diff --git a/alura_imersao_Aula02_cp.ipynb b/alura_imersao_Aula02_cp.ipynb
new file mode 100644
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--- /dev/null
+++ b/alura_imersao_Aula02_cp.ipynb
@@ -0,0 +1,4320 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Cópia de Aula02.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-oC76B_Dfojm"
+      },
+      "source": [
+        "## Aula01"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "z9XYg7h2FcNq",
+        "outputId": "4802d881-17e5-4cfd-b508-7f008bafdbb8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        }
+      },
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "fonte = \"https://github.com/alura-cursos/imersao-dados-2-2020/blob/master/MICRODADOS_ENEM_2019_SAMPLE_43278.csv?raw=true\"\n",
+        "\n",
+        "dados = pd.read_csv(fonte)\n",
+        "dados.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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_INSCRICAO</th>\n",
+              "      <th>NU_ANO</th>\n",
+              "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>CO_UF_RESIDENCIA</th>\n",
+              "      <th>SG_UF_RESIDENCIA</th>\n",
+              "      <th>NU_IDADE</th>\n",
+              "      <th>TP_SEXO</th>\n",
+              "      <th>TP_ESTADO_CIVIL</th>\n",
+              "      <th>TP_COR_RACA</th>\n",
+              "      <th>TP_NACIONALIDADE</th>\n",
+              "      <th>CO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>CO_UF_NASCIMENTO</th>\n",
+              "      <th>SG_UF_NASCIMENTO</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>CO_ESCOLA</th>\n",
+              "      <th>CO_MUNICIPIO_ESC</th>\n",
+              "      <th>NO_MUNICIPIO_ESC</th>\n",
+              "      <th>CO_UF_ESC</th>\n",
+              "      <th>SG_UF_ESC</th>\n",
+              "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
+              "      <th>TP_LOCALIZACAO_ESC</th>\n",
+              "      <th>TP_SIT_FUNC_ESC</th>\n",
+              "      <th>IN_BAIXA_VISAO</th>\n",
+              "      <th>IN_CEGUEIRA</th>\n",
+              "      <th>IN_SURDEZ</th>\n",
+              "      <th>IN_DEFICIENCIA_AUDITIVA</th>\n",
+              "      <th>IN_SURDO_CEGUEIRA</th>\n",
+              "      <th>IN_DEFICIENCIA_FISICA</th>\n",
+              "      <th>IN_DEFICIENCIA_MENTAL</th>\n",
+              "      <th>IN_DEFICIT_ATENCAO</th>\n",
+              "      <th>IN_DISLEXIA</th>\n",
+              "      <th>IN_DISCALCULIA</th>\n",
+              "      <th>IN_AUTISMO</th>\n",
+              "      <th>IN_VISAO_MONOCULAR</th>\n",
+              "      <th>...</th>\n",
+              "      <th>TX_RESPOSTAS_CH</th>\n",
+              "      <th>TX_RESPOSTAS_LC</th>\n",
+              "      <th>TX_RESPOSTAS_MT</th>\n",
+              "      <th>TP_LINGUA</th>\n",
+              "      <th>TX_GABARITO_CN</th>\n",
+              "      <th>TX_GABARITO_CH</th>\n",
+              "      <th>TX_GABARITO_LC</th>\n",
+              "      <th>TX_GABARITO_MT</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>Q001</th>\n",
+              "      <th>Q002</th>\n",
+              "      <th>Q003</th>\n",
+              "      <th>Q004</th>\n",
+              "      <th>Q005</th>\n",
+              "      <th>Q006</th>\n",
+              "      <th>Q007</th>\n",
+              "      <th>Q008</th>\n",
+              "      <th>Q009</th>\n",
+              "      <th>Q010</th>\n",
+              "      <th>Q011</th>\n",
+              "      <th>Q012</th>\n",
+              "      <th>Q013</th>\n",
+              "      <th>Q014</th>\n",
+              "      <th>Q015</th>\n",
+              "      <th>Q016</th>\n",
+              "      <th>Q017</th>\n",
+              "      <th>Q018</th>\n",
+              "      <th>Q019</th>\n",
+              "      <th>Q020</th>\n",
+              "      <th>Q021</th>\n",
+              "      <th>Q022</th>\n",
+              "      <th>Q023</th>\n",
+              "      <th>Q024</th>\n",
+              "      <th>Q025</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>190001004661</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1506138</td>\n",
+              "      <td>Redenção</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>17</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1506138.0</td>\n",
+              "      <td>Redenção</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>ACAEAAACABEBABAADCEEEDE*CBDCCCADADCCCBEBBBBDB</td>\n",
+              "      <td>99999CCCABBCAADDBCEBCCADBEEBDECBAABDEACACAEABB...</td>\n",
+              "      <td>DEEDCAECDDEEECBCBECABEBAECBBCDAECAEBBBBBDCCDB</td>\n",
+              "      <td>1</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>420.0</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>4</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>190001004674</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1504208</td>\n",
