Projeto_Final_Big_Data_Science.ipynb

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    " ## Projeto final - ENEM\n",
    "---\n",
    "\n",
    "### Introdução\n",
    " \n",
    "O Exame Nacional do Ensino Médio (Enem), tem como objetivo avaliar o desempenho escolar dos estudantes ao término da educação básica e desde 2009 passou a ser utilizado como mecanismo de acesso à educação superior.\n",
    "\n",
    "No Enem, para medir o conhecimento do participante, a prova é elaborada com 45 questões em cada área do conhecimento (Ciências da Natureza,Ciências Humanas, Linguagens e Códigos Matemática), além de redação.É importante ressaltar que no exame não existe um limite de padrão inferior ou superior, ou seja, as proficiências dos participantes não variam entre zero e mil. Os valores máximos e mínimos de cada prova dependem das características dos itens selecionados, exceto a redação que tem valor máximo de mil.\n",
    "    \n",
    "Sobre a análise das notas, deve-se saber que estas não se baseiam na quantitade de questões e sim na composição da questão, utilizando o  modelo de Teoria de Resposta ao Item (TRI) , modelo matemático capaz de representar a relação entre a probabilidade de o participante responder corretamente a uma questão, seu conhecimento na área em que está sendo avaliado e as características (parâmetros) dos itens. Um aspecto importante da TRI é que ela considera a particularidade de cada item.\n",
    "\n",
    "A proficiência é verificada com apoio na análise do perfil das respostas, levando em conta a coerência das respostas. Se o participanteacerta a questão de um nível acima, que utiliza conhecimentos de um nível abaixo, podemosinferir que não existe coerência nas respostas e, dessa forma, os acertos acrescentarãomenos na nota do participante.\n",
    "### Objetivos \n",
    "\n",
    "##### O objetivo dessa análise é explorar os dados do ENEM 2021 de forma a visualizar tendências. Os pontos analisados serão:\n",
    "\n",
    "1. Analisar a influência do sexo nas notas\n",
    "2. Analisar a influência da conclusão do ensino médio nas notas\n",
    "3. Analisar a influência da renda familiar nas notas \n",
    "4. Analisar a influência dos Estados nas notas\n",
    "    \n",
    "###### Os dados e informações aqui utilizados  tem origem no Instituto nacional de Estudos e Pesquisas educacionais Anísio Teixeira -INEP, podendo serem acessados em:\n",
    "https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65818e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statistics  as sts\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78631475",
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7ebf23",
   "metadata": {},
   "source": [
    "##### Importação do csv com os microdados do enem 2021, encontrados no site do INEP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea536f1e",
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   "outputs": [
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       "<p>2 rows × 76 columns</p>\n",
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       "   NU_INSCRICAO  NU_ANO  TP_FAIXA_ETARIA TP_SEXO  TP_ESTADO_CIVIL  \\\n",
       "0  210053865474    2021                5       F                1   \n",
       "1  210052384164    2021               12       M                1   \n",
       "\n",
       "   TP_COR_RACA  TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  TP_ESCOLA  \\\n",
       "0            1                 1                1                3          1   \n",
       "1            1                 1                1               11          1   \n",
       "\n",
       "   ...  Q016  Q017  Q018 Q019  Q020 Q021  Q022  Q023  Q024  Q025  \n",
       "0  ...     A     A     A    B     A    A     B     A     B     B  \n",
       "1  ...     A     A     A    B     A    A     C     A     A     A  \n",
       "\n",
       "[2 rows x 76 columns]"
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   "source": [
    "db = pd.read_csv(\"C:\\\\Users\\la-re\\Downloads\\microdados_enem_2021_2\\DADOS\\MICRODADOS_ENEM_2021.csv\", encoding='ISO-8859-1',sep = ';')\n",
    "\n",
    "db.head(2)\n"
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   "id": "7ca1eedf",
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    "db_2021_selec = db.drop(columns=['NU_INSCRICAO','TX_RESPOSTAS_CN','TX_RESPOSTAS_CH','TX_RESPOSTAS_LC','TX_RESPOSTAS_MT','TX_GABARITO_CN','TX_GABARITO_CH','TX_GABARITO_LC','TX_GABARITO_MT','CO_PROVA_MT','CO_PROVA_LC','CO_PROVA_CN',\n",
    "'CO_PROVA_CH','CO_MUNICIPIO_PROVA','NO_MUNICIPIO_PROVA','CO_UF_PROVA','CO_MUNICIPIO_ESC','NO_MUNICIPIO_ESC','CO_UF_ESC','SG_UF_ESC','TP_DEPENDENCIA_ADM_ESC','TP_LOCALIZACAO_ESC',\n",
    "'TP_SIT_FUNC_ESC','Q008','Q009','Q010','Q011','Q012','Q013','Q014','Q015','Q016','Q017','Q018','Q019','Q020','Q021','Q022','Q023','Q024','Q025'])"
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       "   NU_ANO  TP_FAIXA_ETARIA TP_SEXO  TP_ESTADO_CIVIL  TP_COR_RACA  \\\n",
       "0    2021                5       F                1            1   \n",
       "1    2021               12       M                1            1   \n",
       "2    2021               13       F                3            1   \n",
       "3    2021                3       M                1            3   \n",
       "4    2021                2       F                1            3   \n",
       "\n",
       "   TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  TP_ESCOLA  TP_ENSINO  \\\n",
       "0                 1                1                3          1        NaN   \n",
       "1                 1                1               11          1        NaN   \n",
       "2                 1                1               15          1        NaN   \n",
       "3                 1                2                0          2        1.0   \n",
       "4                 1                2                0          2        1.0   \n",
       "\n",
       "   ...  NU_NOTA_COMP4 NU_NOTA_COMP5  NU_NOTA_REDACAO  Q001  Q002  Q003  Q004  \\\n",
       "0  ...          180.0         200.0            760.0     F     F     B     B   \n",
       "1  ...          120.0          80.0            560.0     B     B     B     B   \n",
       "2  ...            NaN           NaN              NaN     B     C     C     B   \n",
       "3  ...          200.0         160.0            780.0     B     B     B     B   \n",
       "4  ...          180.0         180.0            780.0     D     E     F     D   \n",
       "\n",
       "   Q005  Q006  Q007  \n",
       "0   3.0     D     A  \n",
       "1   3.0     B     A  \n",
       "2   3.0     C     A  \n",
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       "4   4.0     C     A  \n",
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  {
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   "id": "59412521",
   "metadata": {},
   "source": [
    "### Análise geral dos dados - Enem 2021:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e574ce18",
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      "DATASET SHAPE:  (3389832, 35) (linhas e colunas) \n",
      "\n",
      "--------------------------------------------------\n",
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      "RangeIndex: 3389832 entries, 0 to 3389831\n",
      "Data columns (total 35 columns):\n",
      " #   Column             Dtype  \n",
      "---  ------             -----  \n",
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      " 1   TP_FAIXA_ETARIA    int64  \n",
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      " 3   TP_ESTADO_CIVIL    int64  \n",
      " 4   TP_COR_RACA        int64  \n",
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      " 6   TP_ST_CONCLUSAO    int64  \n",
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      " 15  TP_PRESENCA_MT     int64  \n",
      " 16  NU_NOTA_CN         float64\n",
      " 17  NU_NOTA_CH         float64\n",
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      "dtypes: float64(13), int64(14), object(8)\n",
      "memory usage: 905.2+ MB\n",
      "None\n",
      "NUMBER OF UNIQUE VALUES PER FEATURE: \n",
      "\n",
      "NU_ANO                  1\n",
      "TP_FAIXA_ETARIA        20\n",
      "TP_SEXO                 2\n",
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      "TP_ESCOLA               3\n",
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      "SG_UF_PROVA            27\n",
      "TP_PRESENCA_CN          3\n",
      "TP_PRESENCA_CH          3\n",
      "TP_PRESENCA_LC          3\n",
      "TP_PRESENCA_MT          3\n",
      "NU_NOTA_CN           5030\n",
      "NU_NOTA_CH           4874\n",
      "NU_NOTA_LC           4728\n",
      "NU_NOTA_MT           6149\n",
      "TP_LINGUA               2\n",
      "TP_STATUS_REDACAO       8\n",
      "NU_NOTA_COMP1          11\n",
      "NU_NOTA_COMP2          10\n",
      "NU_NOTA_COMP3          11\n",
      "NU_NOTA_COMP4          11\n",
      "NU_NOTA_COMP5          11\n",
      "NU_NOTA_REDACAO        50\n",
      "Q001                    8\n",
      "Q002                    8\n",
      "Q003                    6\n",
      "Q004                    6\n",
      "Q005                   20\n",
      "Q006                   17\n",
      "Q007                    4\n",
      "dtype: int64\n",
      "NULL VALUES PER FEATURE\n",
      "NU_ANO                     0\n",
      "TP_FAIXA_ETARIA            0\n",
      "TP_SEXO                    0\n",
      "TP_ESTADO_CIVIL            0\n",
      "TP_COR_RACA                0\n",
      "TP_NACIONALIDADE           0\n",
      "TP_ST_CONCLUSAO            0\n",
      "TP_ANO_CONCLUIU            0\n",
      "TP_ESCOLA                  0\n",
      "TP_ENSINO            2293004\n",
      "IN_TREINEIRO               0\n",
      "SG_UF_PROVA                0\n",
      "TP_PRESENCA_CN             0\n",
      "TP_PRESENCA_CH             0\n",
      "TP_PRESENCA_LC             0\n",
      "TP_PRESENCA_MT             0\n",
      "NU_NOTA_CN           1143988\n",
      "NU_NOTA_CH           1011453\n",
      "NU_NOTA_LC           1011453\n",
      "NU_NOTA_MT           1143988\n",
      "TP_LINGUA                  0\n",
      "TP_STATUS_REDACAO    1011453\n",
      "NU_NOTA_COMP1        1011453\n",
      "NU_NOTA_COMP2        1011453\n",
      "NU_NOTA_COMP3        1011453\n",
      "NU_NOTA_COMP4        1011453\n",
      "NU_NOTA_COMP5        1011453\n",
      "NU_NOTA_REDACAO      1011453\n",
      "Q001                       2\n",
      "Q002                       2\n",
      "Q003                       2\n",
      "Q004                       2\n",
      "Q005                       2\n",
      "Q006                       2\n",
      "Q007                       2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def show_info(data):\n",
    "    print('DATASET SHAPE: ', data.shape, '(linhas e colunas) \\n')\n",
    "    print('-'*50)\n",
    "    print('FEATURE DATA TYPES:')\n",
    "    print(data.info())\n",
    "    print('NUMBER OF UNIQUE VALUES PER FEATURE:', '\\n')\n",
    "    print(data.nunique())\n",
    "    print('NULL VALUES PER FEATURE')\n",
    "    print(data.isnull().sum())\n",
    "show_info(db_2021_selec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06607980",
   "metadata": {},
   "source": [
    "###### Obs: Apesar do tipo de dados ser inteiro em algumas colunas, o significado desse valor deve ser analisado com cuidado, pois ele  depende da legenda e pode causar análises equivocadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec61474d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2245844\n",
       "0    1142539\n",
       "2       1449\n",
       "Name: TP_PRESENCA_CN, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifaçao do NULL VALUES PER FEATURE das notas em cada matéria como os faltantes + os eliminados.\n",
    "db_2021_selec['TP_PRESENCA_CN'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe0d08a",
   "metadata": {},
   "source": [
    "###### Serão apagadas as linhas dos valores NaN das notas 'NU_NOTA_CN','NU_NOTA_CH','NU_NOTA_LC','NU_NOTA_MT' e consequentemente os NaN presentes nas nota de redação também serão excluidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "676f7bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_2021_selec_limpos = db_2021_selec.dropna(subset = {\"NU_NOTA_CN\",'NU_NOTA_CH','NU_NOTA_LC','NU_NOTA_MT'}) # exclusão das linhas nulas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a1b6932",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_ANO</th>\n",
       "      <th>TP_FAIXA_ETARIA</th>\n",
       "      <th>TP_ESTADO_CIVIL</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>TP_NACIONALIDADE</th>\n",
       "      <th>TP_ST_CONCLUSAO</th>\n",
       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>TP_ENSINO</th>\n",
       "      <th>IN_TREINEIRO</th>\n",
       "      <th>...</th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>TP_LINGUA</th>\n",
       "      <th>TP_STATUS_REDACAO</th>\n",
       "      <th>NU_NOTA_COMP1</th>\n",
       "      <th>NU_NOTA_COMP2</th>\n",
       "      <th>NU_NOTA_COMP3</th>\n",
       "      <th>NU_NOTA_COMP4</th>\n",
       "      <th>NU_NOTA_COMP5</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>Q005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2238107.0</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>828996.000000</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238107e+06</td>\n",
       "      <td>2.238106e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>4.501463e+00</td>\n",
       "      <td>1.032940e+00</td>\n",
       "      <td>1.985461e+00</td>\n",
       "      <td>1.026737e+00</td>\n",
       "      <td>1.715638e+00</td>\n",
       "      <td>2.004855e+00</td>\n",
       "      <td>1.458762e+00</td>\n",
       "      <td>1.004976</td>\n",
       "      <td>1.641445e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.351971e+02</td>\n",
       "      <td>4.290997e-01</td>\n",
       "      <td>1.112739e+00</td>\n",
