016                  3106200   \n",
       "8509533  160008480997    2016                  3136207   \n",
       "1705073  160002716418    2016                  3136702   \n",
       "8587004  160008473305    2016                  3106200   \n",
       "1296857  160000211336    2016                  3127701   \n",
       "5059488  160005059343    2016                  3106200   \n",
       "903052   160002176358    2016                  3106200   \n",
       "543002   160001967154    2016                  3136702   \n",
       "3540447  160003747899    2016                  3127701   \n",
       "5022969  160005203427    2016                  3131307   \n",
       "2794722  160001965794    2016                  3106200   \n",
       "4224251  160004048146    2016                  3168705   \n",
       "1557112  160002532168    2016                  3106200   \n",
       "3756205  160003674704    2016                  3151800   \n",
       "3101131  160003341875    2016                  3147105   \n",
       "1241743  160000633108    2016                  3106200   \n",
       "...               ...     ...                      ...   \n",
       "8626826  160008482043    2016                  3143302   \n",
       "8626874  160008411480    2016                  3171303   \n",
       "8626887  160008483874    2016                  3132404   \n",
       "8626889  160008622532    2016                  3106200   \n",
       "8626897  160008609339    2016                  3106200   \n",
       "8626914  160008378159    2016                  3127701   \n",
       "8626958  160008574946    2016                  3168705   \n",
       "8626979  160008497528    2016                  3106200   \n",
       "8626992  160008489314    2016                  3105608   \n",
       "8626994  160008624291    2016                  3106200   \n",
       "8627029  160008458977    2016                  3121605   \n",
       "8627038  160008616424    2016                  3171204   \n",
       "8627056  160008523876    2016                  3101607   \n",
       "8627068  160008482050    2016                  3162500   \n",
       "8627104  160008608857    2016                  3106200   \n",
       "8627117  160008458993    2016                  3167202   \n",
       "8627120  160008482052    2016                  3106200   \n",
       "8627149  160008458997    2016                  3106200   \n",
       "8627150  160008459003    2016                  3136306   \n",
       "8627163  160008588490    2016                  3115102   \n",
       "8627172  160008612160    2016                  3106200   \n",
       "8627184  160008459006    2016                  3154606   \n",
       "8627193  160008523879    2016                  3160702   \n",
       "8627196  160008497544    2016                  3106200   \n",
       "8627204  160008374674    2016                  3101409   \n",
       "8627223  160008462289    2016                  3129806   \n",
       "8627238  160008483776    2016                  3171303   \n",
       "8627282  160008462303    2016                  3138401   \n",
       "8627326  160008608862    2016                  3127701   \n",
       "8627329  160008483779    2016                  3106200   \n",
       "\n",
       "        NO_MUNICIPIO_RESIDENCIA  CO_UF_RESIDENCIA SG_UF_RESIDENCIA  NU_IDADE  \\\n",
       "1254190          Belo Horizonte                31               MG      19.0   \n",
       "2809767          Belo Horizonte                31               MG      26.0   \n",
       "4649479             Divinópolis                31               MG      25.0   \n",
       "799655           Belo Horizonte                31               MG      17.0   \n",
       "1549642              Uberlândia                31               MG      17.0   \n",
       "389924           Belo Horizonte                31               MG      17.0   \n",
       "8097502               Carangola                31               MG      24.0   \n",
       "8021825          Belo Horizonte                31               MG      43.0   \n",
       "936806                  Ibirité                31               MG      26.0   \n",
       "1930967    Conselheiro Lafaiete                31               MG      18.0   \n",
       "5080899          Belo Horizonte                31               MG      28.0   \n",
       "3976671          Belo Horizonte                31               MG      19.0   \n",
       "1920109             Divinópolis                31               MG      19.0   \n",
       "2938226                 Uberaba                31               MG      21.0   \n",
       "830467           Belo Horizonte                31               MG      18.0   \n",
       "8509533          João Monlevade                31               MG      31.0   \n",
       "1705073            Juiz de Fora                31               MG      18.0   \n",
       "8587004          Belo Horizonte                31               MG      24.0   \n",
       "1296857    Governador Valadares                31               MG      17.0   \n",
       "5059488          Belo Horizonte                31               MG      17.0   \n",
       "903052           Belo Horizonte                31               MG      17.0   \n",
       "543002             Juiz de Fora                31               MG      22.0   \n",
       "3540447    Governador Valadares                31               MG      17.0   \n",
       "5022969                Ipatinga                31               MG      17.0   \n",
       "2794722          Belo Horizonte                31               MG      20.0   \n",
       "4224251                 Timóteo                31               MG      18.0   \n",
       "1557112          Belo Horizonte                31               MG      18.0   \n",
       "3756205         Poços de Caldas                31               MG      17.0   \n",
       "3101131           Pará de Minas                31               MG      17.0   \n",
       "1241743          Belo Horizonte                31               MG      18.0   \n",
       "...                         ...               ...              ...       ...   \n",
       "8626826           Montes Claros                31               MG      23.0   \n",
       "8626874                  Viçosa                31               MG      23.0   \n",
       "8626887                 Itajubá                31               MG      39.0   \n",
       "8626889          Belo Horizonte                31               MG      30.0   \n",
       "8626897          Belo Horizonte                31               MG      18.0   \n",
       "8626914    Governador Valadares                31               MG      21.0   \n",
       "8626958                 Timóteo                31               MG      25.0   \n",
       "8626979          Belo Horizonte                31               MG      18.0   \n",
       "8626992               Barbacena                31               MG      30.0   \n",
       "8626994          Belo Horizonte                31               MG      27.0   \n",
       "8627029              Diamantina                31               MG      24.0   \n",
       "8627038              Vespasiano                31               MG      29.0   \n",
       "8627056                 Alfenas                31               MG      21.0   \n",
       "8627068        São João del Rei                31               MG      21.0   \n",
       "8627104          Belo Horizonte                31               MG      20.0   \n",
       "8627117             Sete Lagoas                31               MG      20.0   \n",
       "8627120          Belo Horizonte                31               MG      27.0   \n",
       "8627149          Belo Horizonte                31               MG      39.0   \n",
       "8627150           João Pinheiro                31               MG      20.0   \n",
       "8627163                  Cássia                31               MG      29.0   \n",
       "8627172          Belo Horizonte                31               MG      18.0   \n",
       "8627184      Ribeirão das Neves                31               MG      44.0   \n",
       "8627193           Santos Dumont                31               MG      21.0   \n",