+              "      <td>Marabá</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>23</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1504208.0</td>\n",
+              "      <td>Marabá</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>ABBCEADCEBAAACACEDCDCDACCCACDCBCABCAACBDBDEDE</td>\n",
+              "      <td>99999ABCEBCEDDECEEEBBCCEECCABDABEEBCDEAEBBEAEA...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
+              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>400.0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>4</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>190001004722</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1501402</td>\n",
+              "      <td>Belém</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>35</td>\n",
+              "      <td>F</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1501402.0</td>\n",
+              "      <td>Belém</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>CDCAACCAAADEACBBEBAAEECDABDEBAEAABEDCABABAABC</td>\n",
+              "      <td>99999ECBCECEBBABECCCECECCEECCDECEEEDADDDDECEDD...</td>\n",
+              "      <td>DDBEADEBBCADCEEACABECBCEBADECADCEDAACBDABABCE</td>\n",
+              "      <td>1</td>\n",
+              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
+              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
+              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
+              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>560.0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>5</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>190001004735</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1507300</td>\n",
+              "      <td>São Félix do Xingu</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>23</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1505437.0</td>\n",
+              "      <td>Ourilândia do Norte</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>EECCCEDBADBEAAAAEBAECEAAAAEEEBACCAC*CEAEDAECE</td>\n",
+              "      <td>99999BABCDABBCBDDAAECADBDDADDCBCEEDCAEBCEAEABD...</td>\n",
+              "      <td>CBDEBBCBDEBCABCDBDEDADCCBEDBCAECEBEBDEBAEECBC</td>\n",
+              "      <td>1</td>\n",
+              "      <td>BEEAAEBEEBADEADDADAEABCEDDDBCBCBCCACBCDADCCEB</td>\n",
+              "      <td>EEBCEEDBADBBCBABCCADCEBACDBBACCACACBEADBBADCB</td>\n",
+              "      <td>ADBBEDCABAABBCBCDAAECDDDBAAAECADECDCEBDEEAECBD...</td>\n",
+              "      <td>BEDEEEAADBEBACABCDBABECECACADCBDCCEDCDABECDDD</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>500.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>190001004776</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1500800</td>\n",
+              "      <td>Ananindeua</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>16</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1500800.0</td>\n",
+              "      <td>Ananindeua</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>CCBCBEACABACABBBBACCBCEBDCBABCADADCBCADBAADEA</td>\n",
+              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
+              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
+              "      <td>0</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>780.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>E</td>\n",
+              "      <td>B</td>\n",
+              "      <td>D</td>\n",
+              "      <td>3</td>\n",
+              "      <td>E</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>D</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 136 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q023  Q024 Q025\n",
+              "0  190001004661    2019                  1506138  ...    A     B    B\n",
+              "1  190001004674    2019                  1504208  ...    A     A    B\n",
+              "2  190001004722    2019                  1501402  ...    A     A    B\n",
+              "3  190001004735    2019                  1507300  ...    A     A    B\n",
+              "4  190001004776    2019                  1500800  ...    A     A    B\n",
+              "\n",
+              "[5 rows x 136 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "roHaxkDKHfDt",
+        "outputId": "f64b2edc-f2ee-497f-985b-c56e398d6d32",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "dados.shape"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(127380, 136)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nE1TlKlrHr1y",
+        "outputId": "a6ee2d33-18cd-4ef6-990c-eda70c3bcfab",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "source": [
+        "dados[\"SG_UF_RESIDENCIA\"]"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0         PA\n",