       "      <td>1.206923e+02</td>\n",
       "      <td>1.399525e+02</td>\n",
       "      <td>1.168930e+02</td>\n",
       "      <td>1.299222e+02</td>\n",
       "      <td>1.150862e+02</td>\n",
       "      <td>6.225462e+02</td>\n",
       "      <td>3.778074e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.537410e+00</td>\n",
       "      <td>3.577890e-01</td>\n",
       "      <td>1.022828e+00</td>\n",
       "      <td>2.070285e-01</td>\n",
       "      <td>7.411084e-01</td>\n",
       "      <td>3.795310e+00</td>\n",
       "      <td>6.392017e-01</td>\n",
       "      <td>0.070364</td>\n",
       "      <td>3.704067e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.105777e+02</td>\n",
       "      <td>4.949477e-01</td>\n",
       "      <td>7.174077e-01</td>\n",
       "      <td>3.243648e+01</td>\n",
       "      <td>4.429136e+01</td>\n",
       "      <td>3.510357e+01</td>\n",
       "      <td>3.745456e+01</td>\n",
       "      <td>5.923479e+01</td>\n",
       "      <td>1.839687e+02</td>\n",
       "      <td>1.306132e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.448000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.155000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>6.200000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.140000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.400000e+02</td>\n",
       "      <td>1.800000e+02</td>\n",
       "      <td>1.400000e+02</td>\n",
       "      <td>1.600000e+02</td>\n",
       "      <td>1.600000e+02</td>\n",
       "      <td>7.400000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>9.531000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          NU_ANO  TP_FAIXA_ETARIA  TP_ESTADO_CIVIL   TP_COR_RACA  \\\n",
       "count  2238107.0     2.238107e+06     2.238107e+06  2.238107e+06   \n",
       "mean      2021.0     4.501463e+00     1.032940e+00  1.985461e+00   \n",
       "std          0.0     3.537410e+00     3.577890e-01  1.022828e+00   \n",
       "min       2021.0     1.000000e+00     0.000000e+00  0.000000e+00   \n",
       "25%       2021.0     2.000000e+00     1.000000e+00  1.000000e+00   \n",
       "50%       2021.0     3.000000e+00     1.000000e+00  2.000000e+00   \n",
       "75%       2021.0     5.000000e+00     1.000000e+00  3.000000e+00   \n",
       "max       2021.0     2.000000e+01     4.000000e+00  6.000000e+00   \n",
       "\n",
       "       TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU     TP_ESCOLA  \\\n",
       "count      2.238107e+06     2.238107e+06     2.238107e+06  2.238107e+06   \n",
       "mean       1.026737e+00     1.715638e+00     2.004855e+00  1.458762e+00   \n",
       "std        2.070285e-01     7.411084e-01     3.795310e+00  6.392017e-01   \n",
       "min        0.000000e+00     1.000000e+00     0.000000e+00  1.000000e+00   \n",
       "25%        1.000000e+00     1.000000e+00     0.000000e+00  1.000000e+00   \n",
       "50%        1.000000e+00     2.000000e+00     0.000000e+00  1.000000e+00   \n",
       "75%        1.000000e+00     2.000000e+00     2.000000e+00  2.000000e+00   \n",
       "max        4.000000e+00     4.000000e+00     1.500000e+01  3.000000e+00   \n",
       "\n",
       "           TP_ENSINO  IN_TREINEIRO  ...    NU_NOTA_MT     TP_LINGUA  \\\n",
       "count  828996.000000  2.238107e+06  ...  2.238107e+06  2.238107e+06   \n",
       "mean        1.004976  1.641445e-01  ...  5.351971e+02  4.290997e-01   \n",
       "std         0.070364  3.704067e-01  ...  1.105777e+02  4.949477e-01   \n",
       "min         1.000000  0.000000e+00  ...  0.000000e+00  0.000000e+00   \n",
       "25%         1.000000  0.000000e+00  ...  4.448000e+02  0.000000e+00   \n",
       "50%         1.000000  0.000000e+00  ...  5.155000e+02  0.000000e+00   \n",
       "75%         1.000000  0.000000e+00  ...  6.140000e+02  1.000000e+00   \n",
       "max         2.000000  1.000000e+00  ...  9.531000e+02  1.000000e+00   \n",
       "\n",
       "       TP_STATUS_REDACAO  NU_NOTA_COMP1  NU_NOTA_COMP2  NU_NOTA_COMP3  \\\n",
       "count       2.238107e+06   2.238107e+06   2.238107e+06   2.238107e+06   \n",
       "mean        1.112739e+00   1.206923e+02   1.399525e+02   1.168930e+02   \n",
       "std         7.174077e-01   3.243648e+01   4.429136e+01   3.510357e+01   \n",
       "min         1.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "25%         1.000000e+00   1.000000e+02   1.200000e+02   1.000000e+02   \n",
       "50%         1.000000e+00   1.200000e+02   1.200000e+02   1.200000e+02   \n",
       "75%         1.000000e+00   1.400000e+02   1.800000e+02   1.400000e+02   \n",
       "max         9.000000e+00   2.000000e+02   2.000000e+02   2.000000e+02   \n",
       "\n",
       "       NU_NOTA_COMP4  NU_NOTA_COMP5  NU_NOTA_REDACAO          Q005  \n",
       "count   2.238107e+06   2.238107e+06     2.238107e+06  2.238106e+06  \n",
       "mean    1.299222e+02   1.150862e+02     6.225462e+02  3.778074e+00  \n",
       "std     3.745456e+01   5.923479e+01     1.839687e+02  1.306132e+00  \n",
       "min     0.000000e+00   0.000000e+00     0.000000e+00  1.000000e+00  \n",
       "25%     1.200000e+02   8.000000e+01     5.200000e+02  3.000000e+00  \n",
       "50%     1.200000e+02   1.200000e+02     6.200000e+02  4.000000e+00  \n",
       "75%     1.600000e+02   1.600000e+02     7.400000e+02  4.000000e+00  \n",
       "max     2.000000e+02   2.000000e+02     1.000000e+03  2.000000e+01  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_2021_selec_limpos.describe() #resumo das variaveis quantitativas. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b673882f",
   "metadata": {},
   "source": [
    "TP_FAIXA_ETARIA: 75% = 5 ou seja, a maioria dos candidados tem até 20 anos, segundo a legenda dos microdados.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c11a2cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET SHAPE:  (2238107, 35) (linhas e colunas) \n",
      "\n",
      "--------------------------------------------------\n",
      "FEATURE DATA TYPES:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2238107 entries, 1 to 3389830\n",
      "Data columns (total 35 columns):\n",
      " #   Column             Dtype  \n",
      "---  ------             -----  \n",
      " 0   NU_ANO             int64  \n",
      " 1   TP_FAIXA_ETARIA    int64  \n",
      " 2   TP_SEXO            object \n",
      " 3   TP_ESTADO_CIVIL    int64  \n",
      " 4   TP_COR_RACA        int64  \n",
      " 5   TP_NACIONALIDADE   int64  \n",
      " 6   TP_ST_CONCLUSAO    int64  \n",
      " 7   TP_ANO_CONCLUIU    int64  \n",
      " 8   TP_ESCOLA          int64  \n",
      " 9   TP_ENSINO          float64\n",
      " 10  IN_TREINEIRO       int64  \n",
      " 11  SG_UF_PROVA        object \n",
      " 12  TP_PRESENCA_CN     int64  \n",
      " 13  TP_PRESENCA_CH     int64  \n",
      " 14  TP_PRESENCA_LC     int64  \n",
      " 15  TP_PRESENCA_MT     int64  \n",
      " 16  NU_NOTA_CN         float64\n",
      " 17  NU_NOTA_CH         float64\n",
      " 18  NU_NOTA_LC         float64\n",
      " 19  NU_NOTA_MT         float64\n",
      " 20  TP_LINGUA          int64  \n",
      " 21  TP_STATUS_REDACAO  float64\n",
      " 22  NU_NOTA_COMP1      float64\n",
      " 23  NU_NOTA_COMP2      float64\n",
      " 24  NU_NOTA_COMP3      float64\n",
      " 25  NU_NOTA_COMP4      float64\n",
      " 26  NU_NOTA_COMP5      float64\n",
      " 27  NU_NOTA_REDACAO    float64\n",
      " 28  Q001               object \n",
      " 29  Q002               object \n",
      " 30  Q003               object \n",
      " 31  Q004               object \n",
      " 32  Q005               float64\n",
      " 33  Q006               object \n",
      " 34  Q007               object \n",
      "dtypes: float64(13), int64(14), object(8)\n",
      "memory usage: 614.7+ MB\n",
      "None\n",
      "NUMBER OF UNIQUE VALUES PER FEATURE: \n",
      "\n",
      "NU_ANO                  1\n",
      "TP_FAIXA_ETARIA        20\n",
      "TP_SEXO                 2\n",
      "TP_ESTADO_CIVIL         5\n",
      "TP_COR_RACA             7\n",
      "TP_NACIONALIDADE        5\n",
      "TP_ST_CONCLUSAO         4\n",
      "TP_ANO_CONCLUIU        16\n",
      "TP_ESCOLA               3\n",
      "TP_ENSINO               2\n",
      "IN_TREINEIRO            2\n",
      "SG_UF_PROVA            27\n",
      "TP_PRESENCA_CN          1\n",
      "TP_PRESENCA_CH          1\n",
      "TP_PRESENCA_LC          1\n",
      "TP_PRESENCA_MT          1\n",
      "NU_NOTA_CN           5028\n",
      "NU_NOTA_CH           4863\n",
      "NU_NOTA_LC           4726\n",
      "NU_NOTA_MT           6148\n",
      "TP_LINGUA               2\n",
      "TP_STATUS_REDACAO       8\n",
      "NU_NOTA_COMP1          11\n",
      "NU_NOTA_COMP2          10\n",
      "NU_NOTA_COMP3          11\n",
      "NU_NOTA_COMP4          11\n",
      "NU_NOTA_COMP5          11\n",
      "NU_NOTA_REDACAO        50\n",
      "Q001                    8\n",
      "Q002                    8\n",
      "Q003                    6\n",
      "Q004                    6\n",
      "Q005                   20\n",
      "Q006                   17\n",
      "Q007                    4\n",
      "dtype: int64\n",
      "NULL VALUES PER FEATURE\n",
      "NU_ANO                     0\n",
      "TP_FAIXA_ETARIA            0\n",
      "TP_SEXO                    0\n",
      "TP_ESTADO_CIVIL            0\n",
      "TP_COR_RACA                0\n",
      "TP_NACIONALIDADE           0\n",
      "TP_ST_CONCLUSAO            0\n",
      "TP_ANO_CONCLUIU            0\n",
      "TP_ESCOLA                  0\n",
      "TP_ENSINO            1409111\n",
      "IN_TREINEIRO               0\n",
      "SG_UF_PROVA                0\n",
      "TP_PRESENCA_CN             0\n",
      "TP_PRESENCA_CH             0\n",
      "TP_PRESENCA_LC             0\n",
      "TP_PRESENCA_MT             0\n",
      "NU_NOTA_CN                 0\n",
      "NU_NOTA_CH                 0\n",
      "NU_NOTA_LC                 0\n",
      "NU_NOTA_MT                 0\n",
      "TP_LINGUA                  0\n",
      "TP_STATUS_REDACAO          0\n",
      "NU_NOTA_COMP1              0\n",
      "NU_NOTA_COMP2              0\n",
      "NU_NOTA_COMP3              0\n",
      "NU_NOTA_COMP4              0\n",
      "NU_NOTA_COMP5              0\n",
      "NU_NOTA_REDACAO            0\n",
      "Q001                       1\n",
      "Q002                       1\n",
      "Q003                       1\n",
      "Q004                       1\n",
      "Q005                       1\n",
      "Q006                       1\n",
      "Q007                       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "show_info(db_2021_selec_limpos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5f7bd39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.975872550616074"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Porcentagem de faltantes.\n",
    "((3389832-2238107)/3389832)*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a61b6403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    1381949\n",
       "M     856158\n",
       "Name: TP_SEXO, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_2021_selec_limpos['TP_SEXO'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62d9b02f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.746332950122586"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porc_fememinina=(1381949/2238107)*100\n",
    "porc_fememinina"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e69dca",
   "metadata": {},
   "source": [
    "**61% dos candidatos que fizeram a prova foram do sexo feminino**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b84832",
   "metadata": {},
   "source": [
    "## Comparação das notas entre os sexos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "74ca1a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1486575</th>\n",
       "      <td>F</td>\n",
       "      <td>867.1</td>\n",
       "      <td>748.2</td>\n",
       "      <td>683.9</td>\n",
       "      <td>842.6</td>\n",
       "      <td>740.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628415</th>\n",
       "      <td>F</td>\n",
       "      <td>867.1</td>\n",
       "      <td>722.7</td>\n",
       "      <td>699.3</td>\n",
       "      <td>871.7</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591848</th>\n",
       "      <td>M</td>\n",
       "      <td>867.1</td>\n",
       "      <td>742.9</td>\n",
       "      <td>673.2</td>\n",
       "      <td>944.1</td>\n",
       "      <td>940.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126029</th>\n",
       "      <td>F</td>\n",
       "      <td>867.1</td>\n",
       "      <td>814.3</td>\n",
       "      <td>659.7</td>\n",
       "      <td>935.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244402</th>\n",
       "      <td>M</td>\n",
       "      <td>867.1</td>\n",
       "      <td>733.7</td>\n",
       "      <td>676.4</td>\n",
       "      <td>948.8</td>\n",
       "      <td>920.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TP_SEXO  NU_NOTA_CN  NU_NOTA_CH  NU_NOTA_LC  NU_NOTA_MT  \\\n",
       "1486575       F       867.1       748.2       683.9       842.6   \n",
       "1628415       F       867.1       722.7       699.3       871.7   \n",
       "591848        M       867.1       742.9       673.2       944.1   \n",
       "2126029       F       867.1       814.3       659.7       935.0   \n",
       "244402        M       867.1       733.7       676.4       948.8   \n",
       "\n",
       "         NU_NOTA_REDACAO  TP_COR_RACA  \n",
       "1486575            740.0            1  \n",
       "1628415            840.0            1  \n",
       "591848             940.0            1  \n",
       "2126029            880.0            1  \n",
       "244402             920.0            1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_gerais5= db_2021_selec_limpos[['TP_SEXO','NU_NOTA_CN','NU_NOTA_CH','NU_NOTA_LC','NU_NOTA_MT','NU_NOTA_REDACAO','TP_COR_RACA']]\n",