       "8627196          Belo Horizonte                31               MG      38.0   \n",
       "8627204               Albertina                31               MG      15.0   \n",
       "8627223                 Ibirité                31               MG      28.0   \n",
       "8627238                  Viçosa                31               MG      28.0   \n",
       "8627282              Leopoldina                31               MG      24.0   \n",
       "8627326    Governador Valadares                31               MG      18.0   \n",
       "8627329          Belo Horizonte                31               MG      28.0   \n",
       "\n",
       "        TP_SEXO  TP_ESTADO_CIVIL  TP_COR_RACA  ...   Q041  Q042 Q043  Q044  \\\n",
       "1254190       M              0.0            1  ...    0.0     C    A     B   \n",
       "2809767       M              0.0            1  ...    NaN     D    A     A   \n",
       "4649479       F              0.0            1  ...    5.0     A    A     A   \n",
       "799655        F              0.0            1  ...    NaN     C    A     A   \n",
       "1549642       M              0.0            1  ...    NaN     C    A     A   \n",
       "389924        M              0.0            1  ...    NaN     C    A     A   \n",
       "8097502       M              0.0            1  ...    5.0     A    A     A   \n",
       "8021825       M              1.0            1  ...    0.0     C    A     A   \n",
       "936806        M              0.0            3  ...    NaN     A    A     A   \n",
       "1930967       F              0.0            1  ...    NaN     C    A     A   \n",
       "5080899       M              0.0            1  ...    0.0     A    A     A   \n",
       "3976671       F              0.0            1  ...    NaN     A    A     A   \n",
       "1920109       M              0.0            1  ...    NaN     C    A     A   \n",
       "2938226       M              0.0            1  ...    NaN     C    A     A   \n",
       "830467        M              0.0            1  ...    NaN     C    A     A   \n",
       "8509533       M              0.0            1  ...    NaN     A    A     A   \n",
       "1705073       M              0.0            1  ...    NaN     C    A     A   \n",
       "8587004       M              0.0            3  ...    0.0     D    A     A   \n",
       "1296857       F              0.0            1  ...    NaN     C    A     A   \n",
       "5059488       M              0.0            1  ...    NaN     C    A     A   \n",
       "903052        M              0.0            1  ...    NaN     C    A     A   \n",
       "543002        F              0.0            1  ...    NaN     C    A     A   \n",
       "3540447       F              0.0            3  ...    NaN     C    A     A   \n",
       "5022969       M              0.0            2  ...    NaN     C    A     A   \n",
       "2794722       M              0.0            1  ...    NaN     C    A     A   \n",
       "4224251       M              0.0            1  ...    NaN     C    A     A   \n",
       "1557112       M              0.0            1  ...    NaN     C    A     A   \n",
       "3756205       M              0.0            1  ...    NaN     C    A     A   \n",
       "3101131       M              0.0            1  ...    NaN     C    A     A   \n",
       "1241743       M              0.0            0  ...    NaN     C    A     A   \n",
       "...         ...              ...          ...  ...    ...   ...  ...   ...   \n",
       "8626826       F              1.0            2  ...    NaN     A    B     B   \n",
       "8626874       M              0.0            3  ...    NaN     A    A     B   \n",
       "8626887       F              2.0            1  ...    5.0     A    B     B   \n",
       "8626889       F              0.0            3  ...    5.0     A    B     B   \n",
       "8626897       F              0.0            3  ...    NaN     A    A     A   \n",
       "8626914       F              0.0            3  ...    5.0     A    A     A   \n",
       "8626958       M              0.0            3  ...    5.0     A    A     A   \n",
       "8626979       M              0.0            3  ...    5.0     A    A     A   \n",
       "8626992       F              0.0            2  ...    NaN     A    A     A   \n",
       "8626994       M              0.0            3  ...    5.0     A    A     B   \n",
       "8627029       M              0.0            4  ...    3.0     A    A     A   \n",
       "8627038       F              0.0            3  ...    NaN     A    C     C   \n",
       "8627056       F              0.0            3  ...    NaN     B    A     A   \n",
       "8627068       M              NaN            1  ...    NaN     A    A     A   \n",
       "8627104       F              0.0            1  ...    NaN     A    A     A   \n",
       "8627117       F              1.0            1  ...    NaN     A    B     B   \n",
       "8627120       F              0.0            5  ...    NaN     A    A     A   \n",
       "8627149       M              1.0            3  ...    NaN     A    D     C   \n",
       "8627150       F              0.0            3  ...    NaN     A    A     B   \n",
       "8627163       F              0.0            3  ...    NaN     A    B     A   \n",
       "8627172       F              0.0            2  ...    NaN     A    A     A   \n",
       "8627184       F              2.0            3  ...    5.0     A    A     B   \n",
       "8627193       F              0.0            1  ...    NaN     A    A     A   \n",
       "8627196       M              NaN            2  ...    NaN     A    C     C   \n",
       "8627204       F              0.0            3  ...    NaN     A    A     A   \n",
       "8627223       M              0.0            3  ...    NaN     A    B     C   \n",
       "8627238       F              1.0            1  ...    0.0     A    A     A   \n",
       "8627282       F              0.0            2  ...    NaN     A    A     B   \n",
       "8627326       M              0.0            3  ...    NaN     A    A     A   \n",
       "8627329       M              1.0            3  ...    5.0     A    A     B   \n",
       "\n",
       "        Q045  Q046  Q047  Q048  Q049  Q050  \n",
       "1254190    A     A     D     A     B     A  \n",
       "2809767    A     A     D     A     A     A  \n",
       "4649479    A     A     A     A     A     A  \n",
       "799655     A     B     D     A     A     A  \n",
       "1549642    A     B     D     A     A     A  \n",
       "389924     A     B     D     A     A     A  \n",
       "8097502    A     A     A     A     A     A  \n",
       "8021825    A     A     D     A     A     A  \n",
       "936806     A     A     A     B     B     D  \n",
       "1930967    A     B     D     A     A     A  \n",
       "5080899    A     A     D     A     A     A  \n",
       "3976671    A     A     A     A     A     A  \n",
       "1920109    A     A     D     A     A     A  \n",
       "2938226    A     A     D     A     A     A  \n",
       "830467     A     B     D     A     A     A  \n",
       "8509533    A     A     A     A     A     A  \n",
       "1705073    A     B     D     A     A     A  \n",
       "8587004    A     A     D     A     B     A  \n",
       "1296857    A     B     D     A     A     A  \n",
       "5059488    A     C     D     A     A     A  \n",
       "903052     A     B     D     A     A     A  \n",
       "543002     A     A     D     A     A     A  \n",
       "3540447    A     B     D     A     B     A  \n",
       "5022969    A     B     D     A     A     A  \n",
       "2794722    A     A     D     A     A     A  \n",
       "4224251    A     A     D     A     A     A  \n",
       "1557112    A     B     D     A     A     A  \n",
       "3756205    A     B     D     A     A     A  \n",
       "3101131    A     B     D     A     A     A  \n",
       "1241743    A     B     D     A     A     A  \n",
       "...      ...   ...   ...   ...   ...   ...  \n",
       "8626826    A     A     A     B     C     A  \n",
       "8626874    A     A     A     A     B     A  \n",
       "8626887    C     A     A     B     B     A  \n",