+              "1         PA\n",
+              "2         PA\n",
+              "3         PA\n",
+              "4         PA\n",
+              "          ..\n",
+              "127375    MG\n",
+              "127376    BA\n",
+              "127377    BA\n",
+              "127378    BA\n",
+              "127379    MG\n",
+              "Name: SG_UF_RESIDENCIA, Length: 127380, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iKsMyeSjJvCa",
+        "outputId": "2ed4b28c-9069-4306-9f54-35ac514a6f2d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 680
+        }
+      },
+      "source": [
+        "dados.columns.values"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['NU_INSCRICAO', 'NU_ANO', 'CO_MUNICIPIO_RESIDENCIA',\n",
+              "       'NO_MUNICIPIO_RESIDENCIA', 'CO_UF_RESIDENCIA', 'SG_UF_RESIDENCIA',\n",
+              "       'NU_IDADE', 'TP_SEXO', 'TP_ESTADO_CIVIL', 'TP_COR_RACA',\n",
+              "       'TP_NACIONALIDADE', 'CO_MUNICIPIO_NASCIMENTO',\n",
+              "       'NO_MUNICIPIO_NASCIMENTO', 'CO_UF_NASCIMENTO', 'SG_UF_NASCIMENTO',\n",
+              "       'TP_ST_CONCLUSAO', 'TP_ANO_CONCLUIU', 'TP_ESCOLA', 'TP_ENSINO',\n",
+              "       'IN_TREINEIRO', 'CO_ESCOLA', 'CO_MUNICIPIO_ESC',\n",
+              "       'NO_MUNICIPIO_ESC', 'CO_UF_ESC', 'SG_UF_ESC',\n",
+              "       'TP_DEPENDENCIA_ADM_ESC', 'TP_LOCALIZACAO_ESC', 'TP_SIT_FUNC_ESC',\n",
+              "       'IN_BAIXA_VISAO', 'IN_CEGUEIRA', 'IN_SURDEZ',\n",
+              "       'IN_DEFICIENCIA_AUDITIVA', 'IN_SURDO_CEGUEIRA',\n",
+              "       'IN_DEFICIENCIA_FISICA', 'IN_DEFICIENCIA_MENTAL',\n",
+              "       'IN_DEFICIT_ATENCAO', 'IN_DISLEXIA', 'IN_DISCALCULIA',\n",
+              "       'IN_AUTISMO', 'IN_VISAO_MONOCULAR', 'IN_OUTRA_DEF', 'IN_GESTANTE',\n",
+              "       'IN_LACTANTE', 'IN_IDOSO', 'IN_ESTUDA_CLASSE_HOSPITALAR',\n",
+              "       'IN_SEM_RECURSO', 'IN_BRAILLE', 'IN_AMPLIADA_24', 'IN_AMPLIADA_18',\n",
+              "       'IN_LEDOR', 'IN_ACESSO', 'IN_TRANSCRICAO', 'IN_LIBRAS',\n",
+              "       'IN_TEMPO_ADICIONAL', 'IN_LEITURA_LABIAL', 'IN_MESA_CADEIRA_RODAS',\n",
+              "       'IN_MESA_CADEIRA_SEPARADA', 'IN_APOIO_PERNA', 'IN_GUIA_INTERPRETE',\n",
+              "       'IN_COMPUTADOR', 'IN_CADEIRA_ESPECIAL', 'IN_CADEIRA_CANHOTO',\n",
+              "       'IN_CADEIRA_ACOLCHOADA', 'IN_PROVA_DEITADO', 'IN_MOBILIARIO_OBESO',\n",
+              "       'IN_LAMINA_OVERLAY', 'IN_PROTETOR_AURICULAR', 'IN_MEDIDOR_GLICOSE',\n",
+              "       'IN_MAQUINA_BRAILE', 'IN_SOROBAN', 'IN_MARCA_PASSO', 'IN_SONDA',\n",
+              "       'IN_MEDICAMENTOS', 'IN_SALA_INDIVIDUAL', 'IN_SALA_ESPECIAL',\n",
+              "       'IN_SALA_ACOMPANHANTE', 'IN_MOBILIARIO_ESPECIFICO',\n",
+              "       'IN_MATERIAL_ESPECIFICO', 'IN_NOME_SOCIAL', 'CO_MUNICIPIO_PROVA',\n",
+              "       'NO_MUNICIPIO_PROVA', 'CO_UF_PROVA', 'SG_UF_PROVA',\n",
+              "       'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n",
+              "       'TP_PRESENCA_MT', 'CO_PROVA_CN', 'CO_PROVA_CH', 'CO_PROVA_LC',\n",
+              "       'CO_PROVA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n",
+              "       'NU_NOTA_MT', 'TX_RESPOSTAS_CN', 'TX_RESPOSTAS_CH',\n",
+              "       'TX_RESPOSTAS_LC', 'TX_RESPOSTAS_MT', 'TP_LINGUA',\n",
+              "       'TX_GABARITO_CN', 'TX_GABARITO_CH', 'TX_GABARITO_LC',\n",
+              "       'TX_GABARITO_MT', 'TP_STATUS_REDACAO', 'NU_NOTA_COMP1',\n",
+              "       'NU_NOTA_COMP2', 'NU_NOTA_COMP3', 'NU_NOTA_COMP4', 'NU_NOTA_COMP5',\n",
+              "       'NU_NOTA_REDACAO', 'Q001', 'Q002', 'Q003', 'Q004', 'Q005', 'Q006',\n",
+              "       'Q007', 'Q008', 'Q009', 'Q010', 'Q011', 'Q012', 'Q013', 'Q014',\n",
+              "       'Q015', 'Q016', 'Q017', 'Q018', 'Q019', 'Q020', 'Q021', 'Q022',\n",
+              "       'Q023', 'Q024', 'Q025'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4RinKz2RKws6",
+        "outputId": "de05bc57-0262-467f-ca4d-daea07809b02",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        }
+      },
+      "source": [
+        "dados[[\"SG_UF_RESIDENCIA\", \"Q025\"]]"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>SG_UF_RESIDENCIA</th>\n",
+              "      <th>Q025</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>PA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>PA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>PA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>PA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>PA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127375</th>\n",
+              "      <td>MG</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127376</th>\n",