    "notas_gerais5.sort_values(by='NU_NOTA_CN', ascending=False).head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1bf6b58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>TP_SEXO_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1486575</th>\n",
       "      <td>867.1</td>\n",
       "      <td>748.2</td>\n",
       "      <td>683.9</td>\n",
       "      <td>842.6</td>\n",
       "      <td>740.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628415</th>\n",
       "      <td>867.1</td>\n",
       "      <td>722.7</td>\n",
       "      <td>699.3</td>\n",
       "      <td>871.7</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591848</th>\n",
       "      <td>867.1</td>\n",
       "      <td>742.9</td>\n",
       "      <td>673.2</td>\n",
       "      <td>944.1</td>\n",
       "      <td>940.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126029</th>\n",
       "      <td>867.1</td>\n",
       "      <td>814.3</td>\n",
       "      <td>659.7</td>\n",
       "      <td>935.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244402</th>\n",
       "      <td>867.1</td>\n",
       "      <td>733.7</td>\n",
       "      <td>676.4</td>\n",
       "      <td>948.8</td>\n",
       "      <td>920.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_NOTA_CN  NU_NOTA_CH  NU_NOTA_LC  NU_NOTA_MT  NU_NOTA_REDACAO  \\\n",
       "1486575       867.1       748.2       683.9       842.6            740.0   \n",
       "1628415       867.1       722.7       699.3       871.7            840.0   \n",
       "591848        867.1       742.9       673.2       944.1            940.0   \n",
       "2126029       867.1       814.3       659.7       935.0            880.0   \n",
       "244402        867.1       733.7       676.4       948.8            920.0   \n",
       "\n",
       "         TP_COR_RACA  TP_SEXO_M  \n",
       "1486575            1          0  \n",
       "1628415            1          0  \n",
       "591848             1          1  \n",
       "2126029            1          0  \n",
       "244402             1          1  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#conversão em variável binária (0,1)\n",
    "db_dummies=pd.get_dummies(notas_gerais5,columns=['TP_SEXO'],drop_first=True) #feminino = 0 ,masculino =1\n",
    "\n",
    "#Cálculo da correlação entre as variáveis\n",
    "correlation4 = db_dummies.corr()\n",
    "plot = sns.heatmap(correlation4, annot = True, fmt=\".1f\", linewidths=.6)\n",
    "plot\n",
    "db_dummies.sort_values(by='NU_NOTA_CN', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743175f1",
   "metadata": {},
   "source": [
    "##### Significado dos valores de correlação\n",
    "* ρ = 0,9 a 1 (positivo ou negativo): correlação muito forte;\n",
    "* ρ = 0,7 a 09 (positivo ou negativo): correlação forte;\n",
    "* ρ = 0,5 a 0,7 (positivo ou negativo): correlação moderada;\n",
    "* ρ = 0,3 a 0,5 (positivo ou negativo): correlação fraca;\n",
    "* ρ = 0 a 0,3 (positivo ou negativo): não possui correlação."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f82dc67",
   "metadata": {},
   "source": [
    "Não houve correlação entre o sexo e as notas, mas houve relação entre as notas das matérias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2bd440fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">NU_NOTA_CN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1381949.0</td>\n",
       "      <td>483.205442</td>\n",
       "      <td>76.801419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.1</td>\n",
       "      <td>473.0</td>\n",
       "      <td>531.8</td>\n",
       "      <td>867.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>856158.0</td>\n",
       "      <td>505.936664</td>\n",
       "      <td>84.262653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441.3</td>\n",
       "      <td>499.8</td>\n",
       "      <td>565.7</td>\n",
       "      <td>867.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NU_NOTA_CN                                                        \n",
       "             count        mean        std  min    25%    50%    75%    max\n",
       "TP_SEXO                                                                   \n",
       "F        1381949.0  483.205442  76.801419  0.0  426.1  473.0  531.8  867.1\n",
       "M         856158.0  505.936664  84.262653  0.0  441.3  499.8  565.7  867.1"
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       "      <td>856158.0</td>\n",
       "      <td>534.273942</td>\n",
       "      <td>98.122727</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "        NU_NOTA_CH                                                        \n",
       "             count        mean        std  min    25%    50%    75%    max\n",
       "TP_SEXO                                                                   \n",
       "F        1381949.0  516.310264  91.321966  0.0  444.9  516.5  582.4  846.9\n",
       "M         856158.0  534.273942  98.122727  0.0  457.0  541.2  605.9  846.9"
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       "      <td>1381949.0</td>\n",
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       "      <td>556.6</td>\n",
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       "      <td>78.843505</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "        NU_NOTA_LC                                                        \n",
       "             count        mean        std  min    25%    50%    75%    max\n",
       "TP_SEXO                                                                   \n",
       "F        1381949.0  501.675265  77.458396  0.0  447.3  504.4  556.6  826.1\n",
       "M         856158.0  510.040547  78.843505  0.0  455.0  514.8  567.1  813.2"
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       "      <td>589.4</td>\n",
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       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>856158.0</td>\n",
       "      <td>559.882396</td>\n",
       "      <td>116.640181</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "        NU_NOTA_MT                                                         \n",
       "             count        mean         std  min    25%    50%    75%    max\n",
       "TP_SEXO                                                                    \n",
       "F        1381949.0  519.903830  103.743143  0.0  437.1  498.5  589.4  953.1\n",
       "M         856158.0  559.882396  116.640181  0.0  462.0  549.1  647.2  953.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#criando tabelas com as colunas nota e sexo.\n",
    "notas_CN= db_2021_selec_limpos[['TP_SEXO','NU_NOTA_CN']]\n",
    "notas_CH= db_2021_selec_limpos[['TP_SEXO','NU_NOTA_CH']]\n",
    "notas_LC= db_2021_selec_limpos[['TP_SEXO','NU_NOTA_LC']]\n",
    "notas_MT= db_2021_selec_limpos[['TP_SEXO','NU_NOTA_MT']]\n",
    "#descrição geral das notas de cada matéria\n",
    "display(notas_CN.groupby('TP_SEXO').describe())\n",
    "display(notas_CH.groupby('TP_SEXO').describe())\n",
    "display(notas_LC.groupby('TP_SEXO').describe())\n",
    "display(notas_MT.groupby('TP_SEXO').describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f7e9abb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#media e desvio padrao para criaçao de grafico\n",
    "media_desvio_CN = notas_CN.groupby('TP_SEXO').agg([np.mean, np.std])\n",
    "media_desvio_CH = notas_CH.groupby('TP_SEXO').agg([np.mean, np.std])\n",
    "media_desvio_LC = notas_LC.groupby('TP_SEXO').agg([np.mean, np.std])\n",
    "media_desvio_MT = notas_MT.groupby('TP_SEXO').agg([np.mean, np.std])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6ebff686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desvio_CN = media_desvio_CN['NU_NOTA_CN']\n",
    "desvio_CH = media_desvio_CH['NU_NOTA_CH']\n",
    "desvio_LC = media_desvio_LC['NU_NOTA_LC']\n",
    "desvio_MT = media_desvio_MT['NU_NOTA_MT']\n",
    " \n",
    "desvio_CN.plot(kind = \"barh\", y = \"mean\", legend = False,\n",
    "            title = \" Ciências da Natureza\",xerr = \"std\",)\n",
    "plt.xlabel('MÉDIA')\n",
    "plt.ylabel('SEXO')\n",
    "desvio_CH.plot(kind = \"barh\", y = \"mean\", legend = False,\n",
    "            title = \"Ciências Humanas\",xerr = \"std\")\n",
    "plt.xlabel('MÉDIA')\n",
    "plt.ylabel('SEXO')\n",
    "desvio_LC.plot(kind = \"barh\", y = \"mean\", legend = False,\n",
    "            title = \"Linguagens, Códigos e suas Tecnologias\",xerr = \"std\")\n",
    "plt.xlabel('MÉDIA')\n",
    "plt.ylabel('SEXO')\n",
    "desvio_MT.plot(kind = \"barh\", y = \"mean\", legend = False,\n",
    "            title = \"Matemática e suas tecnologias\", xerr = \"std\")\n",
    "plt.xlabel('MÉDIA')\n",
    "plt.ylabel('SEXO')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae7fb86",
   "metadata": {},
   "source": [
    "A média dos homens ficou maior em todas as matérias, mas considerando o desvio padrão (std) a diferença não é muito significativa. A matéria onde aconteceu uma maior diferença foi Matemática (NU_NOTA_MT), tendo sido observada uma diferença de 50 pontos analisando a média e de 57,8 analisando 75% das notas dos candidatos.\n",
    "\n",
    "A maior nota na matéria de Linguagens e Códigos foi do sexo feminino e em todas as outras matérias as notas maiores foram iguais entre os sexos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e693b3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">NU_NOTA_REDACAO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1381949.0</td>\n",
       "      <td>633.407224</td>\n",
       "      <td>182.574509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>856158.0</td>\n",
       "      <td>605.015102</td>\n",
       "      <td>184.855705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NU_NOTA_REDACAO                                                    \\\n",
       "                  count        mean         std  min    25%    50%    75%   \n",
       "TP_SEXO                                                                     \n",
       "F             1381949.0  633.407224  182.574509  0.0  540.0  620.0  760.0   \n",
       "M              856158.0  605.015102  184.855705  0.0  520.0  600.0  720.0   \n",
       "\n",
       "                 \n",
       "            max  \n",
       "TP_SEXO          \n",
       "F        1000.0  \n",
       "M        1000.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_redacao = db_2021_selec_limpos[['TP_SEXO','NU_NOTA_REDACAO']]\n",
    "notas_redacao.groupby('TP_SEXO').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6deef0d0",
   "metadata": {},
   "source": [
    "O mesmo foi observado nas notas das redações, mas dessa vez o melhor desempenho foi para as candidatas (sexo feminino), com uma diferença de 28 pontos analisando a média e de 40 pontos analisando os 75% dos candidatos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "456dadb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\la-re\\anaconda3\\lib\\site-packages (5.6.0)\n",
      "Requirement already satisfied: six in c:\\users\\la-re\\anaconda3\\lib\\site-packages (from plotly) (1.16.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\la-re\\anaconda3\\lib\\site-packages (from plotly) (8.0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly\n",
    "pd.options.plotting.backend = \"plotly\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85c1de0",
   "metadata": {},
   "source": [
    "### Ranking \n",
    "##### Para melhor visualização, segue abaixo os estados com as 10 maiores médias para cada matéria com sexo e Estado indicados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2fa867c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG_UF_PROVA</th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <th>M</th>\n",
       "      <td>527.092717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DF</th>\n",
       "      <th>M</th>\n",
       "      <td>526.200384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <th>M</th>\n",
       "      <td>525.575207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <th>M</th>\n",
       "      <td>524.232733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <th>M</th>\n",
       "      <td>519.037794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <th>M</th>\n",
       "      <td>518.794426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <th>M</th>\n",
       "      <td>518.450710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ</th>\n",
       "      <th>M</th>\n",
       "      <td>516.613657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <th>M</th>\n",
       "      <td>505.169101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <th>F</th>\n",
       "      <td>503.060067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NU_NOTA_CN\n",
       "SG_UF_PROVA TP_SEXO            \n",
       "SP          M        527.092717\n",
       "DF          M        526.200384\n",
       "SC          M        525.575207\n",
       "MG          M        524.232733\n",
       "RS          M        519.037794\n",
       "ES          M        518.794426\n",
       "PR          M        518.450710\n",
       "RJ          M        516.613657\n",
       "RN          M        505.169101\n",
       "SP          F        503.060067"
      ]
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       "      <th>SP</th>\n",
       "      <th>M</th>\n",
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       "    <tr>\n",
       "      <th>DF</th>\n",
       "      <th>M</th>\n",
       "      <td>556.381417</td>\n",
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       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <th>M</th>\n",
       "      <td>554.401978</td>\n",
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       "      <th>SC</th>\n",
       "      <th>M</th>\n",
       "      <td>552.777337</td>\n",
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       "      <th>RJ</th>\n",
       "      <th>M</th>\n",
       "      <td>552.361611</td>\n",