       "8626889    A     A     A     A     B     B  \n",
       "8626897    A     A     A     A     A     A  \n",
       "8626914    A     A     A     A     A     A  \n",
       "8626958    D     A     A     A     A     D  \n",
       "8626979    A     B     A     A     A     B  \n",
       "8626992    A     D     A     A     B     C  \n",
       "8626994    B     A     A     A     B     B  \n",
       "8627029    A     A     A     A     A     A  \n",
       "8627038    A     C     A     C     C     A  \n",
       "8627056    A     A     C     A     A     A  \n",
       "8627068    A     A     A     A     A     B  \n",
       "8627104    A     A     A     A     A     A  \n",
       "8627117    D     B     A     C     C     D  \n",
       "8627120    A     A     A     A     A     A  \n",
       "8627149    B     C     A     D     C     C  \n",
       "8627150    A     A     A     A     B     A  \n",
       "8627163    A     D     E     C     B     C  \n",
       "8627172    B     C     A     A     A     A  \n",
       "8627184    A     B     A     B     C     C  \n",
       "8627193    A     A     A     A     B     A  \n",
       "8627196    A     C     A     C     C     A  \n",
       "8627204    A     C     A     A     A     A  \n",
       "8627223    C     B     A     B     C     C  \n",
       "8627238    A     A     A     B     B     D  \n",
       "8627282    C     B     A     C     C     D  \n",
       "8627326    A     B     A     A     A     A  \n",
       "8627329    D     D     A     A     B     D  \n",
       "\n",
       "[954219 rows x 166 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "TESTE = sort[['NU_NOTA_MT','NU_IDADE', 'TP_SEXO']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_IDADE</th>\n",
       "      <th>TP_SEXO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1254190</th>\n",
       "      <td>991.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809767</th>\n",
       "      <td>991.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4649479</th>\n",
       "      <td>991.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799655</th>\n",
       "      <td>991.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549642</th>\n",
       "      <td>991.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389924</th>\n",
       "      <td>991.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8097502</th>\n",
       "      <td>990.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8021825</th>\n",
       "      <td>990.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936806</th>\n",
       "      <td>990.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930967</th>\n",
       "      <td>985.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5080899</th>\n",
       "      <td>985.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3976671</th>\n",
       "      <td>983.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920109</th>\n",
       "      <td>982.3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938226</th>\n",
       "      <td>980.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830467</th>\n",
       "      <td>980.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8509533</th>\n",
       "      <td>978.9</td>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705073</th>\n",
       "      <td>978.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8587004</th>\n",
       "      <td>977.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296857</th>\n",
       "      <td>977.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5059488</th>\n",
       "      <td>977.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903052</th>\n",
       "      <td>977.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543002</th>\n",
       "      <td>977.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540447</th>\n",
       "      <td>976.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022969</th>\n",
       "      <td>976.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794722</th>\n",
       "      <td>975.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4224251</th>\n",
       "      <td>974.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557112</th>\n",
       "      <td>973.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3756205</th>\n",
       "      <td>973.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101131</th>\n",
       "      <td>972.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241743</th>\n",
       "      <td>972.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626826</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626874</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626887</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626889</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626897</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626914</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626958</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626979</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627029</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627038</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627056</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627068</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627117</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627120</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627149</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627150</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627163</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627172</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627184</th>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627193</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627196</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627204</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627223</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627238</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627282</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627326</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627329</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>954219 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_NOTA_MT  NU_IDADE TP_SEXO\n",
       "1254190       991.5      19.0       M\n",
       "2809767       991.5      26.0       M\n",
       "4649479       991.5      25.0       F\n",
       "799655        991.5      17.0       F\n",
       "1549642       991.5      17.0       M\n",
       "389924        991.5      17.0       M\n",
       "8097502       990.5      24.0       M\n",
       "8021825       990.5      43.0       M\n",
       "936806        990.5      26.0       M\n",
       "1930967       985.3      18.0       F\n",
       "5080899       985.3      28.0       M\n",
       "3976671       983.1      19.0       F\n",
       "1920109       982.3      19.0       M\n",
       "2938226       980.4      21.0       M\n",
       "830467        980.4      18.0       M\n",
       "8509533       978.9      31.0       M\n",
       "1705073       978.2      18.0       M\n",
       "8587004       977.6      24.0       M\n",
       "1296857       977.5      17.0       F\n",
       "5059488       977.5      17.0       M\n",
       "903052        977.2      17.0       M\n",
       "543002        977.2      22.0       F\n",
       "3540447       976.7      17.0       F\n",
       "5022969       976.6      17.0       M\n",
       "2794722       975.3      20.0       M\n",
       "4224251       974.7      18.0       M\n",
       "1557112       973.8      18.0       M\n",
       "3756205       973.2      17.0       M\n",
       "3101131       972.2      17.0       M\n",
       "1241743       972.1      18.0       M\n",
       "...             ...       ...     ...\n",
       "8626826         NaN      23.0       F\n",
       "8626874         NaN      23.0       M\n",
       "8626887         NaN      39.0       F\n",
       "8626889         NaN      30.0       F\n",
       "8626897         NaN      18.0       F\n",
       "8626914         NaN      21.0       F\n",
       "8626958         NaN      25.0       M\n",
       "8626979         NaN      18.0       M\n",