+              "      <td>BA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127377</th>\n",
+              "      <td>BA</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127378</th>\n",
+              "      <td>BA</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127379</th>\n",
+              "      <td>MG</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>127380 rows × 2 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       SG_UF_RESIDENCIA Q025\n",
+              "0                    PA    B\n",
+              "1                    PA    B\n",
+              "2                    PA    B\n",
+              "3                    PA    B\n",
+              "4                    PA    B\n",
+              "...                 ...  ...\n",
+              "127375               MG    B\n",
+              "127376               BA    B\n",
+              "127377               BA    B\n",
+              "127378               BA    A\n",
+              "127379               MG    B\n",
+              "\n",
+              "[127380 rows x 2 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9XyBWm9DL3hI",
+        "outputId": "aab654f2-a8e7-483b-e8e4-d9b1a04f4ba1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "source": [
+        "dados[\"SG_UF_RESIDENCIA\"]"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0         PA\n",
+              "1         PA\n",
+              "2         PA\n",
+              "3         PA\n",
+              "4         PA\n",
+              "          ..\n",
+              "127375    MG\n",
+              "127376    BA\n",
+              "127377    BA\n",
+              "127378    BA\n",
+              "127379    MG\n",
+              "Name: SG_UF_RESIDENCIA, Length: 127380, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WEH6_lABMT2T",
+        "outputId": "5525ab1d-b706-4809-c85f-7e9cecac927d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        }
+      },
+      "source": [
+        "dados[\"SG_UF_RESIDENCIA\"].unique()"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['PA', 'RO', 'MT', 'AC', 'AM', 'RR', 'MA', 'PR', 'BA', 'PI', 'CE',\n",
+              "       'PE', 'AP', 'TO', 'SC', 'GO', 'MG', 'SP', 'SE', 'RJ', 'PB', 'AL',\n",
+              "       'RN', 'ES', 'DF', 'RS', 'MS'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kDXEe1rxMY0D",
+        "outputId": "46cf97d2-7452-4706-b2fe-7e52785a1ecc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "len(dados[\"SG_UF_RESIDENCIA\"].unique())"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "27"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yyQaMKoyMeaK",
+        "outputId": "53281969-f53f-49d0-c1fb-352cd9bbb463",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 493
+        }
+      },
+      "source": [
+        "dados[\"SG_UF_RESIDENCIA\"].value_counts()"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SP    20264\n",
+              "MG    13547\n",
+              "BA    10040\n",
+              "RJ     8467\n",
+              "CE     7381\n",
+              "PA     7170\n",
+              "PE     6941\n",
+              "MA     5543\n",
+              "RS     5466\n",
+              "PR     5259\n",
+              "GO     4268\n",
+              "PB     3690\n",
+              "PI     3034\n",
+              "RN     2959\n",
+              "AM     2820\n",
+              "SC     2673\n",
+              "ES     2550\n",
+              "DF     2408\n",
+              "AL     2263\n",
+              "MT     2230\n",
+              "SE     1815\n",
+              "MS     1655\n",
+              "RO     1400\n",
+              "TO     1178\n",
+              "AP     1047\n",
+              "AC      946\n",
+              "RR      366\n",
+              "Name: SG_UF_RESIDENCIA, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ML7wBPY1MrZL",
+        "outputId": "b922ca16-44ef-4153-f52b-be12c192bc8f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "source": [
+        "dados[\"NU_IDADE\"].value_counts()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "18    21452\n",
+              "17    21256\n",
+              "19    14418\n",
+              "20    10413\n",
+              "16     7830\n",
+              "      ...  \n",
+              "75        2\n",
+              "73        2\n",
+              "82        1\n",
+              "77        1\n",
+              "76        1\n",
+              "Name: NU_IDADE, Length: 65, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xj2Lmq2_NNPk",
+        "outputId": "1acd212d-e5d7-4836-ed26-118d49c1bce5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "source": [
+        "dados[\"NU_IDADE\"].value_counts().sort_index()"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "13        4\n",
+              "14      141\n",
+              "15     2009\n",