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       "      <th>RS</th>\n",
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       "      <th>M</th>\n",
       "      <td>544.816515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <th>M</th>\n",
       "      <td>544.259399</td>\n",
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       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <th>F</th>\n",
       "      <td>541.830839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <th>F</th>\n",
       "      <td>535.098867</td>\n",
       "    </tr>\n",
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       "</table>\n",
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       "                     NU_NOTA_CH\n",
       "SG_UF_PROVA TP_SEXO            \n",
       "SP          M        558.515525\n",
       "DF          M        556.381417\n",
       "MG          M        554.401978\n",
       "SC          M        552.777337\n",
       "RJ          M        552.361611\n",
       "RS          M        552.316859\n",
       "ES          M        544.816515\n",
       "PR          M        544.259399\n",
       "SP          F        541.830839\n",
       "MG          F        535.098867"
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       "      <th>NU_NOTA_LC</th>\n",
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       "      <th>SP</th>\n",
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       "      <th>MG</th>\n",
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       "      <th>RS</th>\n",
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       "    </tr>\n",
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       "      <th>SC</th>\n",
       "      <th>M</th>\n",
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       "    </tr>\n",
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       "      <th>DF</th>\n",
       "      <th>F</th>\n",
       "      <td>520.912903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <th>M</th>\n",
       "      <td>519.221252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <th>M</th>\n",
       "      <td>518.209421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                     NU_NOTA_LC\n",
       "SG_UF_PROVA TP_SEXO            \n",
       "SP          M        534.865725\n",
       "DF          M        532.136903\n",
       "SP          F        528.593384\n",
       "MG          M        527.471602\n",
       "RJ          M        526.441238\n",
       "RS          M        525.617208\n",
       "SC          M        522.746128\n",
       "DF          F        520.912903\n",
       "PR          M        519.221252\n",
       "ES          M        518.209421"
      ]
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       "      <th>MG</th>\n",
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       "      <th>SC</th>\n",
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       "      <th>RJ</th>\n",
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       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <th>M</th>\n",
       "      <td>576.767220</td>\n",
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       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <th>M</th>\n",
       "      <td>573.904934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <th>M</th>\n",
       "      <td>555.032881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <th>M</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                     NU_NOTA_MT\n",
       "SG_UF_PROVA TP_SEXO            \n",
       "SP          M        595.593093\n",
       "MG          M        589.034600\n",
       "SC          M        586.051732\n",
       "DF          M        580.512593\n",
       "RS          M        579.324806\n",
       "RJ          M        577.964779\n",
       "ES          M        576.767220\n",
       "PR          M        573.904934\n",
       "RN          M        555.032881\n",
       "MS          M        553.341989"
      ]
     },
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    },
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG_UF_PROVA</th>\n",
       "      <th>TP_SEXO</th>\n",
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       "  <tbody>\n",
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       "      <th>MG</th>\n",
       "      <th>F</th>\n",
       "      <td>667.233735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ</th>\n",
       "      <th>F</th>\n",
       "      <td>652.749375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <th>F</th>\n",
       "      <td>651.953192</td>\n",
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       "      <th>ES</th>\n",
       "      <th>F</th>\n",
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       "      <th>SC</th>\n",
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       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <th>F</th>\n",
       "      <td>641.444116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DF</th>\n",
       "      <th>F</th>\n",
       "      <td>641.238790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <th>F</th>\n",
       "      <td>637.455604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <th>M</th>\n",
       "      <td>637.044262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <th>F</th>\n",
       "      <td>635.382592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                     NU_NOTA_REDACAO\n",
       "SG_UF_PROVA TP_SEXO                 \n",
       "MG          F             667.233735\n",
       "RJ          F             652.749375\n",
       "SP          F             651.953192\n",
       "ES          F             647.183154\n",
       "SC          F             643.406433\n",
       "RS          F             641.444116\n",
       "DF          F             641.238790\n",
       "SE          F             637.455604\n",
       "MG          M             637.044262\n",
       "RN          F             635.382592"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notasCN_estado = db_2021_selec_limpos.filter(items=['NU_NOTA_CN','SG_UF_PROVA','TP_SEXO'])\\\n",
    "                  .groupby(['SG_UF_PROVA','TP_SEXO']).mean()\\\n",
    "                  .sort_values(by='NU_NOTA_CN', ascending=False)\n",
    "notasCH_estado = db_2021_selec_limpos.filter(items=['NU_NOTA_CH','SG_UF_PROVA','TP_SEXO'])\\\n",
    "                  .groupby(['SG_UF_PROVA','TP_SEXO']).mean()\\\n",
    "                  .sort_values(by='NU_NOTA_CH', ascending=False)\n",
    "notasLC_estado = db_2021_selec_limpos.filter(items=['NU_NOTA_LC','SG_UF_PROVA','TP_SEXO'])\\\n",
    "                  .groupby(['SG_UF_PROVA','TP_SEXO']).mean()\\\n",
    "                  .sort_values(by='NU_NOTA_LC', ascending=False)\n",
    "notasMT_estado = db_2021_selec_limpos.filter(items=['NU_NOTA_MT','SG_UF_PROVA','TP_SEXO'])\\\n",
    "                  .groupby(['SG_UF_PROVA','TP_SEXO']).mean()\\\n",
    "                  .sort_values(by='NU_NOTA_MT', ascending=False)\n",
    "notasredacao_estado = db_2021_selec_limpos.filter(items=['NU_NOTA_REDACAO','SG_UF_PROVA','TP_SEXO'])\\\n",
    "                  .groupby(['SG_UF_PROVA','TP_SEXO']).mean()\\\n",
    "                  .sort_values(by='NU_NOTA_REDACAO', ascending=False)\n",
    "display(notasCN_estado.head(10),notasCH_estado.head(10),notasLC_estado.head(10),notasMT_estado.head(10),notasredacao_estado.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dce9ee5",
   "metadata": {},
   "source": [
    "\n",
    "Os estados com melhores notas foram SP,MG, RJ e DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41463f78",
   "metadata": {},
   "source": [
    "Observa-se que, de fato, as maiores médias de notas por estado foram em sua maioria do sexo masculino, enquanto na redação o sexo feminino obteve as maiores médias.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8966f3",
   "metadata": {},
   "source": [
    "### Legenda dos dados da faixa etária e raça:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca2b664",
   "metadata": {},
   "source": [
    "\t\t\n",
    "Legenda| TP_FAIXA_ETARIA |   |   Legenda| TP_COR_RACA               \n",
    "---| ------------------- |-- |----------| ------------\n",
    "1  |Menor de 17 anos     |   |0         |Não declarado\n",
    "2  |17 anos              |   |1         |Branca\n",
    "3  |18 anos              |   |2         |Preta\n",
    "4  |19 anos              |   |3         |Parda\n",
    "5  |20 anos              |   |4         |Amarela\n",
    "6  |21 anos              |   |5         |Indígena\n",
    "7  |22 anos              |   |6         |Não dispõe da informação\n",
    "8  |23 anos              |   |\n",
    "9  |24 anos              |   |\n",
    "10 |25 anos              |   |\n",
    "11 |Entre 26 e 30 anos   |   |\n",
    "12 |Entre 31 e 35 anos   |   |\n",
    "13 |Entre 36 e 40 anos   |   |\n",
    "14 |Entre 41 e 45 anos   |   |\n",
    "15 |Entre 46 e 50 anos   |   |\n",
    "16 |Entre 51 e 55 anos   |   |\n",
    "17 |Entre 56 e 60 anos   |   |\n",
    "18 |Entre 61 e 65 anos   |   |     \n",
    "19 |Entre 66 e 70 anos   |   |\n",
    "20 |Maior de 70 anos     |   |\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b009070",
   "metadata": {},
   "source": [
    " Moda (maior frequencia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "48072e00",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    979456\n",
       "3    912861\n",
       "2    246568\n",
       "4     44947\n",
       "0     43733\n",
       "5     10540\n",
       "6         2\n",
       "Name: TP_COR_RACA, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_2021_selec_limpos['TP_COR_RACA'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "63220a0e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\la-re\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning:\n",
      "\n",
      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = ( 8 , 4 )) \n",
    "sns.countplot(db_2021_selec_limpos['TP_COR_RACA'], palette = 'pastel')   \n",
    "\n",
    "ax.set_xlabel( \"RAÇA\" , size = 11) \n",
    "ax.set_ylabel( \"CANDITATOS\" , size = 11 ) \n",
    "ax.set_title( \"QUANTIDADE DE CANDIDATOS POR RAÇA\" , size = 12 ) \n",
    "plt.style.use('ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b13df3",
   "metadata": {},
   "source": [
    "Quantidade de pardos e brancos superiores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c5f9a1a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       " <style>\n",
       "   .output_png {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "</style> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#formatação das figuras para o centro\n",
    "from IPython.core.display import HTML as Center\n",
    "\n",
    "Center(\"\"\" <style>\n",
    "   .output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "</style> \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "23fbe927",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\la-re\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning:\n",
      "\n",
      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#quantidade de candidatos por estado\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "sns.countplot(db_2021_selec_limpos['SG_UF_PROVA'], palette = 'pastel',order=db_2021_selec_limpos['SG_UF_PROVA'].value_counts().index)   \n",
    "ax.set_xlabel( \"Estados onde a prova foi realizada\" , size = 12 ) \n",
    "ax.set_ylabel( \"Contagem\" , size = 12 ) \n",
    "ax.set_title( \"Quantidade de canditados por Estado\" , size = 14 ) \n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce208f9",
   "metadata": {},
   "source": [
    "### Legenda dos dados do ano de conclusao:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3645ea",
   "metadata": {},
   "source": [
    "Legenda| TP_ANO_CONCLUIU       \n",
    "---| ------------------- --\n",
    "0  |Não informado           \n",
    "1  |2020                    \n",
    "2  |2019                    \n",
    "3  |2018                    \n",
    "4  |2017                   \n",
    "5  |2016                   \n",
    "6  |2015                       \n",
    "7  |2014             \n",
    "8  |2012           \n",
    "9  |2013  \n",
    "10 |2011   \n",
    "11 |2010   \n",
    "12 |2009  \n",
    "13 |2008   \n",
    "14 |2007   \n",
    "15 |Antes de 2007   \n",
    "\n",
    "\n",
    "Legenda| TP_ESCOLA                \n",
    "---| ------\n",
    "1  |Não respondeu\n",
    "2  |Pública\n",
    "3  |Privada\n",
    "\n",
    "Legenda| TP_ST_CONCLUSAO                \n",
    "---| ------\n",
    "1  |Já concluí o Ensino Médio\n",
    "2  |Estou cursando e concluirei o Ensino Médio em 2021\n",
    "3  |Estou cursando e concluirei o Ensino Médio após 2021\n",
    "4  |Não concluí e não estou cursando o Ensino Médio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "b29e5104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1016825\n",
       "2     847399\n",
       "3     367373\n",
       "4       6510\n",
       "Name: TP_ST_CONCLUSAO, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_2021_selec_limpos['TP_ST_CONCLUSAO'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "3977de19",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1303081\n",
       "1      276836\n",
       "2      158387\n",
       "3      100071\n",
       "15      93846\n",
       "4       70782\n",
       "5       50492\n",
       "6       38899\n",
       "7       30956\n",
       "8       24727\n",
       "9       20968\n",
       "10      16987\n",
       "11      15343\n",
       "12      13831\n",
       "13      11864\n",
       "14      11037\n",
       "Name: TP_ANO_CONCLUIU, dtype: int64"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_2021_selec_limpos['TP_ANO_CONCLUIU'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe098b94",
   "metadata": {},
   "source": [