       "8626992         NaN      30.0       F\n",
       "8626994         NaN      27.0       M\n",
       "8627029         NaN      24.0       M\n",
       "8627038         NaN      29.0       F\n",
       "8627056         NaN      21.0       F\n",
       "8627068         NaN      21.0       M\n",
       "8627104         NaN      20.0       F\n",
       "8627117         NaN      20.0       F\n",
       "8627120         NaN      27.0       F\n",
       "8627149         NaN      39.0       M\n",
       "8627150         NaN      20.0       F\n",
       "8627163         NaN      29.0       F\n",
       "8627172         NaN      18.0       F\n",
       "8627184         NaN      44.0       F\n",
       "8627193         NaN      21.0       F\n",
       "8627196         NaN      38.0       M\n",
       "8627204         NaN      15.0       F\n",
       "8627223         NaN      28.0       M\n",
       "8627238         NaN      28.0       F\n",
       "8627282         NaN      24.0       F\n",
       "8627326         NaN      18.0       M\n",
       "8627329         NaN      28.0       M\n",
       "\n",
       "[954219 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(TESTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_IDADE</th>\n",
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       "      <th>5080899</th>\n",
       "      <td>985.3</td>\n",
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       "      <th>1920109</th>\n",
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       "      <th>2938226</th>\n",
       "      <td>980.4</td>\n",
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       "      <th>830467</th>\n",
       "      <td>980.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>8509533</th>\n",
       "      <td>978.9</td>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705073</th>\n",
       "      <td>978.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>8587004</th>\n",
       "      <td>977.6</td>\n",
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       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>1296857</th>\n",
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       "    <tr>\n",
       "      <th>543002</th>\n",
       "      <td>977.2</td>\n",
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       "    <tr>\n",
       "      <th>3540447</th>\n",
       "      <td>976.7</td>\n",
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       "      <td>F</td>\n",
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       "    <tr>\n",
       "      <th>5022969</th>\n",
       "      <td>976.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>2794722</th>\n",
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       "      <th>4224251</th>\n",
       "      <td>974.7</td>\n",
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       "      <th>1557112</th>\n",
       "      <td>973.8</td>\n",
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       "      <th>3756205</th>\n",
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       "      <th>3101131</th>\n",
       "      <td>972.2</td>\n",
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       "    <tr>\n",
       "      <th>1241743</th>\n",
       "      <td>972.1</td>\n",
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       "      <th>7277564</th>\n",
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       "      <td>M</td>\n",
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       "      <th>7277291</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094391</th>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4388519</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7306711</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8576334</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7309126</th>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5602865</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4393462</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6072800</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726709</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057821</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7332082</th>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7331185</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115904</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7323744</th>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5297116</th>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122490</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345504</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345703</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051969</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446330</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8573786</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050545</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528950</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4966188</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>642484 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_NOTA_MT  NU_IDADE TP_SEXO\n",
       "1254190       991.5      19.0       M\n",
       "2809767       991.5      26.0       M\n",
       "4649479       991.5      25.0       F\n",
       "799655        991.5      17.0       F\n",
       "1549642       991.5      17.0       M\n",
       "389924        991.5      17.0       M\n",
       "8097502       990.5      24.0       M\n",
       "8021825       990.5      43.0       M\n",
       "936806        990.5      26.0       M\n",
       "1930967       985.3      18.0       F\n",
       "5080899       985.3      28.0       M\n",
       "3976671       983.1      19.0       F\n",
       "1920109       982.3      19.0       M\n",
       "2938226       980.4      21.0       M\n",
       "830467        980.4      18.0       M\n",
       "8509533       978.9      31.0       M\n",
       "1705073       978.2      18.0       M\n",
       "8587004       977.6      24.0       M\n",
       "1296857       977.5      17.0       F\n",
       "5059488       977.5      17.0       M\n",
       "903052        977.2      17.0       M\n",
       "543002        977.2      22.0       F\n",
       "3540447       976.7      17.0       F\n",
       "5022969       976.6      17.0       M\n",
       "2794722       975.3      20.0       M\n",
       "4224251       974.7      18.0       M\n",
       "1557112       973.8      18.0       M\n",
       "3756205       973.2      17.0       M\n",
       "3101131       972.2      17.0       M\n",
       "1241743       972.1      18.0       M\n",
       "...             ...       ...     ...\n",
       "173934          0.0      18.0       M\n",
       "7283440         0.0      36.0       F\n",
       "2220156         0.0      30.0       F\n",
       "7277564         0.0      21.0       M\n",
       "7277291         0.0      23.0       F\n",
       "5094391         0.0      41.0       F\n",
       "4388519         0.0      25.0       F\n",
       "7306711         0.0      19.0       M\n",
       "8576334         0.0      22.0       M\n",
       "7309126         0.0      37.0       F\n",
       "5602865         0.0      19.0       M\n",
       "4393462         0.0      17.0       M\n",
       "6072800         0.0      24.0       F\n",
       "3726709         0.0      16.0       M\n",
       "2057821         0.0      26.0       M\n",
       "7332082         0.0      37.0       F\n",
       "7331185         0.0      23.0       F\n",
       "1115904         0.0      18.0       F\n",
       "7323744         0.0      34.0       F\n",
       "533000          0.0      45.0       M\n",
       "5297116         0.0      46.0       F\n",
       "1122490         0.0      18.0       F\n",
       "6345504         0.0      18.0       F\n",
       "6345703         0.0      19.0       M\n",
       "2051969         0.0      22.0       M\n",
       "2446330         0.0      23.0       M\n",
       "8573786         0.0      18.0       M\n",
       "2050545         0.0      35.0       M\n",
       "528950          0.0      17.0       F\n",
       "4966188         0.0      17.0       M\n",
       "\n",
       "[642484 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TESTE.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TP_SEXO\n",
       "F    490.981906\n",
       "M    536.515841\n",
       "Name: NU_NOTA_MT, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TESTE.groupby(['TP_SEXO'])['NU_NOTA_MT'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "559"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TESTE[TESTE['NU_NOTA_MT'] == 0]['TP_SEXO'].count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TP_SEXO\n",
       "F    277\n",
       "M    282\n",
       "Name: NU_NOTA_MT, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TESTE[TESTE['NU_NOTA_MT']==0].groupby(['TP_SEXO'])['NU_NOTA_MT'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "TESTE = TESTE[TESTE['NU_NOTA_MT'] != 0].dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_IDADE</th>\n",
       "      <th>TP_SEXO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1254190</th>\n",
       "      <td>991.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809767</th>\n",
       "      <td>991.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4649479</th>\n",
       "      <td>991.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799655</th>\n",
       "      <td>991.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549642</th>\n",
       "      <td>991.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389924</th>\n",
       "      <td>991.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8097502</th>\n",
       "      <td>990.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8021825</th>\n",
       "      <td>990.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936806</th>\n",
       "      <td>990.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930967</th>\n",
       "      <td>985.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5080899</th>\n",
       "      <td>985.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3976671</th>\n",
       "      <td>983.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920109</th>\n",
       "      <td>982.3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938226</th>\n",
       "      <td>980.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830467</th>\n",
       "      <td>980.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8509533</th>\n",
       "      <td>978.9</td>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705073</th>\n",
       "      <td>978.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8587004</th>\n",
       "      <td>977.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296857</th>\n",
       "      <td>977.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5059488</th>\n",
       "      <td>977.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903052</th>\n",
       "      <td>977.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543002</th>\n",
       "      <td>977.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540447</th>\n",
       "      <td>976.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022969</th>\n",
       "      <td>976.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794722</th>\n",
       "      <td>975.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4224251</th>\n",
       "      <td>974.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557112</th>\n",
       "      <td>973.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3756205</th>\n",
       "      <td>973.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101131</th>\n",
       "      <td>972.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241743</th>\n",
       "      <td>972.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294147</th>\n",
       "      <td>320.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7523123</th>\n",
       "      <td>320.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4774120</th>\n",
       "      <td>320.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362487</th>\n",
       "      <td>320.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5865161</th>\n",
       "      <td>320.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6576068</th>\n",
       "      <td>320.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399390</th>\n",
       "      <td>320.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6522372</th>\n",
       "      <td>320.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4292252</th>\n",
       "      <td>320.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8201414</th>\n",
       "      <td>320.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543881</th>\n",
       "      <td>320.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7561846</th>\n",
       "      <td>320.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259612</th>\n",
       "      <td>319.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8332093</th>\n",
       "      <td>319.9</td>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7221921</th>\n",
       "      <td>319.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7968119</th>\n",
       "      <td>319.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443834</th>\n",
       "      <td>319.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429486</th>\n",
       "      <td>319.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6765717</th>\n",
       "      <td>319.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934135</th>\n",
       "      <td>319.8</td>\n",
       "      <td>41.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584282</th>\n",
       "      <td>319.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5618282</th>\n",
       "      <td>319.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440701</th>\n",
       "      <td>319.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5530963</th>\n",
       "      <td>319.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5346213</th>\n",
       "      <td>319.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8501908</th>\n",
       "      <td>319.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5149331</th>\n",
       "      <td>319.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199993</th>\n",
       "      <td>319.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839162</th>\n",
       "      <td>319.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234451</th>\n",
       "      <td>319.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>641925 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_NOTA_MT  NU_IDADE TP_SEXO\n",
       "1254190       991.5      19.0       M\n",
       "2809767       991.5      26.0       M\n",
       "4649479       991.5      25.0       F\n",
       "799655        991.5      17.0       F\n",
       "1549642       991.5      17.0       M\n",
       "389924        991.5      17.0       M\n",
       "8097502       990.5      24.0       M\n",
       "8021825       990.5      43.0       M\n",
       "936806        990.5      26.0       M\n",
       "1930967       985.3      18.0       F\n",
       "5080899       985.3      28.0       M\n",
       "3976671       983.1      19.0       F\n",
       "1920109       982.3      19.0       M\n",
       "2938226       980.4      21.0       M\n",
       "830467        980.4      18.0       M\n",
       "8509533       978.9      31.0       M\n",
       "1705073       978.2      18.0       M\n",
       "8587004       977.6      24.0       M\n",
       "1296857       977.5      17.0       F\n",
       "5059488       977.5      17.0       M\n",
       "903052        977.2      17.0       M\n",
       "543002        977.2      22.0       F\n",
       "3540447       976.7      17.0       F\n",
       "5022969       976.6      17.0       M\n",
       "2794722       975.3      20.0       M\n",
       "4224251       974.7      18.0       M\n",
       "1557112       973.8      18.0       M\n",
       "3756205       973.2      17.0       M\n",
       "3101131       972.2      17.0       M\n",
       "1241743       972.1      18.0       M\n",
       "...             ...       ...     ...\n",
       "1294147       320.2      17.0       F\n",
       "7523123       320.1      16.0       F\n",
       "4774120       320.1      19.0       F\n",
       "362487        320.1      16.0       F\n",