    "A maioria não informou o ano de conclusao, portanto não é tido como um ótimo indicador, mas será feita a relação do tempo de conclusão do ensino médio e as notas de Redação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "85155254",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RJ    5\n",
       "MG    4\n",
       "SP    3\n",
       "PE    3\n",
       "BA    2\n",
       "PA    1\n",
       "GO    1\n",
       "PB    1\n",
       "CE    1\n",
       "Name: SG_UF_PROVA, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Localização das notas máximas de redação segundo o ano de conclusão\n",
    "tabela_ano_c = db_2021_selec_limpos[['TP_ANO_CONCLUIU','NU_NOTA_REDACAO','SG_UF_PROVA']]\n",
    "tabela_ano_c.sort_values(by='NU_NOTA_REDACAO', ascending=False).head(25)\n",
    "filtro  = tabela_ano_c['NU_NOTA_REDACAO'] > 980 # selecionando apenas as notas mil\n",
    "notas_mil = tabela_ano_c[filtro]\n",
    "\n",
    "tabela_ano_c.sort_values(by='NU_NOTA_REDACAO', ascending=False).head(25)\n",
    "notas_mil = tabela_ano_c[filtro]\n",
    "notas_mil['SG_UF_PROVA'].value_counts()# localização das notas máximas por estado.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0767b3f",
   "metadata": {},
   "source": [
    "Apenas 21 candidatos tiraram nota máxima na redação e isso ocorreu em 9 estados no total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "30842a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>SG_UF_PROVA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 NU_NOTA_REDACAO  SG_UF_PROVA\n",
       "TP_ANO_CONCLUIU                              \n",
       "0                              7            7\n",
       "1                              2            2\n",
       "2                              4            4\n",
       "3                              7            7\n",
       "4                              1            1"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_mil.groupby('TP_ANO_CONCLUIU').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a89ed4",
   "metadata": {},
   "source": [
    "Apesar de boa parte estar no \"não informado\", nota-se que as notas máximas em redação foram obtidas pelas pessoas mais recentemente saídas do ensino médio (consultar a legenda)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198c6e8f",
   "metadata": {},
   "source": [
    "### Gráfico da Média da redação por ano de conclusão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "384efeca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ano_conclusao = tabela_ano_c.pivot_table(index='TP_ANO_CONCLUIU', values='NU_NOTA_REDACAO', aggfunc='mean')\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(ano_conclusao, label= ['Média da redacão X Ano de conclusao'])\n",
    "plt.legend (ncol = 1, loc= (0.08, 0.05),fontsize=9)\n",
    "plt.xlim(left=0)\n",
    "plt.title('MÉDIA DA REDAÇÃO  x ANO DE CONCLUSÃO',fontsize=13)\n",
    "plt.yticks(range(550, 680, 10)) # alterar escala do eixo\n",
    "plt.xticks(range(0, 15, 1)) # alterar escala do eixo\n",
    "plt.xlim(0, 15) # definir limite do eixo\n",
    "plt.axvline(1, color='k') #criação de linha no ponto 1, onde o ano é especificado\n",
    "plt.xlabel('ANO CONCLUSÃO DO ENSINO MÉDIO', fontsize=10)\n",
    "plt.ylabel('MÉDIA NOTA DA REDAÇÃO', fontsize=10)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc2c0572",
   "metadata": {},
   "source": [
    "Conforme analisado, a maioria dos dados referentes ao ano de conclusão não sao informativos (0 =\"não informado\"), por isso a análise do gráfico a partir da linha preta é mais sólida. A partir desse ponto é possível ver uma tendência da média cair conforme o ano de formação se torna mais distante. Vide legenda acima. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f843261",
   "metadata": {},
   "source": [
    "### Legenda - renda mensal familiar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51277bc",
   "metadata": {},
   "source": [
    "Legenda| Renda mensal da família (reais)            \n",
    "-------| -----------------------\n",
    "A      |Nenhuma Renda\n",
    "B      |Até  1.100,00\n",
    "C      |De  1.100,01 até  1.650,00\n",
    "D      |De  1.650,01 até  2.200,00\n",
    "E      |De  2.200,01 até  2.750,00\n",
    "F      |De  2.750,01 até  3.300,00\n",
    "G      |De  3.300,01 até  4.400,00\n",
    "H      |De  4.400,01 até  5.500,00\n",
    "I      |De  5.500,01 até  6.600,00\n",
    "J      |De  6.600,01 até  7.700,00\n",
    "K      |De  7.700,01 até  8.800,00\n",
    "L      |De  8.800,01 até  9.900,00\n",
    "M      |De  9.900,01 até  11.000,00\n",
    "N      |De  11.000,01 até  13.200,00\n",
    "O      |De  13.200,01 até  16.500,00\n",
    "P      |De  16.500,01 até  22.000,00\n",
    "Q      |Acima de  22.000,00\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "9fc90bd9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    546733\n",
       "C    346838\n",
       "D    277708\n",
       "F    168049\n",
       "G    147647\n",
       "E    143957\n",
       "H    118749\n",
       "A    112899\n",
       "I     75420\n",
       "M     47849\n",
       "J     46934\n",
       "Q     39914\n",
       "K     37157\n",
       "O     32862\n",
       "P     32802\n",
       "L     32560\n",
       "N     30028\n",
       "Name: Q006, dtype: int64"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_2021_selec_limpos['Q006'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56a0312",
   "metadata": {},
   "source": [
    "Através dessa análise vemos que a maioria dos candidatos tem renda familiar até 4.400,00."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "78d2555d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q006</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>752.414191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>745.189927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>738.371371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>730.735314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>720.293423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>712.899263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>708.104529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>701.767589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>692.066295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>678.737505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>661.265180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>643.822933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>629.309308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>611.618174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>596.308305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>566.065813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>534.462307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NU_NOTA_REDACAO\n",
       "Q006                 \n",
       "Q          752.414191\n",
       "P          745.189927\n",
       "O          738.371371\n",
       "N          730.735314\n",
       "M          720.293423\n",
       "L          712.899263\n",
       "K          708.104529\n",
       "J          701.767589\n",
       "I          692.066295\n",
       "H          678.737505\n",
       "G          661.265180\n",
       "F          643.822933\n",
       "E          629.309308\n",
       "D          611.618174\n",
       "C          596.308305\n",
       "B          566.065813\n",
       "A          534.462307"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Média das notas de redação por renda familiar\n",
    "notas_redacao_renda = db_2021_selec_limpos.filter(items=['NU_NOTA_REDACAO','Q006'])\\\n",
    "                  .groupby('Q006').mean()\\\n",
    "                  .sort_values(by='NU_NOTA_REDACAO', ascending=False)\n",
    "notas_redacao_renda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0427a0",
   "metadata": {},
   "source": [
    "Através da análise é possível ver que conforme a renda aumenta a tendência é de as notas também aumentarem, sendo a diferença mais discrepante quando compara-se com as menores rendas. \n",
    "Abaixo segue o gráfico relacionado "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b0a9465d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "renda_REDACAO=notas_redacao_renda.pivot_table(index='Q006', values='NU_NOTA_REDACAO', aggfunc='mean')\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(renda_REDACAO, label= ['Media nota da Redação'])\n",
    "plt.legend (ncol = 1, loc= (0.6, 0.1))\n",
    "plt.xlim(left=0)\n",
    "plt.style.use(\"seaborn\")\n",
    "plt.title('MEDIA REDAÇÃO x RENDA FAMILIAR ')\n",
    "plt.xlabel('RENDA FAMILIAR')\n",
    "plt.ylabel('NOTA MÉDIA REDAÇÃO')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3f4e9f",
   "metadata": {},
   "source": [
    "### Análise de renda considerando os Estados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "e6a5cc3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>SG_UF_PROVA</th>\n",
       "      <th>AC</th>\n",
       "      <th>AL</th>\n",
       "      <th>AM</th>\n",
       "      <th>AP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CE</th>\n",
       "      <th>DF</th>\n",
       "      <th>ES</th>\n",
       "      <th>GO</th>\n",
       "      <th>MA</th>\n",
       "      <th>...</th>\n",
       "      <th>PR</th>\n",
       "      <th>RJ</th>\n",
       "      <th>RN</th>\n",
       "      <th>RO</th>\n",
       "      <th>RR</th>\n",
       "      <th>RS</th>\n",
       "      <th>SC</th>\n",
       "      <th>SE</th>\n",
       "      <th>SP</th>\n",
       "      <th>TO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q006</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>526.968026</td>\n",
       "      <td>543.663764</td>\n",
       "      <td>469.777995</td>\n",
       "      <td>519.185022</td>\n",
       "      <td>537.493184</td>\n",
       "      <td>508.663151</td>\n",
       "      <td>545.402766</td>\n",
       "      <td>548.015564</td>\n",
       "      <td>520.425080</td>\n",
       "      <td>520.433464</td>\n",
       "      <td>...</td>\n",
       "      <td>547.740864</td>\n",
       "      <td>549.336512</td>\n",
       "      <td>548.973670</td>\n",
       "      <td>502.814371</td>\n",
       "      <td>518.549223</td>\n",
       "      <td>544.310656</td>\n",
       "      <td>581.397206</td>\n",
       "      <td>550.087617</td>\n",
       "      <td>555.902899</td>\n",
       "      <td>505.225410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>550.224176</td>\n",
       "      <td>574.432793</td>\n",
       "      <td>505.740135</td>\n",
       "      <td>540.666667</td>\n",
       "      <td>573.652642</td>\n",
       "      <td>552.116983</td>\n",
       "      <td>555.838281</td>\n",
       "      <td>573.498377</td>\n",
       "      <td>552.060170</td>\n",
       "      <td>559.432326</td>\n",
       "      <td>...</td>\n",
       "      <td>559.629073</td>\n",
       "      <td>573.144484</td>\n",
       "      <td>582.609473</td>\n",
       "      <td>541.706280</td>\n",
       "      <td>525.560976</td>\n",
       "      <td>563.940249</td>\n",
       "      <td>564.429210</td>\n",
       "      <td>587.797124</td>\n",
       "      <td>566.841486</td>\n",
       "      <td>544.421292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>580.661080</td>\n",
       "      <td>604.553614</td>\n",
       "      <td>545.621866</td>\n",
       "      <td>570.891455</td>\n",
       "      <td>605.710329</td>\n",
       "      <td>609.679321</td>\n",
       "      <td>577.149817</td>\n",
       "      <td>600.104712</td>\n",
       "      <td>581.694402</td>\n",
       "      <td>597.856062</td>\n",
       "      <td>...</td>\n",
       "      <td>570.920379</td>\n",
       "      <td>599.954878</td>\n",
       "      <td>611.055831</td>\n",
       "      <td>565.496002</td>\n",
       "      <td>561.931818</td>\n",
       "      <td>584.707258</td>\n",
       "      <td>582.572187</td>\n",
       "      <td>619.244969</td>\n",
       "      <td>591.307867</td>\n",
       "      <td>576.526542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>591.358735</td>\n",
       "      <td>628.287293</td>\n",
       "      <td>558.797536</td>\n",
       "      <td>587.815126</td>\n",
       "      <td>626.042619</td>\n",
       "      <td>639.024574</td>\n",
       "      <td>592.212841</td>\n",
       "      <td>610.047611</td>\n",
       "      <td>600.731014</td>\n",
       "      <td>618.667746</td>\n",
       "      <td>...</td>\n",
       "      <td>580.783889</td>\n",
       "      <td>614.304642</td>\n",
       "      <td>625.224226</td>\n",
       "      <td>576.915716</td>\n",
       "      <td>589.048474</td>\n",
       "      <td>595.187680</td>\n",
       "      <td>594.352131</td>\n",
       "      <td>645.285601</td>\n",
       "      <td>603.267937</td>\n",
       "      <td>592.426083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>610.490196</td>\n",
       "      <td>642.703566</td>\n",
       "      <td>574.492308</td>\n",
       "      <td>603.290676</td>\n",
       "      <td>648.021330</td>\n",
       "      <td>669.129173</td>\n",
       "      <td>604.044299</td>\n",
       "      <td>630.134111</td>\n",
       "      <td>625.008073</td>\n",
       "      <td>640.639740</td>\n",
       "      <td>...</td>\n",
       "      <td>591.536681</td>\n",
       "      <td>635.407449</td>\n",
       "      <td>656.381806</td>\n",
       "      <td>587.923186</td>\n",
       "      <td>579.477612</td>\n",
       "      <td>610.496144</td>\n",
       "      <td>604.715550</td>\n",
       "      <td>668.034188</td>\n",
       "      <td>620.988976</td>\n",
       "      <td>617.743590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "SG_UF_PROVA          AC          AL          AM          AP          BA  \\\n",
       "Q006                                                                      \n",
       "A            526.968026  543.663764  469.777995  519.185022  537.493184   \n",
       "B            550.224176  574.432793  505.740135  540.666667  573.652642   \n",