       "5865161       320.1      21.0       M\n",
       "6576068       320.1      24.0       F\n",
       "1399390       320.0      19.0       M\n",
       "6522372       320.0      22.0       F\n",
       "4292252       320.0      19.0       F\n",
       "8201414       320.0      24.0       F\n",
       "3543881       320.0      23.0       M\n",
       "7561846       320.0      26.0       M\n",
       "1259612       319.9      34.0       F\n",
       "8332093       319.9      27.0       M\n",
       "7221921       319.9      18.0       F\n",
       "7968119       319.9      52.0       F\n",
       "3443834       319.9      18.0       M\n",
       "1429486       319.9      17.0       M\n",
       "6765717       319.9      17.0       M\n",
       "3934135       319.8      41.0       F\n",
       "584282        319.8      27.0       F\n",
       "5618282       319.8      20.0       M\n",
       "440701        319.7      23.0       M\n",
       "5530963       319.7      24.0       M\n",
       "5346213       319.6      29.0       F\n",
       "8501908       319.5      22.0       F\n",
       "5149331       319.5      16.0       M\n",
       "6199993       319.5      33.0       F\n",
       "1839162       319.5      30.0       M\n",
       "234451        319.5      44.0       F\n",
       "\n",
       "[641925 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(TESTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TP_SEXO\n",
       "F    491.344275\n",
       "M    537.084127\n",
       "Name: NU_NOTA_MT, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TESTE.groupby(['TP_SEXO'])['NU_NOTA_MT'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_IDADE\n",
       "12.0    492.700000\n",
       "13.0    516.835714\n",
       "14.0    514.046051\n",
       "15.0    513.931594\n",
       "16.0    516.316622\n",
       "17.0    519.307275\n",
       "18.0    516.885124\n",
       "19.0    514.196290\n",
       "20.0    512.527581\n",
       "21.0    510.172873\n",
       "22.0    510.215444\n",
       "23.0    508.656262\n",
       "24.0    511.063963\n",
       "25.0    509.647617\n",
       "26.0    507.561114\n",
       "27.0    507.794698\n",
       "28.0    504.826922\n",
       "29.0    501.560549\n",
       "30.0    500.350166\n",
       "31.0    501.323215\n",
       "32.0    495.984993\n",
       "33.0    493.556776\n",
       "34.0    488.735295\n",
       "35.0    488.047220\n",
       "36.0    484.006697\n",
       "37.0    481.951577\n",
       "38.0    479.626812\n",
       "39.0    478.080628\n",
       "40.0    471.532679\n",
       "41.0    472.278056\n",
       "           ...    \n",
       "52.0    467.560075\n",
       "53.0    468.075624\n",
       "54.0    464.840244\n",
       "55.0    460.270000\n",
       "56.0    465.176539\n",
       "57.0    462.194017\n",
       "58.0    454.893367\n",
       "59.0    458.988024\n",
       "60.0    457.781818\n",
       "61.0    457.061881\n",
       "62.0    465.670060\n",
       "63.0    480.122556\n",
       "64.0    482.164865\n",
       "65.0    436.900000\n",
       "66.0    434.441026\n",
       "67.0    455.890476\n",
       "68.0    454.840816\n",
       "69.0    458.181818\n",
       "70.0    475.966667\n",
       "71.0    467.984211\n",
       "72.0    457.378571\n",
       "73.0    399.150000\n",
       "74.0    413.071429\n",
       "75.0    480.654545\n",
       "76.0    465.100000\n",
       "77.0    418.625000\n",
       "78.0    469.575000\n",
       "80.0    389.533333\n",
       "83.0    419.600000\n",
       "85.0    492.500000\n",
       "Name: NU_NOTA_MT, Length: 70, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_idade = TESTE.groupby(['NU_IDADE'])['NU_NOTA_MT'].mean()\n",
    "media_idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r3FTlt1gKdOhGqZIaWz+NPe/+H+pTO1n1q3/t8npHWzNjF3+Hrc6JLJtzGzMi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\n53dQCsYp5KwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c90361630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_idade.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_IDADE\n",
       "12.0    492.700000\n",
       "13.0    516.835714\n",
       "14.0    514.046051\n",
       "15.0    513.931594\n",
       "16.0    516.316622\n",
       "17.0    519.307275\n",
       "18.0    516.885124\n",
       "19.0    514.196290\n",
       "20.0    512.527581\n",
       "21.0    510.172873\n",
       "22.0    510.215444\n",
       "23.0    508.656262\n",
       "24.0    511.063963\n",
       "25.0    509.647617\n",
       "26.0    507.561114\n",
       "27.0    507.794698\n",
       "28.0    504.826922\n",
       "29.0    501.560549\n",
       "30.0    500.350166\n",
       "31.0    501.323215\n",
       "32.0    495.984993\n",
       "33.0    493.556776\n",
       "34.0    488.735295\n",
       "35.0    488.047220\n",
       "36.0    484.006697\n",
       "37.0    481.951577\n",
       "38.0    479.626812\n",
       "39.0    478.080628\n",
       "40.0    471.532679\n",
       "41.0    472.278056\n",
       "42.0    470.716991\n",
       "43.0    472.072854\n",
       "44.0    470.790581\n",
       "45.0    469.785608\n",
       "46.0    470.047887\n",
       "47.0    470.458660\n",
       "48.0    467.987709\n",
       "49.0    465.758889\n",
       "50.0    466.330512\n",
       "51.0    467.873847\n",
       "52.0    467.560075\n",
       "53.0    468.075624\n",
       "54.0    464.840244\n",
       "55.0    460.270000\n",
       "56.0    465.176539\n",
       "57.0    462.194017\n",
       "58.0    454.893367\n",
       "59.0    458.988024\n",
       "60.0    457.781818\n",
       "Name: NU_NOTA_MT, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_idade_60 = TESTE[TESTE['NU_IDADE']<=60].groupby(['NU_IDADE'])['NU_NOTA_MT'].mean()\n",
    "media_idade_60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XQoICeGpCfwpc5Tzw/hp8YXEeY8zxfCpdcwtctLGunSarW0IU95/fg/kZObz1wy6nyzHG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\na/fw6bo9bNrnXlY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2cb8308400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_idade_60.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>IN_DISLEXIA</th>\n",
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       "      <td>990.5</td>\n",
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       "    <tr>\n",
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       "      <td>977.2</td>\n",
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       "      <td>0</td>\n",
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       "      <th>8626992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627029</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627038</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627056</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627068</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627117</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627120</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627149</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627150</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627163</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627172</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627184</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627193</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627196</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627204</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627223</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627238</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627282</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627326</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627329</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>954219 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_NOTA_MT  IN_DISLEXIA\n",
       "1254190       991.5            0\n",
       "2809767       991.5            0\n",
       "4649479       991.5            0\n",
       "799655        991.5            0\n",
       "1549642       991.5            0\n",
       "389924        991.5            0\n",
       "8097502       990.5            0\n",
       "8021825       990.5            0\n",
       "936806        990.5            0\n",
       "1930967       985.3            0\n",
       "5080899       985.3            0\n",
       "3976671       983.1            0\n",
       "1920109       982.3            0\n",
       "2938226       980.4            0\n",
       "830467        980.4            0\n",
       "8509533       978.9            0\n",
       "1705073       978.2            0\n",
       "8587004       977.6            0\n",
       "1296857       977.5            0\n",
       "5059488       977.5            0\n",
       "903052        977.2            0\n",
       "543002        977.2            0\n",
       "3540447       976.7            0\n",
       "5022969       976.6            0\n",
       "2794722       975.3            0\n",
       "4224251       974.7            0\n",
       "1557112       973.8            0\n",
       "3756205       973.2            0\n",
       "3101131       972.2            0\n",
       "1241743       972.1            0\n",
       "...             ...          ...\n",
       "8626826         NaN            0\n",
       "8626874         NaN            0\n",
       "8626887         NaN            0\n",
       "8626889         NaN            0\n",
       "8626897         NaN            0\n",
       "8626914         NaN            0\n",
       "8626958         NaN            0\n",
       "8626979         NaN            0\n",
       "8626992         NaN            0\n",
       "8626994         NaN            0\n",
       "8627029         NaN            0\n",
       "8627038         NaN            0\n",
       "8627056         NaN            0\n",
       "8627068         NaN            0\n",
       "8627104         NaN            0\n",
       "8627117         NaN            0\n",
       "8627120         NaN            0\n",
       "8627149         NaN            0\n",
       "8627150         NaN            0\n",
       "8627163         NaN            0\n",
       "8627172         NaN            0\n",
       "8627184         NaN            0\n",
       "8627193         NaN            0\n",
       "8627196         NaN            0\n",
       "8627204         NaN            0\n",
       "8627223         NaN            0\n",
       "8627238         NaN            0\n",
       "8627282         NaN            0\n",
       "8627326         NaN            0\n",
       "8627329         NaN            0\n",
       "\n",
       "[954219 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dislexia = sort[['NU_NOTA_MT','IN_DISLEXIA']]\n",
    "dislexia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_DISLEXIA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NU_NOTA_MT\n",
       "IN_DISLEXIA            \n",
       "0                642326\n",
       "1                   168"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dislexia.groupby(['IN_DISLEXIA']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_AUTISMO</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NU_NOTA_MT\n",
       "IN_AUTISMO            \n",
       "0               642467\n",
       "1                   27"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autismo = sort[['NU_NOTA_MT','IN_AUTISMO']]\n",
    "autismo.groupby(['IN_AUTISMO']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_CEGUEIRA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NU_NOTA_MT\n",
       "IN_CEGUEIRA            \n",
       "0                642396\n",
       "1                    98"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cegueira = sort[['NU_NOTA_MT','IN_CEGUEIRA']]\n",
    "cegueira.groupby(['IN_CEGUEIRA']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SURDEZ</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           NU_NOTA_MT\n",
       "IN_SURDEZ            \n",
       "0              642311\n",
       "1                 183"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surdez = sort[['NU_NOTA_MT','IN_SURDEZ']]\n",
    "surdez.groupby(['IN_SURDEZ']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SURDO_CEGUEIRA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NU_NOTA_MT\n",
       "IN_SURDO_CEGUEIRA            \n",
       "0                      642493\n",
       "1                           1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surdo_cegueira = sort[['NU_NOTA_MT','IN_SURDO_CEGUEIRA']]\n",
    "surdo_cegueira.groupby(['IN_SURDO_CEGUEIRA']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_GESTANTE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NU_NOTA_MT\n",
       "IN_GESTANTE            \n",
       "0                642303\n",
       "1                   191"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gestante = sort[['NU_NOTA_MT','IN_GESTANTE']]\n",
    "gestante.groupby(['IN_GESTANTE']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SABATISTA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>639388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3106</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              NU_NOTA_MT\n",
       "IN_SABATISTA            \n",
       "0                 639388\n",
       "1                   3106"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sabatista = sort[['NU_NOTA_MT','IN_SABATISTA']]\n",
    "sabatista.groupby(['IN_SABATISTA']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_CADEIRA_CANHOTO</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    NU_NOTA_MT\n",
       "IN_CADEIRA_CANHOTO            \n",
       "0                       642491\n",
       "1                            3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canhoto = sort[['NU_NOTA_MT','IN_CADEIRA_CANHOTO']]\n",
    "canhoto.groupby(['IN_CADEIRA_CANHOTO']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_MOBILIARIO_OBESO</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NU_NOTA_MT\n",
       "IN_MOBILIARIO_OBESO            \n",
       "0                        642491\n",
       "1                             3"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obeso = sort[['NU_NOTA_MT','IN_MOBILIARIO_OBESO']]\n",
    "obeso.groupby(['IN_MOBILIARIO_OBESO']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_NOME_SOCIAL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>642467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NU_NOTA_MT\n",
       "IN_NOME_SOCIAL            \n",
       "0                   642467\n",
       "1                       27"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nome_social = sort[['NU_NOTA_MT','IN_NOME_SOCIAL']]\n",
    "nome_social.groupby(['IN_NOME_SOCIAL']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mXx3rDzUShbkRPrpsLg9ufF0X4ImIDGLUQeHum919sbsvBpYA7cC9wdvfHXjP3R8AMLOT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\nqf8FRUYYbEqMoDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2cb73f8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = sort[['IN_CERTIFICADO','NU_IDADE']]\n",
    "c = c[c['NU_IDADE']<=60]\n",
    "c = c[c['IN_CERTIFICADO']==1].groupby('NU_IDADE')['IN_CERTIFICADO'].count()\n",
    "c.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[17418,\n",
       " 13098,\n",
       " 8674,\n",
       " 7108,\n",
       " 5740,\n",
       " 5013,\n",
       " 4186,\n",
       " 3700,\n",
       " 3253,\n",
       " 2915,\n",
       " 2665,\n",
       " 2314,\n",
       " 1978,\n",
       " 1764,\n",
       " 1612,\n",
       " 1607,\n",
       " 1639,\n",
       " 1466,\n",
       " 1354,\n",
       " 1337,\n",
       " 1252,\n",
       " 1196,\n",
       " 1203,\n",
       " 1079,\n",
       " 1035,\n",
       " 1035,\n",
       " 990,\n",
       " 934,\n",
       " 864,\n",
       " 760,\n",
       " 703,\n",
       " 662,\n",
       " 577,\n",
       " 508,\n",
       " 443,\n",
       " 403,\n",
       " 338,\n",
       " 284,\n",
       " 273,\n",
       " 203,\n",
       " 196,\n",
       " 148,\n",
       " 133]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = []\n",
    "for i in c:\n",
    "    T.append(i)\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_IDADE\n",
       "18.0    129415\n",
       "19.0     99651\n",
       "20.0     76742\n",
       "21.0     61977\n",
       "22.0     50572\n",
       "23.0     41577\n",
       "24.0     34299\n",
       "25.0     29474\n",
       "26.0     24957\n",
       "27.0     21642\n",