       "C            580.661080  604.553614  545.621866  570.891455  605.710329   \n",
       "D            591.358735  628.287293  558.797536  587.815126  626.042619   \n",
       "E            610.490196  642.703566  574.492308  603.290676  648.021330   \n",
       "\n",
       "SG_UF_PROVA          CE          DF          ES          GO          MA  ...  \\\n",
       "Q006                                                                     ...   \n",
       "A            508.663151  545.402766  548.015564  520.425080  520.433464  ...   \n",
       "B            552.116983  555.838281  573.498377  552.060170  559.432326  ...   \n",
       "C            609.679321  577.149817  600.104712  581.694402  597.856062  ...   \n",
       "D            639.024574  592.212841  610.047611  600.731014  618.667746  ...   \n",
       "E            669.129173  604.044299  630.134111  625.008073  640.639740  ...   \n",
       "\n",
       "SG_UF_PROVA          PR          RJ          RN          RO          RR  \\\n",
       "Q006                                                                      \n",
       "A            547.740864  549.336512  548.973670  502.814371  518.549223   \n",
       "B            559.629073  573.144484  582.609473  541.706280  525.560976   \n",
       "C            570.920379  599.954878  611.055831  565.496002  561.931818   \n",
       "D            580.783889  614.304642  625.224226  576.915716  589.048474   \n",
       "E            591.536681  635.407449  656.381806  587.923186  579.477612   \n",
       "\n",
       "SG_UF_PROVA          RS          SC          SE          SP          TO  \n",
       "Q006                                                                     \n",
       "A            544.310656  581.397206  550.087617  555.902899  505.225410  \n",
       "B            563.940249  564.429210  587.797124  566.841486  544.421292  \n",
       "C            584.707258  582.572187  619.244969  591.307867  576.526542  \n",
       "D            595.187680  594.352131  645.285601  603.267937  592.426083  \n",
       "E            610.496144  604.715550  668.034188  620.988976  617.743590  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#média das notas de redação de cada estado em relação a renda familiar\n",
    "teste50.pivot_table(index='Q006', columns='SG_UF_PROVA', values='NU_NOTA_REDACAO', aggfunc='mean').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "027555de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df55=teste50.pivot_table(index='Q006', columns='SG_UF_PROVA', values='NU_NOTA_REDACAO', aggfunc='mean')\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(df55, label= ['AC', 'AL', 'AM', 'AP', 'BA', 'CE', 'DF', 'ES', 'GO', 'MA', 'MG', 'MS', 'MT', 'PA', 'PB', 'PE', 'PI', 'PR', 'RJ', 'RN', 'RO', 'RR', 'RS', 'SC', 'SE', 'SP', 'TO'])\n",
    "plt.legend (ncol = 1, loc= (1.03, 0))\n",
    "plt.xlim(left=0)\n",
    "plt.title('NOTAS DA REDAÇÃO x RENDA FAMILIAR x ESTADO')\n",
    "plt.xlabel('RENDA FAMILIAR')\n",
    "plt.ylabel('NOTA REDAÇÃO')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59ec860",
   "metadata": {},
   "source": [
    "Dessa análise, mais uma vez é possível ver tendência de crescimento da média conforme a renda aumenta, mas é possível ver que a partir da renda J (De 6.600,01 até 7.700,001) a flutuação se torna mais evidente, boas notas começam a ser encontradas em todas as rendas, apesar da curva se manter subindo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "d8da36ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "nova_data2= db_2021_selec_limpos.drop(columns=[ 'TP_ST_CONCLUSAO','TP_ANO_CONCLUIU','TP_ESCOLA','TP_FAIXA_ETARIA','TP_SEXO','TP_ESTADO_CIVIL','IN_TREINEIRO','NU_NOTA_COMP1','NU_NOTA_COMP2','NU_NOTA_COMP3','NU_NOTA_COMP4','NU_NOTA_COMP5','TP_COR_RACA', 'TP_PRESENCA_CN','TP_PRESENCA_CH','TP_PRESENCA_LC','TP_PRESENCA_MT','TP_NACIONALIDADE','TP_ENSINO','TP_LINGUA','TP_STATUS_REDACAO', 'Q001','Q002','Q003','Q004','Q005','Q005','Q007'])\n",
    "# Média de todas as notas em relação a renda.\n",
    "Media = pd.pivot_table(nova_data2, index=['Q006'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50cc158",
   "metadata": {},
   "source": [
    "#### Análise da renda em relação a média de todas as notas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "0e4a82cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_ANO</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>Media_Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q006</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2021</td>\n",
       "      <td>471.924401</td>\n",
       "      <td>449.259398</td>\n",
       "      <td>454.178489</td>\n",
       "      <td>470.339162</td>\n",
       "      <td>534.462307</td>\n",
       "      <td>476.032752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2021</td>\n",
       "      <td>485.195380</td>\n",
       "      <td>458.595073</td>\n",
       "      <td>469.805110</td>\n",
       "      <td>484.990884</td>\n",
       "      <td>566.065813</td>\n",
       "      <td>492.930452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NU_ANO  NU_NOTA_CH  NU_NOTA_CN  NU_NOTA_LC  NU_NOTA_MT  NU_NOTA_REDACAO  \\\n",
       "Q006                                                                            \n",
       "A       2021  471.924401  449.259398  454.178489  470.339162       534.462307   \n",
       "B       2021  485.195380  458.595073  469.805110  484.990884       566.065813   \n",
       "\n",
       "      Media_Total  \n",
       "Q006               \n",
       "A      476.032752  \n",
       "B      492.930452  "
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_Media = Media.assign(Media_Total = (Media.NU_NOTA_CH +Media.NU_NOTA_CN + Media.NU_NOTA_LC + Media.NU_NOTA_MT+ Media.NU_NOTA_REDACAO) /5)\n",
    "notas_Media.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "1bc59e1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C4zMT6Rb0cfTo0XzPT01NNQ0aNDBXXXVVnsl/T58+ba688krTunVrk5GRUeD+zzUh8htvvGEAs3jxYmNM/s/lnz+Sk5MLPBbG5H6uIiIiCpxI98xHhQoVTJ06dczNN99s/vWvf+XJX9xjWpA/ZzijuF9vhU2gbMzZJ0guaCLdgo7tuHHjjDF5J9JdvHixAczcuXML3Hd0dLSpXLmySUtLO+f7zsjIMJdffrmJjIw853ETEZGyYxlTgqGqRERERERELkG2jMKXlZXF22+/zdixY3nwwQf55z//mWf98uXLGTRoEN9++61nWUZGBv/4xz8YM2YM48aNK/YNzhs3biyN6GXCqdmcmguUraScms2puUDZSsqp2ZyaC5StpJyazam5QNlEzpctBWrOnDlYlsWrr77Kyy+/7JlgEeDXX3/lyy+/zDcfxtKlS/Hx8WHatGlMmDCBf/3rX5w4caLI+9y0aVOp5S9tTs3m1FygbCXl1GxOzQXKVlJOzebUXKBsJeXUbE7NBcomcr7KvUClp6ezfPlyzzwokHdI1zfffJOhQ4fmuwE7ISGBG2+8EYDg4GAiIiJYv359+QUXEREREZFLXrmPwnf48GECAgJYtGgRmzdvxtfXl4EDB9K8eXM+//xzQkND8519AkhJSaFmzZqex8HBwWcdXaogFStWLJX8ZcGps8o7NRcoW0k5NZtTc4GylZRTszk1FyhbSTk1m1NzgbOzOfnnNZEzyn0Qid27d/Poo48yatQounXrxp49e5g8eTKPPfYYb731FpMnT8bX15eYmBhuvfVWz6Sdd955J6+99hqBgYEAzJ49m4oVKxY4meDGjRs9p4ErVqzIoEGDyucNioiIiEiJLViwgNOnTwPQsmVLIiMj7Q0k8iflfgYqKCgIy7Lo2rUrAGFhYQQHB3Po0CGOHDnC+PHjATh27Bivv/46gwcP5rrrriMoKIikpCRPgUpOTqZ169YF7icyMjLfN9yhQ4dw4qCDAQEBpKWl2R0jH6fmAmUrKadmc2ouULaScmo2p+YCZSspp2Zzai5wbjbLsqhdu7Z+6S2OV+4FKjAwkObNm7Nx40Zat25NcnIySUlJNGvWjLfeesuz3Z/PQHXo0IFly5YRHh5OUlIS27Zt4/777y/Wvo0xjixQgHKVgLKVjFOzOTUXKFtJOTWbU3OBspWUU7M5NRc4O5uI05V7gQK47777iI2NZc6cObhcLoYPH55nIImz6dWrF7GxsYwZMwaXy8WwYcPw9/cvp8QiIiIiIiI2FahatWoRExNzzm3+vN7Pz89zeZ+IiIiIiIgdbJkHSkRERERE5EKkAiUiIiIiIlJEKlAiIiIiIiJFpAIlIiIiIiJSRCpQIiIiIiIiRaQCJSIiIiIiUkQqUCIiIiIiIkWkAiUiIiIitjJZmZjN6+yOIVIktkykKyIiIiKXNpORAVu+xXy7EvP9eqzg2nBDL7tjiRRKBUpEREREyoVJP43ZvB7z7UrY/C1UC8Jq0xnXw89h1WtgdzyRIlGBEhEREZEyY06dxHy/DvNtAvywAWrWxmrTCevW26BOPSzLAvD8KeJ0KlAiIiIiUqrMyROYTWtyS9PW76D2Zbmlqd8dWCGX2R1P5LyoQImIiIjIeTNpqZiNqzEbEmDb9xAalluaBg3DqlXH7ngipUYFSkRERERKxKQexXy3Jveepp1boN4VWG06Y0X/FatmbbvjiZQJFSgRERERKTJz7FfMd6tzL8/btRXqN8o90zR0LFaNmnbHEylzKlAiIiIick7mSDJmw6rc0rR7O4Q3zS1Nwx7EqlbD7ngi5UoFSkRERETyMcmHfitNK2FfIjS6EiuqG9ZfH8GqUs3ueCK2UYESEREREQBM0gHMtwm5Z5p++QmatMDqegNWZBRWQBW744k4ggqUiIiIyCXMHPwF8+3K3NJ08GdoGol1dQ+syPZYlQPsjifiOCpQIiIiIpcQYwxm/97fS1PSQbiyNdaNfbBatMOq5G93RBFHU4ESERERuciZ06fgxx8wOzaTuuVb3CmH4cq2WD0GYbVoi+VXye6IIhcMFSgRERGRi4zJSIdd2zA7vsds3wx7d0GNYKwmLag4aBinr4jA8qtod0yRC5IKlIiIiMgFzmRlQuJ2zI7NuYXpp51QpSpW4xZY3W/O/fO3OZp8AwNJT021N7DIBUwFSkREROQCY7Kz4Kcffz/DlLgdKgdgNW6O1ekarLvHQc3aWJZld1SRi44KlIiIiIjDmZwc2JeI2f5bYdq1FXwr5Bamtp2xhoyA2nVVmETKgQqUiIiIiMMYtxt++en3wvTjD+ByQaPmuYM+DLwb6tTDcrnsjipyybGlQGVlZTFr1iw2bdqEt7c3YWFhjB07lhkzZrBjxw58fX2pVKkSd999N2FhYQBkZGQQGxtLYmIiLpeL6OhooqKi7IgvIiIiUqqMMXBgH2b7Zsz272HnFnDnQMNmWE2aY/UeApeFYbm87I4qcsmzpUDNmTMHy7J49dVXsSyLo0ePAtC+fXuGDx+Ol5cX3377La+88gqvvvoqAEuXLsXHx4dp06aRlJTEhAkTaNasGf7+mqtARERELizGGDi8P/fs0vbvMTu3QEY6hDfNLUw394fLw7G8VJhEnKbcC1R6ejrLly/n9ddf91ynW61aNQDatm3r2a5hw4YkJyfjdrtxuVwkJCQwcuRIAIKDg4mIiGD9+vV07969vN+CiIiISLEYYyDlcO7Zpe2bMTs2w8k0uKIJVpPmuK69Feo3xPL2sTuqiBSi3AvU4cOHCQgIYNGiRWzevBlfX18GDhxI8+bN82z3ySef0KpVK1y/XdubkpJCzZo1PeuDg4NJSUkp1+wiIiIiRWWOJOeeYdrx22V5x49A/ca5hanr9bnlycfX7pgiUkzlXqBycnI4fPgwoaGhDBkyhD179jB58mReeeUVAgMDAYiPj2fVqlU8/fTTeZ77x5FljDHn3M/GjRvZtGkTAD4+PkRHRxMQEFDK76Z0+Pr6et67kzg1FyhbSTk1m1NzgbKVlFOzOTUXKFtJOSmb++ivZG/dSNYP35H6w3e4kw7i1aAxPs1a4d39YbwbX+mIyWuddMzOZu7cuWRlZQHQsmVLIiMj7Q0k8iflXqCCgoKwLIuuXbsCEBYWRnBwMD///DPNmjUjISGBDz74gKeeeooqVarkeV5SUpLnGz45OZnWrVsXuJ/IyMh833BpaWmFFi87BAYGkurACe2cmguUraScms2puUDZSsqp2ZyaC5StpOzMZtKO555dOjN57eEDcFl9rCbNqTx0NKfq1oeKlcgCsgAys3I/bObUz6dlWfj7+xMdHW13FJFzKvcCFRgYSPPmzdm4cSOtW7cmOTmZpKQk6tSpQ0JCAvPmzePJJ58kKCgoz/M6dOjAsmXLCA8PJykpiW3btnH//feXd3wRERG5RJmTabBjS25h2rEZDuzLHUq8SQtc/e6ERldiVc4d3MonMBDLgSVFRM6fLaPw3XfffcTGxjJnzhxcLhfDhw+nWrVqTJs2japVq/Liiy96tn3qqacICAigV69exMbGMmbMGFwuF8OGDdMIfCIiIlJmzKmT8ONWzI7vcwvTz3tyJ6tt3BzXrX/JLUwBVQp9HRG5uNhSoGrVqkVMTEy+5e+//36Bz/Hz82P8+PFlmEpEREQuZSb9NOzamjsX047NsC8RgmrnDit+Yz+sxs2xqlSzO6aI2MyWAiUiIiJiN5OZAYnbfytM38OeH6FqjdzCdE3P3MJUPajwFxKRS4oKlIiIiFwSTFYW7N7x+yV5u3dAQNXcotT1Bqx7/4YVVMvumCLicCpQIiIiclEy2dmw58ffRsn7HhK3Q8VKWE1aYEV1xxo6BmqG5JkmRUSkMCpQIiIiclEwOTmwL/H3S/J2bQMfX2h8JVbrTljRw6F2qAqTiJwXFSgRERG5IBl3Dtm7d+LesCp3HqZdW8Fy5Y6O17wt1oC7c4cZd7nsjioiFxEVKBEREblgGLc7d+CHNcsx364kLScHwpvmDvzQewhcFobl8rI7pohcxFSgRERExPHMwZ8xq5dj1sRB+mmstp1xjXicwFbtSDt5yu54InIJUYESERERRzLHfsWsXYFZsxwO/oLVoh2uv9wHV7bG8vYBwPLSjzIiUr70r46IiIg4hjl9CrNhVW5p2rkl936ma3piteqIVamy3fFERFSgRERExF4mOwt++C73Er1Na6F2XawO3bHufgCrWg2744mI5KECJSIiIuXOGAOJ2zBr4jDrvgG/iljtr8I14WWsuvXsjiciUiAVKBERESk3uYNBxGHWxsGpk1htu+Aa+TiER2i4cRG5IKhAiYiISJkyx45g1q3ArF4OB/ZBy3a4Bg+DZm2wfHzsjiciUiwqUCIiIlLqzOlTmO9W5Q47vmMzNGyGdfUtWK07aTAIEbmgqUCJiIhIqfAMBrEmDrNxTe5gEFHdse4ap8EgROSioQIlIiIiJZY7GMT23NK0fgX4+mFFXYVrwlSsupfbHU9EpNSpQImIiEixmYO/YNYsz71E79SJ3MEgRjwG4U01GISIXNRUoERERKRIPINBrImD/XuhRTtcg4bBlRoMQkQuHSpQIiIiUiCTfoqM71aRs/zT3MEgwptidb8Zq3VHrEr+dscTESl3KlAiIiLiYbIyYfcOzPbvMdu/h592klE3DKt9V6yhY7GqB9kdUUTEVipQIiIilzCTkwN7d/1emHZtg0r+WE2aY3W+Duue8QRe0YjU1FS7o4qIOIIKlIiIyCXEuN2wf+/vhWnnFvDyhsbNsVp1xLpteO7w45Zld1QREUdSgRIREbmIGWPg8IHfCtOm3PuYsrKhUbPcs0y9oyG0vkbOExEpIhUoERGRi4z5NTn37NKZs0wnUiE8AqtJC6zr+8Dl4Vje+hFARKQk9K+niIjIBc6kHsPs2Px7YUo5DPUbYTVugeueB+CKJli+FeyOKSJyUVCBEhERucCYUydg5xbM9s25henAzxB6OVaTFrj+ch80bIrlV8numCIiFyUVKBEREYczGemwa9vvAz/sTYRadXIL0623QaMrsQIC7Y4pInJJsKVAZWVlMWvWLDZt2oS3tzdhYWGMHTuW48eP89prr3H48GF8fHy47777aNKkCQAZGRnExsaSmJiIy+UiOjqaqKgoO+KLiIiUKZOdlXcupt07oUo1rIgWWNf2zL2XqWoNu2OKiFySCi1QBw4c4NSpU4SHh3uWff/99yxevJiMjAzatGlD//79izXc6Zw5c7Asi1dffRXLsjh69KhnecOGDZkwYQK7du3i5ZdfZtq0aXh5ebF06VJ8fHyYNm0aSUlJTJgwgWbNmuHvr1nQRUTkwmZycsjetR33t6t+m4vpB/CrlFuUOl6DdfcDEFRLQ4uLiDhAoQXqvffeIywszFOgDh48yAsvvECzZs24/PLLWbJkCT4+PvTu3btIO0xPT2f58uW8/vrrnv8IqlWrBsCqVauYPn06AOHh4VSpUoXt27fTrFkzEhISGDlyJADBwcFERESwfv16unfvXuw3LSIiYgdjDBw7Avv3YPbvhV/2YvbvgYM/c6KCH6bhlVgt2mENHgYhl6kwiYg4UKEFKjExkb59+3oex8fHc9lll/H4448DUL9+fZYuXVrkAnX48GECAgJYtGgRmzdvxtfXl4EDBxIWFoYxhsDA36/hrlmzJikpKQCkpKRQs2ZNz7rg4GDPOhEREacxp0/lTli7f2+ewsTpUxAcAnUvx6p7Oa5WUVA3jMArGpJ24qTdsUVEpBCFFqgTJ054zhABbNmyhbZt23oeN23alH//+99F3mFOTg6HDx8mNDSUIUOGsGfPHiZPnszUqVML/U3bH9cbY8657caNG9m0aRMAPj4+REdHExAQUOSc5cnX1zdPcXQKp+YCZSspp2Zzai5QtpJyarayyGWys3Ef/JmcfbvJ+fmn3D/37cadfAirSjW86jXI/bi2Z+6foWFnHVLc19cXy+VVqtlKi1M/n+DcbE7NBc7OBjB37lyysrIAaNmyJZGRkfYGEvmTQgtU1apVOXz4MEFBQWRmZrJ7924GDRrkWX/69Gm8izEZX1BQEJZl0bVrVwDCwsIIDg5m//79AKSmpnq+qZOTkwkKCvI8LykpKc+61q1bF7ifyMjIfN9waWlphRYvOwQGBpKammp3jHycmguUraScms2puUDZSsqp2c4nlzEGjv6aezbplz+cVTr0C7i8PGeUCG+K1e1mXKFhWAFVMED2bx8ApGfkfpRitrKmbMXn1Fzg3GyWZeHv7090dLTdUUTOqdDm065dO959910GDhzIhg0bqFSpEhEREZ71P/30EyEhIUXeYWBgIM2bN2fjxo20bt2a5ORkkpKSqFOnDh06dOCzzz5j0KBB7Nq1i2PHjnlG4evQoQPLli0jPDycpKQktm3bxv3331+CtywiInJu5tRJOLD3t6L0231K+/fC6dNQ68zld2G42nSGupfnDvDgctkdW0REykGhBWrw4MG8+eabxMbGUqVKFcaNG5fnjFNcXBxt2rQp1k7vu+8+YmNjmTNnDi6Xi+HDh1OtWjWGDBnCa6+9xtixY/H29mbMmDF4eeVeztCrVy9iY2MZM2YMLpeLYcOGaQQ+ERE5LyY7Gw7vx/yy5w9nlvbCkWSoUj23KIVejtXleqy6YRASetbL70RE5NJhGSde01ZGDh48qEv4isGpuUDZSsqp2ZyaC5StpJyYzRw/SqWUg5zcufX30e8O7Qdvb6hTDys07PfL8OqGlfvEtE48ZmcoW/E5NRc4N5tlWcW6qknELuc1ke6xY8dYsWIFcXFx/P3vfy+tTCIiIufFnEiFvYmYPT9i9uyCPT/C8SOcCrkM6tSDupfjatcZ6oZBjWBdficiIkVW7AKVlZXFunXriIuL4/vvv6du3bp5RuUTEREpTyb9FOzdjdnzI+z5EbN3FyQfgpq1scIaYjWMwLquF1zegCrBtR35m3cREblwFLlA7dixg+XLl7N69WqqV6/O/v37eeKJJ7jyyivLMp+IiIiHycyAn3/ynFUye3fljoJXtQZcHo4VFo6ry/UQFo5V2ZlTV4iIyIWt0AK1aNEi4uPjcbvddOrUiUmTJlGvXj1uu+02qlatWg4RRUTkUmSys3NHwNv7I+zZlXuG6cA+qFgZwhpihTXE1bZLblmqUq3wFxQRESkFhRaohQsX0qNHDwYPHoyvr295ZBIRkUuMcefAwf2/laXf7lv6+Sfw8c0tSJeH4+oxGMLCoXrNQideFxERKSuFFqi//vWvxMfHc//999OmTRs6d+5My5YtyyObiIhchIwxkHwQ89OPsPe3M0v7doMxUO8KrLBwrGtvxbo8HIJDNMCDiIg4SqEFqnv37nTv3p2UlBTi4+OZOXMmaWlpuN1udu/eTZ06dXDpPzcRETkLYwwcTfGcVTJ7cksTmRkQWj+3LHW+Dis6HEIuw/pt7j8RERGnKvIgEkFBQfTr149+/fqxY8cO4uLieOedd3j33Xdp3bo1o0ePLsucIiJyATBZWbDje07v30POjh9yhw8/mZY7z9Ll4VitO2H1Hwp1Lsfy8bE7roiISLEVWqAGDx7Mm2++SZUqVTzLGjduTOPGjbnnnntYu3Yt8fHxZRpSREScy2Rnw7ZNmHUrMBvXQMVKuJu2xGoaiXXLQLisAVaFCnbHFBERKRXnNZGut7c3nTp1olOnTqWVR0RELgDGnQM7tuSWpg2rwMcXq20XXA/EQP1GVK5SRfMtiYjIRem8CpSIiFw6jNsNu7Zh1q/AfJsAxmC17Yxr5OMQHqHBHkRE5JJQpAK1dOlS/Pz8zrnNgAEDSiWQiIg4hzEGftqJWfcNZv03kJWJ1bojrnv/Bo2vxHJp0AcREbm0FKlAbdu2Da9zjIxkWZYKlIjIRcIYAz/vxqxdkVuaTp3EatUB19DR0KQllrcuXhARkUtXkf4XfPjhh/MMIiEiIhcfs38fZl08Zt03cPwIVssoXH+5D5q11oh5IiIiv9GvEUVELmHm0P7ce5rWfQPJh6BFW1z97oAr22rkPBERkbNQgRIRucSYlMO/3dO0Ag7syz3DdMtArJbtsPwq2R1PRETE0QotUBMnTsTf3788soiISBkxR1Iw367ErFsB+xIhoiXWtbdiRUZhVdK/8SIiIkVVaIFq2rQpWVlZfPTRR6xatYpDhw4BULt2bTp16kTPnj3x0bXxIiKOY1KPYr5NyC1Nu3dAoyuxulyP1fopLP9Au+OJiIhckAotUKdPnyYmJoZDhw7RuXNnunfvDsD+/fv58MMPWbt2LRMnTix0mHMRESl75kQqZsOq3NHzdm6BBo2x2nXF+usjWIHV7I4nIiJywSu0QC1cuBBjDNOmTSMwMO9vLAcPHsyzzz7LwoULueOOO8ospIiIFMycOonZuDr3TNO276Feg9zSdNc4rOpBdscTERG5qBRaoNasWcOIESPylSeAKlWqMGTIEN544w0VKBGRcmTST5O5eR05ccvghw1Qp15uaRoyAiuolt3xRERELlqFFqijR48SEhJS4Pq6dety9OjRUg0lIiJnZ375CbP8U8zqOE7XrIXVpjPWgLuxate1O5qIiMglodACVa1aNQ4cOECNGjXOun7//v1UrVq1tHOJiMhvTFZm7gh6yz+Fn3djtbsK10PPUKVFG1JTU+2OJyIickkptEC1b9+e2bNn8+STTxIQEJBnXWpqKu+99x5RUVFlFlBE5FJlkg5i4j/DrPwS/KtgdbsJa8xTWJU17LiIiIhdCi1QAwcOZMuWLYwePZquXbtSp04dIPfM0zfffENwcDADBw4s86AiIpcCk5MDm9fhXv4p7NiM1TIK1/BHoHFzLMuyO56IiMglr9ACValSJaZMmcKSJUtYvXo1y5cvB6BWrVr07NmTXr16UaFChbLOKSJyUTPHjmC++RwT/zlYYHW9MXcUvarV7Y4mIiIif1BogQLw9fVlwIABDBgwoFR2OmrUKHx8fDwT8Pbt25dOnTqRmJjIv//9b7KyssjKyqJ79+707t0bgIyMDGJjY0lMTMTlchEdHa1LB0XkgmaMge3f4477FDathSYtcEUPh+Ztsby87I4nIiIiZ1GkAlUWHnzwQerVq5dn2RtvvMGgQYNo27YtJ06c4IEHHqBNmzaEhoaydOlSfHx8mDZtGklJSUyYMIFmzZrh7697AUTkwmJOnsCs+h8m7jM4kYbV+Tqsp2dg1axtdzQREREpRKEFavDgwYW+iGVZzJs3r1QCnTx5EoD09HS8vb09BSkhIYGRI0cCEBwcTEREBOvXr6d79+6lsl8RkbJmfvoRE/dJ7oS39a7A6jE4dxjy387Gi4iIiPMVWqCeeOKJAtd99913fP7553iV4FKTadOm4Xa7adiwIdHR0QQGBjJy5EhefPFF5s2bR2pqKsOHD/cMkZ6SkkLNmjU9zw8ODiYlJaXY+xURKU8mIwOzNi73bNOh/Vgdu+N67CWs0Pp2RxMREZESKLRANW/ePN+ybdu28f777/PTTz9x44030qdPn2LtdNKkSQQFBZGdnc28efOYPn06jz32GEuWLOH222+nU6dOHD58mJiYGMLDwz0j//1xBCpjzDn3sXHjRjZt2gSAj48P0dHR+YZhdwpfX18CAwPtjpGPU3OBspWUU7M5NReUPFvO/r1kfLGEzLjPcAXVpsINvfDtcj1WxUq2ZysPTs3m1FygbCXl1GxOzQXOzgYwd+5csrKyAGjZsiWRkZH2BhL5k2LdA5WYmMi8efPYunUrV199NePHj6datWrF3mlQUFDuzr296dGjB+PGjSM1NZW1a9cyduxYIHeUv4YNG7Jjxw7q1KlDUFAQSUlJnm/45ORkWrduXeA+IiMj833DpaWlFVq87BAYGOjIyTCdmguUraScms2puaB42Ux2NmxcnTsEeeJ2rLadscY8hbmiCRmWRUZWNmSV3vu8WI5beXJqLlC2knJqNqfmAudmsywLf39/oqOj7Y4ick5FKlA///wz8+bNY8OGDXTt2pVXXnmF4ODgEu0wPT2dnJwcKleuDMDKlSupX78+/v7++Pj4sHXrVpo2bUpqaio//vgjvXr1AqBDhw4sW7aM8PBwkpKS2LZtG/fff3+JMoiIlCZzJBkTvwzzzRfgWyF3wtvhj2AFOPc3vCIiIlIyhRaoadOmkZCQQFRUFFOnTvVcTldSx48fZ+rUqbjdbowx1KpVi1GjRuFyuRg/fjwzZ87E7XaTnZ3NrbfeSnh4OAC9evUiNjaWMWPG4HK5GDZsmEbgExHbGLcbfvgudwjyHzbAlW1w3TUOmkZiuVx2xxMREZEyYplCrmkbPHgw3t7ehV4rGxsbW6rBysLBgwd1CV8xODUXKFtJOTWbU3NB/mwm7Tjmmy8x8Z9BViZW1xtyP6rXPMerlE82J3FqNqfmAmUrKadmc2oucG42y7IICQmxO4ZIoQo9AzVixIjyyCEi4ljGGNi1DbP8U8yGBAiPwDXgLmgZheVt23R6IiIiYoNC/+fXPEsicqky6afJWP017s8Ww9EUrE7X4pr4KlbtULujiYiIiE30q1MRkT8xWVmY+M8wHy8go2YtrOt7Y7XtilWhgt3RRERExGYqUCIivzHuHMyaeMxHc8CvIq6hYwnocg1paWl2RxMRERGHUIESkUueMQa+X4/7w1mQfhqr9xCsqKuwXF55JvAWERERUYESkUua+XEr7sUz4fABrB6DsK66CcvHx+5YIiIi4lAlLlA//PADGRkZNGrUSPMxicgFx/yyB/eHs2HnFqwb+mKNm4jlV8nuWCIiIuJwhRao//znP6Snp/OXv/wFALfbzZQpU9iyZQsAAQEBPPnkk1x++eVlm1REpBSY5EOYJe9jvl2J1e0mrLvGYgVUsTuWiIiIXCBchW2wcuVKwsLCPI8TEhLYuXMnkydP5u2336Zhw4bMnz+/LDOKiJw3k3oU9/tv4p44GiwL1+QZuAbfq/IkIiIixVLoGaikpKQ8Z5c2bNhAx44dadSoEQD9+/fnpZdeKruEIiLnwZw+hfn8P5gvPoKIFrgmTMWqqzPmIiIiUjKFFqg/j0C1Y8cO+vfv73kcGBjIyZMnSz+ZiMh5MFmZmLhPMR8vhJBQXA/EYIVH2B1LRERELnCFFqh69eqRkJBA//792b17NykpKTRv3tyzPjk5mapVq5ZlRhGRIjPuHMyq5Zglc6GSP657xsOVrTUcuYiIiJSKQgtU//79efHFF1m9ejVJSUl06tSJmjVretavW7fOczmfiIhdjDGwaQ3uxbMhOwur351Y7bpiuQq91VNERESkyAotUC1btuT5559n8+bNVKlShY4dO+ZZX6dOHbp161ZmAUVECmN2bMmdyynlMFbPwVhdb8Dy1lxOIiIiUvqKNA/UZZddxmWXXXbWdddccw3ffvst9evXL9VgIiKFMft2587llLgtdy6n63ph+VW0O5aIiIhcxEo8ke6uXbuIi4tj5cqVZGVlERUVVZq5REQKZJIOYj6ag9m4Gqv7LVj3jMcKCLQ7loiIiFwCilWgjhw5Qnx8PHFxcRw4cICoqCiGDx9Oy5YtyyqfiIiHOX4U89/5mJVfYrW/CtfkWKzqNQt/ooiIiEgpKbRAZWZmsmbNGpYvX87OnTuJjIxk4MCBTJs2jUGDBhEaGloeOUXkEmZOncQs+xDzvyXQNBLXk69ghZz9smIRERGRslRogRo+fDiNGjWiW7duPPTQQ1SsmHt/wbRp08o8nIhc2kxmBubrTzCffgChYbgenIzVoLHdsUREROQSVmiBqly5MsnJyaSkpJCamuopUCIiZcXk5GAS/odZOg8CAnHd9xA0jdRcTiIiImK7QgvUa6+9xrZt24iLi+ORRx4hJCSETp06lUc2EbnEGGPgu1W4P3wP3DlYA+/GatNZczmJiIiIYxRpEImIiAgiIiK45557WLNmDfHx8RhjiI2NpWPHjrRr145atWqVdVYRuYiZbZtyhyQ/kozV8y9YXa7H8i7xQKEiIiIiZaJYP534+vrStWtXunbtypEjR1ixYgVff/01s2fPZv78+WWVUUQuYmb/PtKmzcT941asm/tjXdMTq4Kf3bFEREREzqrEv96tXr06vXv3pnfv3iQmJpZmJhG5BJiMdMzSeZiv/4vP9b1x3/MAVuUAu2OJiIiInFOpXB9zxRVXlMbLiMglwmxcjfv9t6B6EK7H/k6lpi1ITU21O5aIiIhIoXSDgYiUG5NyGPe8tyBxG1b/u7A6XasBIkREROSCogIlImXOZGdhvvgI8/ECrHZdsZ6OxQoItDuWiIiISLHZUqBGjRqFj48PPj4+APTt25dOnTphjGHhwoWsXLkSb29vAgICiImJASAjI4PY2FgSExNxuVxER0cTFRVlR3wRKQazYwvuObHgcuF6IAYrvKndkURERERKrEQFKjMzk4SEBNLT02nRogV16tQp9ms8+OCD1KtXL8+yTz/9lH379jF16lS8vb05evSoZ93SpUvx8fFh2rRpJCUlMWHCBJo1a4a/v39J3oKIlDGTegzzwTuYDauxev0F65pbNSy5iIiIXPAKvfng3Xff5d///rfncWZmJo8//jhvvPEG8+fP55FHHmHr1q2lEmbJkiXcfvvteP/2Q1a1atU86xISErjxxhsBCA4OJiIigvXr15fKfkWk9Bi3G3fcZ7ifHInJSMf19Gu4buir8iQiIiIXhUJ/otm0aRN33HGH53F8fDxHjx5l2rRp1KhRg9dff51FixbRtGnxLsuZNm0abrebhg0bEh0djbe3N6mpqaxdu5Y1a9YA0KNHDzp16gRASkoKNWvW9Dw/ODiYlJSUYu1TRMqW2ZeI+71YSDuO694HsZq3tTuSiIiISKkqtEClpKRQt25dz+ONGzfSsWNHgoKCgNySM3ny5GLtdNKkSQQFBZGdnc28efOYPn06o0ePJjs7m8zMTKZMmUJKSgoTJkwgNDTUc6mfZVme1zDGnHMfGzduZNOmTQD4+PgQHR1NQIAz55jx9fUlMNB5N9Q7NRcoW0mVVTZz6iSnF/ybjP/9F78eA/Hre3uxJsO9FI9ZaVC24nNqLlC2knJqNqfmAmdnA5g7dy5ZWVkAtGzZksjISHsDifxJoQXK19fX80UMsHPnzjxnpCpUqEB6enqxdnqmfHl7e9OjRw/GjRtHQEAAfn5+dO3a1bNN48aN2b17N/Xq1SMoKIikpCTPN3xycjKtW7cucB+RkZH5vuHS0tIKLV52CAwMdOQcOE7NBcpWUqWdzRiDWb8SM/9fULsurideISsklKyMTMjItC1XaVK2knFqNqfmAmUrKadmc2oucG42y7Lw9/cnOjra7igi51ToPVANGjTg888/B3LP6qSmptK8eXPP+kOHDlG9evUi7zA9PZ2TJ096Hq9cuZL69esD0LlzZzZu3AjAiRMnSExM9Jx96tChA8uWLQMgKSmJbdu20batLg8SsYtJOoD7HzGY99/AGnAXrr89gxUSancsERERkTJV6BmowYMHM2XKFOLj48nIyKBHjx5UrVrVs37lypVEREQUeYfHjx9n6tSpuN1ujDHUqlWLUaNGAXDbbbcxY8YMT2Hr06cPDRo0AKBXr17ExsYyZswYXC4Xw4YN0wh8IjYwWZmYTxdhli3C6nQd1vD/w6qk70URERG5NFimCNe0paamsmPHDqpUqUKjRo3yrFu3bh316tWjVq1aZRaytBw8eFCX8BWDU3OBspXU+WYzW7/DPed1qFgZ15ARWPUbOiJXWVK2knFqNqfmAmUrKadmc2oucG42y7IICQmxO4ZIoYo0rnBgYCDt2rU767qGDRvyzTff0LNnz1INJiLOYY79ipn/NuaHDVh9bsfqfjOWy8vuWCIiIiLlrkQTs2RnZ7Nu3Tri4uLYtGkT1atXV4ESuQiZnBzM1x9jPpqD1aIdrqdnYFUt+j2PIiIiIhebYhWonTt3snz5clatWsWpU6e4+eabGTRokOc+JRG5eJjdO3DPiYWMDFwjHsNqGml3JBERERHbFWkeqLi4OOLj48nMzKRjx45MmDCBJ598kuuuu47QUI26JXIxMSdPYBbPwqz+Guvm/lg39sfy8bE7loiIiIgjFFqgxowZQ7du3RgxYgRNmjQpj0wiYgNjDGbV15gP3oHLr8A18Z9YwbqZV0REROSPCi1QjRs3Zt26dUDuvU9XXnllmYcSkfJlDuzLHV0v6SCuISOgdUcsy7I7loiIiIjjFFqgYmJiPJfxvfXWW5w+fZoOHTqURzYRKWMmIwPz8TzM/5ZidbsZa8wTWH6V7I4lIiIi4lhFGkQiKCiI/v37079/f7Zv3058fDx+fn5MmTKFdu3a0b59e52ZErnAmE1rcb//JlStjuvRl7Auq293JBERERHHK/Yw5k2aNKFJkybcfffdnqHMp0yZwvvvv18W+USklJlfk3HPexN+3IrVfyhW5+uwXC67Y4mIiIhcEIpVoIwxpKWlARAQEECnTp3o1KkTx44dK4tsIlKKTHY26R+9j3vRTKy2nbEmz8AKqGJ3LBEREZELSpEKVEpKCu+99x7fffcd6enpAPj5+dGmTRtuv/12qlfXxJoiTmXST2PWxmG+XEqGlxeusROxGjWzO5aIiIjIBanQApWamsqECRPw9/dn8ODB1K1bF2MM+/fv56uvvmLChAm8+OKLBAQElEdeESkis38vJu5TzKqvITgE67peBN7Ul7RTp+yOJiIiInLBKrRALVmyhKCgICZNmoS39++bR0ZGcsMNNxATE8PSpUuJjo4u06AiUjiTlYXZkIBZ/ins3YXVtguu8U9D/UZYloXlXezbHkVERETkDwr9aWrTpk0MHDgwT3k6w8fHh169evHBBx+oQInYyCQfwsQvw6z8EipWxup2E9boCViVdWZYREREpDQVWqCSkpIICwsrcH39+vVJSkoqzUwiUgTGnQObN+CO+xS2boSW7XDd+zdo0kKj6omIiIiUkUILVHp6OhUqVChwva+vr2dgCREpeyb1KGbFF5gVn0NONlbXG7HuGIVVrYbd0UREREQuekW6IWL79u1Urlz5rOtOnDhRqoFEJD9jDOzcgon7DPPdKmh0Ja5Bw6BleywvL7vjiYiIiFwyilSgXn755bLOISJnYU6dwKz6GhP3GaQew+p8La6Y17Bq1bE7moiIiMglqdACNX/+/PLIISJ/YPbuwiz/FLM2HkLDsG7qnzv5rW/Bl9OKiIiISNnTmMYiDmEyMjDrV+QOQX7wZ6yobrgeeR6r3hV2RxMRERGR3xSpQGVlZbF06VJWrVrFoUOHAKhduzadOnWiZ8+e+Pj4lGlIkYuZOfRL7r1NCf+DqjWwut+MFdUdq9LZ7zsUEREREfsUWqBOnz5NTEwMhw4donPnznTv3h2A/fv38+GHH7J27VomTpyIn59fWWcVuWiY7GzYtAb38k9h11as1p1wjXoCGjbFsiy744mIiIhIAQotUAsXLsQYw7Rp0wgMDMyzbvDgwTz77LMsXLiQO+64o8xCilwszJEUzIplmBVfgLd37oS39z2EFVjV7mgiIiIiUgSFFqg1a9YwYsSIfOUJoEqVKgwZMoQ33nhDBUqkAMbthq0bcye83fwtNGuFa+hoaNYKy6UhyEVEREQuJIUWqKNHjxISElLg+rp163L06NFSDSVyMTBpqZiEL3OHIE8/jdX1Bqy/3IdVI9juaCIiIiJSQoUWqGrVqnHgwAFq1Khx1vX79++natWqpZ1L5IJkjCF7xxbcn3yAWb8SGjTG6nsHVqsOWN4abEVERETkQldogWrfvj2zZ8/mySefJCAgIM+61NRU3nvvPaKiooq101GjRuHj4+MZva9v37506tTJs3758uXMmDGDRx55hDZt2gCQkZFBbGwsiYmJuFwuoqOji71fkbJk9ibinvs6aQd/wep4Na4nX8GqU8/uWCIiIiJSigotUAMHDmTLli2MHj2arl27UqdOHSD3zNM333xDcHAwAwcOLPaOH3zwQerVy//D5a+//sqXX35Jw4YN8yxfunQpPj4+TJs2jaSkJCZMmECzZs3w9/cv9r5FSpM5fQrz0RzMimVYN/SlysR/kJaZZXcsERERESkDhRaoSpUqMWXKFJYsWcLq1atZvnw5ALVq1aJnz5706tWLChUqlFqgN998k6FDhzJnzpw8yxMSEhg5ciQAwcHBREREsH79es+w6iLlzRgD367EPe9fULsurif+gRUSiuVXEVSgRERERC5KhRaorVu30rhxYwYMGMCAAQNKbcfTpk3D7XbTsGFDoqOjCQwM5PPPPyc0NDTf2SeAlJQUatas6XkcHBxMSkpKqeURKQ6TdBD3+2/A3kSsQcOworpp/iYRERGRS0ChBWrSpEm8+eabVKlSpdR2OmnSJIKCgsjOzmbevHlMnz6dYcOG8b///Y/JkycX+Lw//oBqjDnnPjZu3MimTZsA8PHxITo6Ot89XE7h6+t71mHi7ebUXGBfNpOVSfqSeaR/NBffq26g4vhJuPzzfl3puBWfU3OBspWUU7M5NRcoW0k5NZtTc4GzswHMnTuXrKzcKzlatmxJZGSkvYFE/qTQAlUWgoKCcnfu7U2PHj0YN24cO3fu5MiRI4wfPx6AY8eO8frrrzN48GCuu+46goKCSEpK8nzDJycn07p16wL3ERkZme8bLi0trdDiZYfAwEBSU1PtjpGPU3OBPdnM9u9xz4kFb19c458m54omnHAb+FMOHbfic2ouULaScmo2p+YCZSspp2Zzai5wbjbLsvD39yc6OtruKCLnVKQCVZqXJqWnp5OTk0PlypUBWLlyJfXr16dLly506dLFs11MTAy33nqrZxS+Dh06sGzZMsLDw0lKSmLbtm3cf//9pZZLpCAm9Rhm4TuY71Zj9YnGuronlpcmwBURERG5FBWpQL300kt4e59704kTJxZph8ePH2fq1Km43W6MMdSqVYtRo0YV+rxevXoRGxvLmDFjcLlcDBs2TCPwSZkybjfmm88xi2ZCk5a4np6OVT3I7lgiIiIiYqMiFajw8PBSG2mvVq1avPjii4VuFxMTk+exn5+f5/I+kbJmfv4J93sz4PhRXMMexGrRzu5IIiIiIuIARSpQffr0KdVBJEScyqSfwix5HxP3Kda1vbB6DMYqxWH6RUREROTCZssgEiJOY4yB71bjnvcW1KyFa8LLWHXyT/QsIiIiIpc2FSi55JmUw7jffxN278AaeDdWx2s0p5OIiIiInFWhBWr+/PnlkUOk3JnsLMwXH2H+Oz93ItzJM7D8nTsvhoiIiIjYT2eg5JJkdm7B/V4suFy4xk/CCm9qdyQRERERuQCoQMklxaSlYha9g1m/EuvW27CuvRWrkCH6RURERETO0E+Ockkwbjdm5Ze5czo1bIpr0nSsGjXtjiUiIiIiFxgVKLnomf17c+d0OpKM664xWJEd7I4kIiIiIhcoV2EbPPfcc5w6dcrzePHixZw8edLzOC0tTRPciiOZjHTcH7yL+9m/YV3RJPesk8qTiIiIiJyHQs9Abdy4kaysLM/jjz76iM6dO1O5cmUAcnJyOHDgQNklFCkBs3FN7tDk1YNwPfZ3rNAwuyOJiIiIyEWg2JfwGWPKIodIqTC/JuOe9yb8uBWr/1CsztdhuQo90SoiIiIiUiS6B0ouCiY7G/O/pZil72O16Zw7p1NAFbtjiYiIiMhFpkgFyrKsPH//42MRu5ld23IHiXC7cY15CqvxlXZHEhEREZGLVJEK1MMPP+wpTenp6Tz55JO4frssSpf0iV3MyTTMopmYNXFYPQdjXd8by9vH7lgiIiIichErtECNGDGiPHKIFJkxhoy4z3DPmgH1G+GKmYZVs7bdsURERETkElBogerevXs5xBApGrN3F+55/+L0kSRcd4yEVh11SamIiIiIlJsiDyLxyy+/sGHDBpKSkrAsi5o1a9KuXTtCQkLKMp8IACb1KObD9zBr47Cu602VwX8nLTPb7lgiIiIicokpUoGaO3cuH330Ef7+/oSEhGCMISEhgblz59KvXz8GDRpU1jnlEmWyszD/+y/m4/kQEYkr5jWsmrWx/CpBZqrd8URERETkElNogVq/fj0ff/wxo0ePpkuXLp7LpYwxrFixgjfffJPw8HBat25d5mHl0mGMge/X4V7wb/DxwTXycawmLeyOJSIiIiKXuEIL1Jdffknv3r3p2rVrnuWWZXHVVVdx8OBBvvjiCxUoKTXmwD7c89+Gfbuwet+O1fUGLC8vu2OJiIiIiOAqbIOffvqJtm3bFri+Xbt27N69u1RDyaXJnDyBe95buJ95ECskFNczb+DqfrPKk4iIiIg4RqFnoNLS0qhWrVqB66tWrUpaWlqphpJLi8nJwaxYhvloDlwejuvJV7BCLrM7loiIiIhIPoUWqJycHLzOcQbA5XKRk5NTqqHk0mG2bcI9/1+QlYXr7gegeVsNSy4iIiIijlWkUfheeOEFvL3Pvml2toaSluIzyYdwL/w3bP8eq+dgrGt6Ynn72B1LREREROScCi1QAwYMKPRFIiMjSyOLXAJM+inMJx9g/rcEK6o71jOxWIEFXyIqIiIiIuIkhRaogQMHlkcOucgZtxuzejlm8SwIro3rkRew6l1hdywRERERkWIp0iV8IufDJG7Pvc/p+BGswfdite2s+5xERERE5IJkS4EaNWoUPj4++Pjk3vPSt29fOnXqxIwZM9ixYwe+vr5UqlSJu+++m7CwMAAyMjKIjY0lMTERl8tFdHQ0UVFRdsSXIjJHf8UsnonZsArrpv5YN/TFqlDB7lgiIiIiIiVm2xmoBx98kHr16uVZ1r59e4YPH46Xlxfffvstr7zyCq+++ioAS5cuxcfHh2nTppGUlMSECRNo1qwZ/v7+dsSXczCZGZgvPsJ8+gFWy/a4Js/Aql7T7lgiIiIiIufNUZfw/XHC3oYNG5KcnIzb7cblcpGQkMDIkSMBCA4OJiIigvXr19O9e3eb0sqfGWNgQwLuhe+AfyCuB2KwwpvaHUtEREREpNScV4HKzs5m/fr1xMXF8cgjjxTrudOmTcPtdtOwYUOio6MJDAzMs/6TTz6hVatWuFwuAFJSUqhZ8/ezGMHBwaSkpJxPfClF5uefcM97Cw79gtXvTqyO12D99rkTEREREblYlKhA7dq1i+XLl7Nq1SqMMbRu3bpYz580aRJBQUFkZ2czb948pk+fzmOPPeZZHx8fz6pVq3j66afzPO+PAw8YY865j40bN7Jp0yYAfHx8iI6OJiAgoFg5y4uvr2++AukERcnlTj3G6flvkxm3jAo396fiYy9gVarsiGx2Ubbic2ouULaScmo2p+YCZSspp2Zzai5wdjaAuXPnkpWVBUDLli01XY44TpEL1K+//kp8fDzx8fEcO3aMU6dOMWrUKLp06eI5S1RUQUFBuTv39qZHjx6MGzfOsy4hIYEPPviAp556iipVquR5TlJSkucbPjk5+ZzFLTIyMt83XFpaWqHFyw6BgYGkpqbaHSOfc+Uy2dmY5R9jlsyDRs1wTfwn2bXqkJadA+XwXpx6zEDZSsKpuUDZSsqp2ZyaC5StpJyazam5wLnZLMvC39+f6Ohou6OInFOhBSo+Pp64uDh27dpFmzZtGDJkCJGRkdxxxx00aNCg2OUpPT2dnJwcKlfOPUuxcuVK6tevD+SWp3nz5vHkk096StYZHTp0YNmyZYSHh5OUlMS2bdu4//77i7VvKR1m87e4F7wNloXrrw9jNW1ldyQRERERkXJRaIGaMWMGPXr04JFHHsHX1/e8d3j8+HGmTp2K2+3GGEOtWrUYNWoUkHtfVNWqVXnxxRc92z/11FMEBATQq1cvYmNjGTNmDC6Xi2HDhmkEvnJmDv2Ce8G/IXE7Vu9orG43Y3l52R1LRERERKTcFFqg+vXrx4oVK1i3bh2dOnWiS5cuhIaGlniHtWrVylOQ/uj9998v8Hl+fn6MHz++xPuVkjOnTmD+Ox+z/FOsLtdh3fMAlr9zr50WERERESkrhRaoQYMGMWjQILZt20ZcXBxPPPEENWrUwBjDsWPHzqtMibMZdw7u+GWY/7wHoWG4Hv87VmiY3bFERERERGxT5EEkIiIiiIiI4J577mHt2rXEx8czZcoU6tatS/v27Rk0aFBZ5pRyZpIOkDbl75iTJ3DdOQpaRuUZBVFERERE5FJU7GHMfX196dKlC126dOHIkSOsWLGC+Ph4FaiLiDmSjPvlp6jQtjOm9+1YPj52RxIRERERcYTzmki3evXq9O7dm969e5dWHrGZST2G+5WnsJpGUvHusWSnpdkdSURERETEMQotUPPnzy/0RSzL0hmoi4A5dQL3PyZiXdYA6/YRumRPRERERORPCi1Q27dvL48cYjOTfhr3P5+G6jWx7hmP5dLw5CIiIiIif1ZogZo4cWJ55BAbmaxM3DOeBW8fXMMfxvI+rys7RUREREQuWsX6Sfn48eMkJydjWRY1a9YkMFBzAV3oTHY27jdfgvTTuB58Gsvn/CdLFhERERG5WBWpQO3evZu3336bXbt25VneqFEjhg0bRlhYWFlkkzJm3G7MzH9C8iFc//csll8luyOJiIiIiDhaoQUqKSmJSZMm0bBhQ/7v//6POnXqAPDLL7+wbNkyYmJieOmll6hZs2aZh5XSY4zBvP8GZvdOXA8/h1U5wO5IIiIiIiKOV2iBWrp0KY0bN+bxxx/Ps7xOnTq0b9+eZ599lqVLl3LPPfeUWUgpfWbxLMz363A9/AJWlWp2xxERERERuSC4Cttgy5Yt3HLLLQWuv+WWW9i8eXOphpKy5f5kIWbll7jGT8aqoTOHIiIiIiJFVegZqJSUFEJDQwtcX7duXVJSUko1lJQd91f/xSxbnHvPU+26dscREREREbmgFHoGKjMzEx8fnwLX+/j4kJmZWaqhpGy4E77CfDgb19iJWKH17Y4jIiIiInLBKdIofCtXrsTPz++s606fPl2qgaRsmA0JmLmv4xo1AeuKJnbHERERERG5IBVaoIKCgvj4448L3Uacy/zwHe63X8F130NYES3tjiMiIiIicsEqtEBNnz69PHJIGTE/bsUd+zzWnaOxIqPsjiMiIiIickEr9B4ouXCZvYm4p03GGnQ3rqhudscREREREbngFVqgxo8fz4kTJzyP33jjDVJTUz2Pjx8/zu2331426aTEzMGfcf9jIlaPQbiuusnuOCIiIiIiF4WzFqiMjAzP3w8cOEBOTo7ncUJCQp6BI4wxZGVllWFEKS6TfAj3y09idbsJ14197Y4jIiIiInLROGuBeu655/KUqD8yxpRpIDk/5tivuF95CqtNZ6zeQ+yOIyIiIiJyUTlrgbr55pv58ssvyzuLnCeTlor75aewGjbDGjQMy7LsjiQiIiIiclE56yh8UVFRuN1uz2NjjOexZVl5Hv9xO7GPOX0K96sxUOey3BH3XBofRERERESktBU4jLnrDz+ADx8+PM+6cePGlV0iKTaTkYF72tMQEIjr3r9heXnZHUlERERE5KJU6DxQEydOLI8cUkImOwv3688B4PrrY1jePjYnEhERERG5eBVaoJo2bVoeOaQETE4O7n9NhbRUXH97BqtCBbsjiYiIiIhc1AotUIcPHy7SC9WqVavIOx01ahQ+Pj74+OSeLenbty+dOnXi+PHjvPbaaxw+fBgfHx/uu+8+mjRpAuQOrR4bG0tiYiIul4vo6GiioqKKvM+LjXG7MbNfgwM/4/q/57AqVrI7koiIiIjIRa/QAjV27NgivdD8+fOLteMHH3yQevXq5Vk2Z84cGjZsyIQJE9i1axcvv/wy06ZNw8vLi6VLl+Lj48O0adNISkpiwoQJNGvWDH9//2Lt92JgjMEseBuz8wdcDz+HFRBodyQRERERkUtCoQXKz8+PSpUqcdVVV9GpUycqVSq7Mx2rVq1i+vTpAISHh1OlShW2b99Os2bNSEhIYOTIkQAEBwcTERHB+vXr6d69e5nlcSqzZC7m25W4Hn4eq2oNu+OIiIiIiFwyCi1Qb731FmvXriUuLo5PP/2UVq1a0a1bNyIjI/OM1Fdc06ZNw+1207BhQ6Kjoz3DowcG/n42pWbNmqSkpACQkpJCzZo1PeuCg4M96y4l7mUfYpZ/mnvmqWZtu+OIiIiIiFxSCi1Qvr6+dOnShS5dunDkyBFWrFjB3LlziY2NpXPnztx+++14exf6MnlMmjSJoKAgsrOzmTdvHtOnT2f06NGFTvz6x/XGmHNuu3HjRjZt2gSAj48P0dHRBAQEFCtnefH19c1THAuS8eVSTn+ygIAnX8G7QSPH5LKDspWMU7M5NRcoW0k5NZtTc4GylZRTszk1Fzg7G8DcuXPJysoCoGXLlkRGRtobSORPitV8qlevTu/evbnyyiuZNWsWn376KQMGDCj2fUhBQUG5O/f2pkePHowbN85TblJTUz3f1MnJyZ5tg4KCSEpKyrOudevWBe4jMjIy3zdcWlpaocXLDoGBgaSmpp5zG/eaOMzsGbjGTeRUUG0oZPvyymUXZSsZp2Zzai5QtpJyajan5gJlKymnZnNqLnBuNsuy8Pf3Jzo62u4oIudU5Gvwfv31V/7zn/8wfvx4XnzxRa644gpeeumlYpen9PR0Tp486Xm8cuVK6tevD0CHDh347LPPANi1axfHjh3zjMLXoUMHli1bBkBSUhLbtm2jbdu2xdr3hcpsWouZPR3XiEexGmpYeRERERERuxR6Bmr58uXEx8eTmJhImzZtGDp0KC1atCjx/U/Hjx9n6tSpuN1ujDHUqlWLUaNGATBkyBBee+01xo4di7e3N2PGjMHLywuAXr16ERsby5gxY3C5XAwbNuySGIHPbNuE+62/47rnAaxmreyOIyIiIiJySbNMIde0DR48mBo1atC+fXsqVqx4zu2c7uDBgxfUJXwmcTvuf0zEuu1+XJ2udUwuJ1C2knFqNqfmAmUrKadmc2ouULaScmo2p+YC52azLIuQkBC7Y4gUqtAzUBEREViWxd69e8sjj/zG/PIT7n8+jdX3DlvKk4iIiIiI5FdogYqJiSmHGPJH5tB+3C8/hXVDH1zX9LQ7joiIiIiI/KbkEzlJmTC/JuN+5Umsztdh3TLQ7jgiIiIiIvIHKlAOYlKP4n75SawW7bD63VnovFgiIiIiIlK+VKAcwpw8gfuViVgNGmHdNlzlSURERETEgVSgHMCcPoX71RgIqoV11zisEg4RLyIiIiIiZavQQSSkbJmsTE68GgN+FXHd/39Yv817JSIiIiIizqNTHTYyGem4X38Bk5mBa9QELB9fuyOJiIiIiMg56AyUTcyh/bhffx4q++P/6POcyHHeBL8iIiIiIpKXCpQNzIZVuN99FavrjbkT5VYOAAfOCC4iIiIiInmpQJUjk5OD+XAWJn4ZrqFjsdp0sjuSiIiIiIgUgwpUOTHHj+J+8yU4kYrr8b9j1Q61O5KIiIiIiBSTClQ5MLu24n79RazGzbHGPoVVwc/uSCIiIiIiUgIqUGXIGIP53xLMh+9h9R+KdXUPTZArIiIiInIBU4EqIyb9FGbma5hd23A9OBnriiZ2RxIRERERkfOkAlUGzMGfcc94DqpWx/XkK1iBVe2OJCIiIiIipUAFqpS5132DmTUN65qeWL2jsVxedkcSEREREZFSogJVSkx2NuaDdzAJX+Ea9iBWZJTdkUREREREpJSpQJUCc/RX3G++COnpuJ54GSs4xO5IIiIiIiJSBlSgzpPZ/j3uN1/CurIN1pARWBUq2B1JRERERETKiApUCRljMMsWY5bOwxp8L1bXGzREuYiIiIjIRU4FqgTMqZO433kVft6N6/+exQpraHckEREREREpBypQxWR++Ql37PNQs3bu/U7+gXZHEhERERGRcqICVQzu1V9j3ovFuqEvVs9BGqJcREREROQSowJVBCYrC7PgX5h13+D66yNYV7axO5KIiIiIiNhABaoQ5tdk3G+8AG537iV7QbXsjiQiIiIiIjZRgToHs/U73G9NxWrdEesv92H5+NodSUREREREbGRrgVq4cCELFy7k73//O/Xq1SMxMZF///vfZGVlkZWVRffu3enduzcAGRkZxMbGkpiYiMvlIjo6mqioqDLJZdxuzCcLMZ8twrptOK7O15bJfkRERERE5MJiW4HavXs3P/74I0FBQZ5lb7zxBoMGDaJt27acOHGCBx54gDZt2hAaGsrSpUvx8fFh2rRpJCUlMWHCBJo1a4a/v3+p5jInT+B++2U49Auuh5/HqtegVF9fREREREQuXC47dpqVlcXbb7/Nvffem2/y2ZMnTwKQnp6Ot7e3pyAlJCRw4403AhAcHExERATr168v1VxmXyLuZ8aDy5V7v5PKk4iIiIiI/IEtZ6Dmz59P165dCQ4OzrN85MiRvPjii8ybN4/U1FSGDx9O1apVAUhJSaFmzZqebYODg0lJSSm1TO5vvsC8/ybWLQOxbh6A5bKlW4qIiIiIiIOVe4HauXMniYmJDBkyJN+6JUuWcPvtt9OpUycOHz5MTEwM4eHh1KlTByDP2SpjzDn3s3HjRjZt2gSAj48P0dHRBAQE5NvOZGZw6p1/4l73Df7/NwWfFm3P5+2ViK+vL4GBzpuQ16m5QNlKyqnZnJoLlK2knJrNqblA2UrKqdmcmgucnQ1g7ty5ZGVlAdCyZUsiIyPtDSTyJ+VeoLZu3cqBAwcYPXo0AL/++itTpkxhyJAhrF27lrFjxwJQq1YtGjZsyI4dO6hTpw5BQUEkJSV5vuGTk5Np3bp1gfuJjIzM9w2XlpaWp3iZ5EO4X38BvL1xPfEyp6vX5HRqaim/48IFBgaSasN+C+PUXKBsJeXUbE7NBcpWUk7N5tRcoGwl5dRsTs0Fzs1mWRb+/v5ER0fbHUXknMr9OrU+ffrwxhtvMH36dKZPn06NGjWYMGECXbp0wcfHh61btwKQmprKjz/+yGWXXQZAhw4dWLZsGQBJSUls27aNtm1LfrbIbF6P+5kHsa5oguv/nsWqXrPwJ4mIiIiIyCXNMfNAuVwuxo8fz8yZM3G73WRnZ3PrrbcSHh4OQK9evYiNjWXMmDG4XC6GDRtWohH4jDsHs3Qe5oslWLePwNWheym/ExERERERuVjZXqCmT5/u+XuLFi1o0aLFWbfz8/Nj/Pjx57UvcyIN95svwa9JuB57Eavu5ef1eiIiIiIicmmxvUCVJ/eLj0LturgmTMWqWMnuOCIiIiIicoG5pAqU1e0mrKt75Jt7SkREREREpCguqcmOXNfeqvIkIiIiIiIldkkVKBERERERkfOhAiUiIiIiIlJEKlAiIiIiIiJFdEkNIiEiIiIiF7/s7GxycnLsjiEXKC8vL7y9C65JKlAiIiIiclHJycnh119/tTuGXKBq1KhxzgKlS/hERERERESKSAVKRERERESkiFSgREREREREikgFSkREREREpIhUoEREREREbBQVFUXdunVp2LChLfufO3cuUVFRnscPPPAAY8aMsSXL+ThX7i1btlC3bl3q1q173u9NBUpERERExGZPPfUUq1atAmDq1Kk88MADnnVnCtY333yT5zn79+/nsssuo27dumRnZ5dalqeffpopU6ac12vMnz/fU1jO9vHHwvZn2dnZ1K1bl4SEhPPKEBUV5XmNJk2a8N1333Hrrbee12uChjEXEREREbFdQEAAQUFBBa4PCQlh0aJFdOnSxbNs0aJF1KpVi4MHD5ZqlsDAwPN+jV69enH11Vd7Ht90002MHDmSXr16AblzLZUnb29vgoOD8fPzO+85wnQGSkRERETE4fr168cnn3zC6dOnPcs++OAD+vfvn2/b+Ph4br75Zq644gq6dOnCu+++m2d9QkIC3bp144orruDOO+/k6NGjedb/+VK4adOmebbv3Lkzb7/9dqF5K1asSHBwsOfDsiwCAgI8j/ft28ett95K/fr1adu2LTNmzPA8t3PnzgAMHDiQunXres7Gvf/++1x//fWEh4fTvn17XnzxxVI981ZUOgMlIiIiIpccYwykny58w6Lyq4hlWaX3en8SFhZG48aN+eyzz+jbty8bNmzgxIkTdOnShddee82z3a5du7jvvvuIiYmhU6dO7Ny5k7/97W9Uq1aN3r17k5qayr333kvv3r0ZNmwYCQkJPP/88wQEBBS4b19fX1566SVq167Nxo0b+dvf/kZYWBjXXnttid7LiRMnuOOOO7jpppt4+eWX+eGHH3jooYcICQmhb9++LF26lFatWvHWW2/Rtm1b/Pz8AHC73Tz55JOEhYWxa9cu/va3vxEcHMxdd91VohwlpQIlIiIiIpee9NO4x/6l1F7O9c95ULFSqbzW3/72t7MuHzBgAIsWLaJv374sXLiQvn375rsUbsaMGQwZMoTbbrsNgMsvv5x7772XuXPn0rt3bz788EMqVqzI5MmT8fb2Jjw8nFWrVrFhw4YC8wwfPtzz93r16rFy5Uo+/vjjEheoxYsXU6FCBZ5//nm8vb1p2LAhW7du5a233qJv375Ur14dgKpVqxIcHOx53pAhQ/LkGDZsGB9//HGBBWrNmjUlylcYFSgRERERufT4VcwtPaX4emWtV69ePP300/zyyy8sWbKEDz74IN/ld9u3b2f79u3Mnj3bsywnJ4datWoBkJiYSPPmzfH2/r0GREZGnrNAffnll7z22mv89NNPnDp1iqysLDp06FDi93G2DG3atGHmzJnnfN66det4+eWX2blzJ6mpqeTk5BASElLiHCWlAiUiIiIilxzLskrtjFF5qVq1KldffTVjx46lTp06RERE5Bup7uTJk9x333385S95z679sawU51LDvXv3ct999zFy5EgmTZpEQEAAM2bMYM+ePSV+H8aYYj/nxIkTDB06lJ49e/LQQw9RtWpV/vOf/7BgwYIS5ygpFSgRERERkQvEgAEDuOeee3jqqafOur5p06bs3r2b+vXrn3V9gwYN+OSTT8jJyfFc/rdp06YC97d582b8/Pz4v//7P8+yffv2ncc7gPDwcD7++GOys7M9xe7bb78lPDwcyB2hz+Vy5RktLzExkePHjzNhwgSqVKkCwIEDB84rR0lpFD4RERERkQvE9ddfz+bNmxk2bNhZ148YMYIvv/ySF154gZ07d7Jjxw7mz5/vGYmvb9++nDhxgqeeeopdu3bx3nvvERcXV+D+wsLCSEtLY/78+fz000/84x//OGfhKop+/fqRkZHBY489xq5du/jPf/7Dv//9b+69914g9wxZnTp1WLlyJSkpKZw8eZK6devi4+PDzJkz2bt3L7NmzeKzzz47rxwlpQIlIiIiInKBcLlcVK9ePc8leX/UokUL3n//fVavXs3NN99M3759WbBgAZdddhkAVapU4V//+hfx8fHccMMNfPbZZ4wYMaLA/V155ZU88sgjTJkyhZtuuolffvmF22+//bzeg7+/P7Nnz2bbtm1cf/31TJ48mQcffJC+fft6tpkwYQIffvghrVq1YsKECQQFBfHCCy8wc+ZMrr32WuLj4xk9evR55Sgpy5TkIsQL1MGDB0t0zWVZCwwMJDU11e4Y+Tg1FyhbSTk1m1NzgbKVlFOzOTUXKFtJOTWbU3OBc7NZllVqAwJkZGTw66+/lsprlYeoqCjGjRtHdHS03VEueg888AA5OTlMmzatwG1q1KhBhQoVClyvM1AiIiIiIjZ74okniIyMtDvGRWvbtm00bNiQDz/88LxfS4NIiIiIiIjYaNGiRWRlZeFyXVjnNvbv30/37t3Pui4qKor33nuvfAOdwxVXXMHnn38O5F5CeD5UoEREREREbBQaGmp3hBKpVauWp5T8mZ+fXzmnOTdfX98CRyYsLlsL1MKFC1m4cCF///vfqVevHsYYFi5cyMqVK/H29iYgIICYmBgg91rW2NhYEhMTcblcREdHExUVZWd8EREREZFLlre3d6mVkguJbQVq9+7d/PjjjwQFBXmWffrpp+zbt4+pU6fi7e2dZ2blpUuX4uPjw7Rp00hKSmLChAk0a9bsvE/BiYiIiIiIFJUtF1pmZWXx9ttvc++99+aZCXnJkiXcfvvtnmEZq1Wr5lmXkJDAjTfeCEBwcDARERGsX7++fIOLiIiIiMglzZYzUPPnz6dr164EBwd7lp06dYrU1FTWrl3LmjVrAOjRowedOnUCICUlhZo1a3q2Dw4OJiUlpVj7/WNZcxqnZnNqLlC2knJqNqfmAmUrKadmc2ouULaScmo2p+YCZ2ZzYiaRsyn3ArVz504SExMZMmRInuU5OTlkZ2eTmZnJlClTSElJYcKECYSGhlKvXj0g7zdWYfM5bdy40TNLcsWKFRk0aBC1a9cu5XdTepx6KaJTc4GylZRTszk1FyhbSTk1m1NzgbKVlFOzOTUXODvbggULOH36NAAtW7Ys0dDeXl5e1KhRo5STyaXCy8vrnOvL/RK+rVu3cuDAAUaPHs2oUaP49ddfmTJlCrt27cLPz4+uXbsCEBQUROPGjdm9e7fncVJSkud1kpOT89w/9WeRkZEMHTqUoUOHMmjQIBYsWFC2b+w8zJ071+4IZ+XUXKBsJeXUbE7NBcpWUk7N5tRcoGwl5dRsTs0Fzs62YMECBg0a5PkZrqTzInl7e1OhQgV96KNEH2duJypIuReoPn368MYbbzB9+nSmT59OjRo1mDBhAq1ataJz585s3LgRgBMnTpCYmOg5+9ShQweWLVsGQFJSEtu2baNt27ZF3u+Z32Q4UVZWlt0RzsqpuUDZSsqp2ZyaC5StpJyazam5QNlKyqnZnJoLnJ3NyT+viZzhqHmgbrvtNmbMmOEZT75Pnz40aNAAgF69ehEbG8uYMWNwuVwMGzbM0aefRURERETk4mN7gZo+fbrn74GBgTz66KNn3c7Pz4/x48eXeD8tW7Ys8XPLmlOzOTUXKFtJOTWbU3OBspWUU7M5NRcoW0k5NZtTc4GyiZwvyxQ2GoOIiIiIiIgANs0DJSIiIiIiciFSgRIRERERESkiFSgREREREZEisn0QibJ2+vRp7r//fjp37sxf//pXu+N4jBo1Ch8fH3x8fMjOziYsLIzhw4fj5+dndzRycnL48MMP+eabb3C5XLjdbiIiIrj99tupXLmybbn+eMwyMjIIDQ2ld+/eNG7c2LZMZ8t2xrhx4wgNDbUx1e/+nK9hw4bcf//9NqeC7OxsPvzwQ1auXInL5cLb25ugoCAGDRpEWFiYbblGjRrFI4884plGASAmJoZbb72VNm3a2Jbrj86W0SmcmO2PmTIzM/nHP/6Br68vo0ePLnS+j/LM5hSjRo0iIyOD119/3XN8tmzZwtNPP03Pnj258847bc/n4+PD1KlTPRNePvroo9xxxx00a9bM1mzZ2dnMmzePNWvW4O3tjcvlomfPnlx99dW25oL8P3vceOON3HTTTXbHynfMLMvilltu4brrrrM7mshZXfQFauXKlTRo0IA1a9Zw1113OaKgnPHggw9Sr149jDG88MILLF++3BH/kMXGxnLixAmeeeYZ/P39cbvdrFmzhhMnTthaoOD3Ywawbt06nnvuOSZMmEDDhg1tzQV5szmRE/PNmDGD9PR0z9cawPr16/n5559tLVBy8Tp16hQvvvgiderU4d5778Xl0oUYBQkKCmL9+vV06NABgK+++oorrrjC5lS/y8rK4quvvuL666+3O0oe06dPJysri5deegk/Pz+SkpJ47rnnyMnJcUQhOPN/QUpKCg899BARERFcfvnltmYq6JhlZ2c74ucikT+76P/n+Prrr+nduzdNmjQhISHB7jhnlZWVRUZGhiPmtTp06BCrV69m5MiRnjwul4uOHTtSq1Ytm9Pl1a5dO2644QaWLl1qdxQpgYMHD7Ju3bo8X2sAbdu2pWvXrjYmk4tVamoqkyZN8pyBVXk6t6uvvpqvv/4ayC2eP/74o6OGmB40aBCLFi0iIyPD7igehw4dYt26dXmuKAkODuaOO+5g0aJFNqfLKygoiJCQEA4ePGhrjnMds8WLF9uaTaQgF/UZqJ9//pmUlBQiIyPJyclhyZIlXHPNNXbH8nj55Zfx8fEhKSmJBg0a0LFjR7sjsXv3bkJCQggMDLQ7SpGEh4ezbt06u2MAv38+z3juuedsvzToj/6Yb+DAgbRv397WPD/99BO1a9d2xC8OzubPn89Dhw7ZmEZKwyuvvMK1115LdHS03VEuCBEREXz22WccOXKE9evX07FjR0eVzvr16xMREcHHH39Mv3797I4D/P5/aEBAQJ7ljRo14tdffyU1NdUx/7/u27ePAwcO2H726VzH7NixYxw7doyqVavaE06kAM756a4MfPXVV3Tr1g2Xy0Xr1q156623+OWXXxxzX8qZ0+g5OTm8+eabzJkzx/bryi80TprGzImXyP2R0/MdOnSIqVOnkpmZSUREhO33LP75eMXExNgXRkpFq1atWLVqFTfccANBQUF2x7kgdO3aleXLl7Nu3TrGjh3LihUr7I6Ux1/+8hcef/xxx13G52RnfjlUoUIFRowYQUhIiN2RzsnX19fuCCL5XLQFKjs7mxUrVuDl5cXKlSsByMjI4Ouvv+aOO+6wOV1eXl5eREVF8d5779leoBo0aMDBgwdJS0vL99sgJ0pMTOSyyy6zO4aUQP369Tl06BAnTpzA39+f2rVr89JLL7F8+XK+/fZbu+PJRahXr1589913TJo0iYkTJ6pEFUH37t155JFHqFOnjiN/0K5VqxadO3d2zOVx9evXP+v/oTt37qRGjRqOOPvktF+mneuYXXbZZVSqVMnGdCJn55xz8aVs/fr11KpVizfeeIPp06czffp0Jk+eTHx8PNnZ2XbHy2fLli3UqVPH7hjUrl2bqKgoYmNjOXnyJJB7licuLs5xlzCtW7eOzz//nJ49e9odRUogJCSEtm3b8vrrr3u+1gDS09NtTCUXu969e3P99dcTExNDcnKy3XEcr3r16kRHRzNkyBC7oxRowIABrFixgqNHj9odhZCQENq0acMbb7zhuTcrKSmJ2bNn07dvX5vTOVNBx2zOnDmO+4W3yBkX7Rmor776ii5duuRZVq9ePapVq8a3335LVFSUTcl+d+Y0ek5ODjVr1uS+++6zOxIAI0aMYPHixTz++ON4eXlhjCEiIoK2bdvaHc1zzNLT0wkNDeWxxx6jUaNGdscC8t8zc8899xAREWFjIucbNWqU52vN5XLh7+9PYGAgffr0sTuanIecnBxHX3bTq1cvXC4XMTExTJw4keDgYLsjMXnyZM9w3ABTpkyhRo0aNib6nROG3z6XwMBAbr75ZhYsWGB3FABGjx7NvHnzeOihh/IMY37ttdfaHc2xRo8ezfz583nooYewLIukpCQeffRRIiMj7Y4mclaWcdJNJCIickE7evQoDzzwAG+99ZajS5SIOJPb7Wbu3Ll8//33PPXUU44daEgubSpQIiJSKv773//yxRdfcOuttzpivhsREZGyoAIlIiIiIiJSRBftIBIiIiIiIiKlTQVKRERERESkiFSgREREREREikgFSkREREREpIhUoERERERERIpIBUpERERERKSIvO0OICJSlmJiYti6dSsAFSpUoHbt2vTr14+OHTt6tpk+fTpxcXH5nhsdHU2fPn344YcfmDRpEo0bN2by5Mme9YcOHWLs2LG89tprBAcHs3z5cmbMmAGAy+WicuXKhIWF0bVrV7p164ZlWXlePykpibFjxxIREcHEiRPP+T6SkpIYPXp0vuX33nsvN9xwAwALFixg0aJFjBw5km7dup31OPz1r3/lmmuu8SzPzMzkvvvu4/Tp0/zzn/+kdu3anvf7/vvv4+XlxfLly5k3bx6vv/76WbMNGjSIJ554ghYtWuTb5/bt2/nnP/9JcHBwvuecUblyZRo0aMDtt99O/fr1z3kcRERE7KYCJSIXvVtuuYU+ffqQmZnJqlWrePXVVwkJCSEsLMyzTVRUFMOGDcvzPD8/vzyPd+/ezYYNG2jdunWB+6pWrRovvPACbreb1NRUvv/+e9599102btzIAw88kGfb+Ph4unfvzpo1a0hOTqZmzZqFvpdnnnkmTxmpVKmS5+8rVqygR48exMXF5StQADVq1GDFihV5CtTatWupVKkSp0+fLnTfxZGcnMy+ffvo1q0b8fHxDBgwIN8248ePJyIighMnTvDBBx/wwgsvMG3aNHx8fEo1i4iISGnSJXwictHz8/OjatWqBAcH07t3bypVquQ5K3WGj48PVatWzfPx5wJ1ww03MG/ePM41/7hlWVStWpXq1asTFhZGr169ePTRR0lISOC7777Ls+2ZotOuXTvi4+OL9F4CAwPzZPT19QVg27ZtAAwePJjExERSUlLyPbd9+/bs2rUrz7q4uDi6du1apH0XR1xcHO3atfMUqLOpXLkyVatWJTQ0lH79+nHkyBH2799f6llERERKkwqUiFwy3G43a9as4eTJk3h7F/8EfO/evTl8+DCrV68u1vOaNGlCWFgY69ev9yzbvn072dnZNGnShM6dOxe5QBUkLi6OTp06UaFCBVq3bn3W16tYsSJt27b1rDty5Ajbt2+nU6dO57Xvs4mPj6dz585ERESQmZnJ9u3bC9w2MzPTcwllST4vIiIi5Un/U4nIRe+jjz7i448/Jjs7m5ycHGrUqEGHDh3ybLNq1SrWrVuXZ9mjjz5Ks2bNPI8DAgLo0aMHCxYsICoqqlgZ6tSpQ3Jysufx8uXL6dixI5Zl0bx5c06ePMmOHTto3LjxOV/n//7v//I8nj17tufSxDP3Z3Xu3JnZs2fTr1+/fM/v1q0bM2fOpF+/fqxYsYJWrVrluQywNGzfvp3Tp09z5ZVXYlkWHTt2JC4ujiZNmuTZ7oUXXsDlcpGZmYkxhlatWhEaGlqqWUREREqbCpSIXPSuvfZaevbsyZEjR5g9ezaDBg0iMDAwzzatWrXizjvvzLOsevXq+V6rZ8+eLFu2jPj4+HyFoDBnBpHIzMxk9erVPPHEE0DugBMdOnQgLi6u0AL1yCOPEBQUlGfZ2rVrqVGjBvXq1QMgMjKS6dOns3PnTho1apRn2xYtWnDy5El27dpFfHw8t912W7HeQ1HExcURFRWFy5V7kUPnzp155plnuPvuuz2XHALcf//9NGrUiL179zJv3jzuu+++Us8iIiJS2lSgROSi5+/vT+3atalduzZjxozhySefZOrUqVStWtWzjZ+fH7Vr1y70tSpVqkSvXr344IMPePTRR4ucYf/+/Z5ytG7dOk6dOuUpUADGGCpVqsRdd92Vp2T8WVBQUL6ccXFx7N+/n7/85S+eZW63m7i4uHwFyuVy0aVLF2bPns3x48eJjIzkyJEjRX4fhTlTDk+dOsWXX36ZJ8/69evzXC5YrVo1QkJCCAkJ4dSpU/zjH/9g8uTJ+UYrFBERcRIVKBG5pNSpU4dmzZqxaNGifKPuFdVNN93Exx9/zFdffVWk7bdt28bevXsZMmQIkFt4brzxRq6//vo82z3//PP5SkZhjhw5wubNm5kwYUKeQrhnzx7eeecd7rrrrnyj2nXr1o3//ve/3HTTTaV+z9H69eupUKECkyZNyrP8yy+/9NyndTZXXXUV8+fPZ82aNfkurxQREXESFSgRueTcdNNNTJkyhX79+lGtWjUAsrKyOHbsWJ7tfH19z3p/UIUKFejXrx+zZ8/Ot84Yw7Fjx3C73aSlpbFp0yYWL15Mly5diIyM5OjRo3z//fdMmTLFc8ndGW3btj1nyTibFStWULdu3XxzMNWtW5d33nmH9evX55nzCuDyyy/n7bffzjfK4Lnk5OSwZ8+ePMv8/f3zXU54ZvS9P7+37t278/jjj3P06FHPMf8jb29vrrvuOhYtWqQCJSIijqYCJSKXnKZNm1KnTh2WLl3que9pzZo1rFmzJs921113Hffff/9ZX+O6665j6dKleQaGADh69Cj3339/nol07777bq666iogt/BUq1aNK664It9rtm3blmeffZZjx47lOZt0LvHx8bRr1y7fci8vL1q1akVcXFy+AgW5A2IUx/Hjx3n44YfzLOvWrRujRo3yPD527BibNm3i8ccfz/f8Bg0aUK1aNVasWEGvXr3Ouo/rr7+exYsXs379etq2bVusfCIiIuXFMuea0EREREREREQ8NA+UiIiIiIhIEalAiYiIiIiIFJEKlIiIiIiISBGpQImIiIiIiBSRCpSIiIiIiEgRqUCJiIiIiIgUkQqUiIiIiIhIEalAiYiIiIiIFJEKlIiIiIiISBH9P2iYCAsh0cITAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "renda_notageral=notas_Media.pivot_table(index='Q006', values='Media_Total', aggfunc='mean')\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(renda_notageral, label= ['Media_Total'])\n",
    "plt.legend (ncol = 1, loc= (1.03, 0))\n",
    "plt.xlim(left=0)\n",
    "plt.title('MEDIA TOTAL x RENDA FAMILIAR ')\n",
    "plt.xlabel('RENDA FAMILIAR')\n",
    "plt.ylabel('MÉDIA DE TODAS AS NOTAS')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed70607",
   "metadata": {},
   "source": [
    "O gráfico ficou muito semelhante ao primeiro, que relacionava apenas a redação com a renda. A tendência foi visualizada novamente."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa81e1b5",
   "metadata": {},
   "source": [
    "### Conclusão\n",
    "\n",
    "Através das análises realizadas, foi possível constatar que a média feminina nas matérias foi mais baixa que a média masculina, exceto em redação, onde as candidatas obtiveram o maior desempenho. A maior diferença ocorreu na matéria de matemática e isso pode ter acontecido devido a falta de incentivo para as mulheres entrarem na área de exatas em muitas escolas e famílias.\n",
    "O mesmo pode ser levantado como hipótese para o pior desempenho dos candidatos do sexo masculino em redação, por se manterem mais distantes dessa área, as vezes não considerando como algo importante.\n",
    "\n",
    "A maioria dos inscritos foram pardos e brancos.\n",
    "\n",
    "Foi observado que os candidatos recém formados no ensino médio tiveram maior desempenho.\n",
    "\n",
    "Os estados com melhores notas foram os que possuem maior PIB e as melhores notas foram daqueles candidatos que possuem maior renda familiar. Isso corrobora para a conclusão de que a maior qualidade de vida influência no desempenho dos candidatos, principalmente em relação aos candidatos com as menores rendas, onde as piores notas foram observadas. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1ca4c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}