       "28.0     19249\n",
       "29.0     16475\n",
       "30.0     14415\n",
       "31.0     12488\n",
       "32.0     11153\n",
       "33.0     10672\n",
       "34.0     10371\n",
       "35.0      9444\n",
       "36.0      8061\n",
       "37.0      7575\n",
       "38.0      6820\n",
       "39.0      6211\n",
       "40.0      5611\n",
       "41.0      4994\n",
       "42.0      4649\n",
       "43.0      4365\n",
       "44.0      4180\n",
       "45.0      3806\n",
       "46.0      3302\n",
       "47.0      3257\n",
       "48.0      2963\n",
       "49.0      2613\n",
       "50.0      2407\n",
       "51.0      2115\n",
       "52.0      1896\n",
       "53.0      1650\n",
       "54.0      1396\n",
       "55.0      1170\n",
       "56.0      1030\n",
       "57.0       840\n",
       "58.0       715\n",
       "59.0       601\n",
       "60.0       479\n",
       "Name: IN_CERTIFICADO, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maior_idade = sort[['NU_IDADE', 'IN_CERTIFICADO']]\n",
    "maior_idade = maior_idade[maior_idade['NU_IDADE']>=18]\n",
    "maior_idade = maior_idade[maior_idade['NU_IDADE']<=60]\n",
    "maior_idade = maior_idade.groupby('NU_IDADE')['IN_CERTIFICADO'].count()\n",
    "maior_idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 0.1345902716068462),\n",
       " (19, 0.13143872113676733),\n",
       " (20, 0.11302806807224205),\n",
       " (21, 0.11468770672991593),\n",
       " (22, 0.11350154235545361),\n",
       " (23, 0.12057146980301608),\n",
       " (24, 0.12204437447155894),\n",
       " (25, 0.12553436927461492),\n",
       " (26, 0.13034419201025765),\n",
       " (27, 0.13469180297569541),\n",
       " (28, 0.13844875058444595),\n",
       " (29, 0.14045523520485584),\n",
       " (30, 0.13721817551161983),\n",
       " (31, 0.14125560538116591),\n",
       " (32, 0.14453510266296063),\n",
       " (33, 0.15058095952023989),\n",
       " (34, 0.1580368334779674),\n",
       " (35, 0.1552308343922067),\n",
       " (36, 0.16796923458627963),\n",
       " (37, 0.17650165016501651),\n",
       " (38, 0.18357771260997066),\n",
       " (39, 0.1925615842859443),\n",
       " (40, 0.21440028515416146),\n",
       " (41, 0.21605927112535042),\n",
       " (42, 0.22262852226285224),\n",
       " (43, 0.23711340206185566),\n",
       " (44, 0.23684210526315788),\n",
       " (45, 0.24540199684708355),\n",
       " (46, 0.26165960024227741),\n",
       " (47, 0.23334356770033773),\n",
       " (48, 0.2372595342558218),\n",
       " (49, 0.2533486414083429),\n",
       " (50, 0.23971749065226422),\n",
       " (51, 0.24018912529550829),\n",
       " (52, 0.23364978902953587),\n",
       " (53, 0.24424242424242423),\n",
       " (54, 0.24212034383954154),\n",
       " (55, 0.24273504273504273),\n",
       " (56, 0.2650485436893204),\n",
       " (57, 0.24166666666666667),\n",
       " (58, 0.27412587412587414),\n",
       " (59, 0.24625623960066556),\n",
       " (60, 0.27766179540709812)]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 0\n",
    "for j in maior_idade:\n",
    "    T[i] = (i+18, T[i]/j)\n",
    "    i+=1\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GE46/9TZbdh5qU43MmuOAYNagsWPHp1Vu1ukcEMwadO7g3GmVm3U6BwSzBm1YeSFzTx14\nX9ncUwfYsPLCNtXIrDluVDZr0ETDsXsZWa9wQDBrwtplww4A1jOcMjIzM8B3CGaFPPjM+oUDglkO\nDz6zfuKUkVkODz6zfuKAYJbDg8+snzggmOXw4DPrJw4IZjk8+Mz6iRuVzXJ48Jn1EwcEM/K7lnrw\nmfWLUikjSaskHZI0ImljnfWXSHpG0klJV2XKPy1pX+b1a0lr07p7JL2YWbd05r6WWXkTXUuPHjtO\n8F7X0kf2Hm131cxmVWFAkDQAbAWuAJYAV0taMmmzl4HrgO9lCyNiV0QsjYilwKXAG8CPMptsmFgf\nEfsa/xpmjXPXUrOaMimj5cBIRBwGkLQdWAMcnNggIl5K697JOc5VwGMR8UbDtTVrAXctNaspkzIa\nBo5klkdT2XStA74/qexmSc9Kuk3SaQ0c06xp7lpqVlMmIKhOWUznQyTNAy4CdmaKNwEfAS4GzgJu\nmmLf9ZKqkqrj4+PT+VizUty11KymTEAYBRZklucDY9P8nM8BD0fEWxMFEfFK1JwA7qaWmvqAiNgW\nEZWIqAwNDU3zY82KrV02zC1XXsTw4FwEDA/O5ZYrL3LPIus7ZdoQdgOLJS0CjlJL/Xxhmp9zNbU7\ngndJmhcRr0gSsBZ4bprHNJsx7lpqVuIOISJOAjdQS/c8D9wfEQckbZa0GkDSxZJGgc8Cd0o6MLG/\npPOp3WH8zaRD3ytpP7AfOAf40+a/jpmZNUoR02oOaKtKpRLVarXd1bAu5GcaWD+TtCciKkXbeaSy\n9Tw/08CsHE9uZz3PA8/MynFAsJ7ngWdm5TggWM/zwDOzchwQrOd54JlZOW5Utp7nZxqYleOAYH3B\nA8/MijllZGZmgAOCmZklThlZV/GIY7PWcUCwruERx2at5ZSRdQ2PODZrLd8hWNcoGnHsdJJZc3yH\nYF0jb8TxRDrp6LHjBO+lkx7Ze3R2K2nWxRwQrGvkjTh2OsmseQ4I1jXyHnXpCezMmuc2BOsqU404\nPndwLkfrXPw9gZ1ZeaXuECStknRI0oikjXXWXyLpGUknJV01ad3bkval145M+SJJT0t6QdJ9kuY0\n/3WsFzyy9ygrbn2SRRt/wIpbnyzVDuAJ7MyaVxgQJA0AW4ErgCXA1ZKWTNrsZeA64Ht1DnE8Ipam\n1+pM+TeB2yJiMfA6cH0D9bce02jjcF46yczKKZMyWg6MRMRhAEnbgTXAwYkNIuKltO6dMh8qScCl\nwBdS0XeA/wzcUbLe1qPyGoeLLu6ewM6sOWVSRsPAkczyaCor60OSqpKekrQ2lZ0NHIuIkw0e03qU\nG4fN2qfMHYLqlMU0PmNhRIxJugB4UtJ+4JdljylpPbAeYOHChdP4WOtGbhw2a58ydwijwILM8nxg\nrOwHRMRY+vcw8GNgGfALYFDSRECa8pgRsS0iKhFRGRoaKvux1qXcOGzWPmUCwm5gceoVNAdYB+wo\n2AcASWdKOi29PwdYARyMiAB2ARM9kq4FHp1u5a17TdWTyI3DZu2j2rW5YCPp3wC3AwPAXRFxs6TN\nQDUidki6GHgYOBP4NfDziPiYpH8B3Am8Qy343B4R307HvADYDpwF7AWuiYgTefWoVCpRrVYb/KrW\nKSbPWgq1uwBf+M1aQ9KeiKgUblcmIHQKB4TesOLWJ+u2EwwPzuVvN17ahhqZ9bayAcFTV9isc08i\ns87kqSusJfKmonZPIrPO5DsEy9XINBJFo43dk8isMzkg2JQanUaiaCpq9yQy60xOGdmU6Z1Gp5Eo\n00bgaSbMOo8DQp/Le3B9o4+sdBuBWXdyyqjP5d0FNPrISrcRmHUnB4Q+kNcwnHcX0OgjK91GYNad\nej5llNf9sR/kpYSK0jsT56ne+fuD+/bV/byJAOM2ArPu09MBoehi2A+KfslvWHlh3WkkJtI7fmSl\nWf/o6ZRRUffHflDUMNxoesftBGa9p6fvEBrtJdNLyvySbyS9k5dOMrPu1NMBIe9i2GvppKmCW1FK\nqBluJzDrLT2dMmq0l0y3yesC6h4/ZlZWT98hNNNLppsUNRz7l7yZldHTAQH6o5eMp5M2s5nQ0ymj\nPL3USyZvRLGZWVmlAoKkVZIOSRqRtLHO+kskPSPppKSrMuVLJf1E0gFJz0r6fGbdPZJelLQvvZbO\nzFcqpxtz61ONOO6l4GZm7VOYMpI0AGwFPgOMArsl7YiIg5nNXgauA/5w0u5vAF+MiBcknQvskbQz\nIo6l9Rsi4sFmv0Sjuim3XqZXlLuAmlkzyrQhLAdGIuIwgKTtwBrg3YAQES+lde9kd4yIn2bej0l6\nFRgCjmHT4oZjM2u1MimjYeBIZnk0lU2LpOXAHOBnmeKbUyrpNkmnTbHfeklVSdXx8fHpfmzXmSot\n5IZjM2u1MgFBdcpiOh8iaR7wF8CXImLiLmIT8BHgYuAs4KZ6+0bEtoioRERlaGhoOh/bdfLGE7jh\n2MxarUxAGAUWZJbnA2NlP0DSGcAPgK9HxFMT5RHxStScAO6mlprqeXlTUeelhdxwbGatVqYNYTew\nWNIi4CiwDvhCmYNLmgM8DHw3Ih6YtG5eRLwiScBa4Llp1bwLFTUM56WF3HBsZq1WGBAi4qSkG4Cd\nwABwV0QckLQZqEbEDkkXU7vwnwn8nqQ/joiPAZ8DLgHOlnRdOuR1EbEPuFfSELWU1D7gyzP95TpN\nUcNw0WA5NxybWSuVGqkcET8Efjip7I8y73dTSyVN3u8vgb+c4piXTqumPaCoYbiVE9GZmRXp25HK\n7VDUMNyNg+XMrHf0/FxGnaTMHYDTQmbWLg4IDWrk4TpuGDazTuaA0ICi3kJ5wcJ3AGbWqRwQckx1\nYS96uE4vPYnNzPqHA8IU8u4C8noLFXUtNTPrVO5lNIW8C3tebyHPOWRm3coBYQp5F/a8aSQ855CZ\ndSsHhCnkXdjzxgt4ziEz61ZuQ5hC0ZiBqXoLuWupmXUrB4QpNHNhd9dSM+tGDgg5fGE3s37iNgQz\nMwMcEMzMLHFAMDMzwAHBzMySUgFB0ipJhySNSNpYZ/0lkp6RdFLSVZPWXSvphfS6NlP+25L2p2P+\nWXqUppmZtUlhQJA0AGwFrgCWAFdLWjJps5eB64DvTdr3LOAbwCeA5cA3JJ2ZVt8BrAcWp9eqhr+F\nmZk1rcwdwnJgJCIOR8SbwHZgTXaDiHgpIp4F3pm070rg8Yh4LSJeBx4HVkmaB5wRET+JiAC+C6xt\n9suYmVnjygSEYeBIZnk0lZUx1b7D6X0jxzQzsxYoExDq5faj5PGn2rf0MSWtl1SVVB0fHy/5sWZm\nNl1lAsIosCCzPB8YK3n8qfYdTe8LjxkR2yKiEhGVoaGhkh9rZmbTVSYg7AYWS1okaQ6wDthR8vg7\ngcslnZkaky8HdkbEK8CvJH0y9S76IvBoA/U3M7MZUhgQIuIkcAO1i/vzwP0RcUDSZkmrASRdLGkU\n+Cxwp6QDad/XgD+hFlR2A5tTGcBXgD8HRoCfAY/N6DczM7NpUa2TT3eoVCpRrVbbXQ0zs64iaU9E\nVIq280hlMzMDHBDMzCxxQDAzM8ABwczMEgcEMzMDHBDMzCzpqm6nksaBf2jDR58D/KINn9stfH7y\n+fwU8znK1+z5OS8iCqd66KqA0C6SqmX68PYrn598Pj/FfI7yzdb5ccrIzMwABwQzM0scEMrZ1u4K\ndDifn3w+P8V8jvLNyvlxG4KZmQG+QzAzs8QBIUPSAkm7JD0v6YCk30/lZ0l6XNIL6d8z213XdpH0\nIUl/J+nv0zn641S+SNLT6Rzdl56d0bckDUjaK+mv07LPTyLpJUn7Je2TVE1l/hvLkDQo6UFJ/ztd\nj/75bJwjB4T3Own8h4j4KPBJ4KuSlgAbgSciYjHwRFruVyeASyPiN4GlwCpJnwS+CdyWztHrwPVt\nrGMn+H1qzw+Z4PPzfp+OiKWZrpT+G3u//wr8t4j4CPCb1P5favk5ckDIiIhXIuKZ9P5X1P4jDANr\ngO+kzb4DrG1PDdsvav5vWjw1vQK4FHgwlff1OZI0H/gdag+AIj0V0Ocnn//GEklnAJcA3waIiDcj\n4hizcI4cEKYg6XxgGfA08E/TYz9J//6T9tWs/VI6ZB/wKvA4tSfeHUtP14PaM7OH21W/DnA78B+B\nd9Ly2fj8ZAXwI0l7JK1PZf4be88FwDhwd0o7/rmkDzML58gBoQ5JpwN/Bfz7iPhlu+vTaSLi7YhY\nCswHlgMfrbfZ7NaqM0j6XeDViNiTLa6zaV+en2RFRPwWcAW1tOwl7a5QhzkF+C3gjohYBvw/ZimF\n5oAwiaRTqQWDeyPioVT8fyTNS+vnUftl3PfSbeyPqbW3DEo6Ja2aD4y1q15ttgJYLeklYDu1VNHt\n+Py8KyLG0r+vAg9T+1Hhv7H3jAKjEfF0Wn6QWoBo+TlyQMhIud5vA89HxH/JrNoBXJveXws8Ott1\n6xSShiQNpvdzgcuotbXsAq5Km/XtOYqITRExPyLOB9YBT0bEv8XnBwBJH5b0jyfeA5cDz+G/sXdF\nxM+BI5IuTEX/GjjILJwjD0zLkPQvgf8B7Oe9/O9/otaOcD+wEHgZ+GxEvNaWSraZpI9Ta9AaoPaD\n4v6I2CzpAmq/iM8C9gLXRMSJ9tW0/ST9K+API+J3fX5q0nl4OC2eAnwvIm6WdDb+G3uXpKXUOiXM\nAQ4DXyL9vdHCc+SAYGZmgFNGZmaWOCCYmRnggGBmZokDgpmZAQ4IZmaWOCCYmRnggGBmZokDgpmZ\nAfD/AdS6QRuJx19vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2cb74ee0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(*zip(*T))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}