+              "16     7830\n",
+              "17    21256\n",
+              "      ...  \n",
+              "73        2\n",
+              "75        2\n",
+              "76        1\n",
+              "77        1\n",
+              "82        1\n",
+              "Name: NU_IDADE, Length: 65, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ioQ7i7Q1Oln8",
+        "outputId": "cbbbc022-3f78-428f-ebea-a7a5d6824677",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 285
+        }
+      },
+      "source": [
+        "dados[\"NU_IDADE\"].hist()"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f39150a8c50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TYd8dPfNO1-n",
+        "outputId": "539c541d-bbce-4b13-9596-80c7dd998bf7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        }
+      },
+      "source": [
+        "dados[\"NU_IDADE\"].hist(bins = 20, figsize = (10,8))"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f391505feb8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Al4Yeg7wSFN6"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VrQVzO4vNyTP",
+        "outputId": "fd76837e-9687-4928-c42a-1abb0c705fdf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 663
+        }
+      },
+      "source": [
+        "dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"].value_counts().sort_index()"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "14      64\n",
+              "15    1927\n",
+              "16    7142\n",
+              "17    4901\n",
+              "18     858\n",
+              "19     228\n",
+              "20      83\n",
+              "21      44\n",
+              "22      30\n",
+              "23      21\n",
+              "24      13\n",
+              "25       8\n",
+              "26      13\n",
+              "27       9\n",
+              "28       6\n",
+              "29      10\n",
+              "30       4\n",
+              "31       4\n",
+              "32       2\n",
+              "33       1\n",
+              "34       3\n",
+              "35       2\n",
+              "36       2\n",
+              "37       1\n",
+              "38       4\n",
+              "39       2\n",
+              "40       4\n",
+              "41       1\n",
+              "42       4\n",
+              "44       2\n",
+              "45       1\n",
+              "46       2\n",
+              "47       1\n",
+              "48       1\n",
+              "51       1\n",
+              "55       1\n",
+              "62       1\n",
+              "Name: NU_IDADE, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NCPdzdkeNioz",
+        "outputId": "f7873c01-6170-41c1-92a3-2411747c2cbb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        }
+      },
+      "source": [
+        "dados[\"NU_NOTA_REDACAO\"].hist(bins = 20, figsize=(8, 6))"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3914b27f28>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2tJt5q4dSolR",
+        "outputId": "2ea1e405-cfdc-42b4-fa16-2aecafa3a155",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        }
+      },
+      "source": [
+        "dados[\"NU_NOTA_LC\"].hist(bins = 20, figsize=(8, 6))"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f39137f6c50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BDLcSca0S8nd",
+        "outputId": "8cdf2ab4-3625-4caa-958d-ad5ed2c87aa8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "dados[\"NU_NOTA_REDACAO\"].mean()"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "571.5700253970197"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Vz4VGeogTHRw",
+        "outputId": "f83cdd58-6d34-452e-a5cb-c126a6f16f3a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "dados[\"NU_NOTA_REDACAO\"].std()"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "188.07645455749753"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YeKjZt4oTS5o",
+        "outputId": "7d1ee60b-5efc-49ad-975c-9d18c7bc821b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
+        "\n",
+        "dados[provas].describe()"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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_CN</th>\n",
+              "      <th>NU_NOTA_CH</th>\n",
+              "      <th>NU_NOTA_MT</th>\n",
+              "      <th>NU_NOTA_LC</th>\n",
+              "      <th>NU_NOTA_REDACAO</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>92758.000000</td>\n",
+              "      <td>98043.000000</td>\n",
+              "      <td>92758.000000</td>\n",
+              "      <td>98043.000000</td>\n",
+              "      <td>98043.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>477.964015</td>\n",
+              "      <td>507.365912</td>\n",
+              "      <td>523.555206</td>\n",
+              "      <td>520.463928</td>\n",
+              "      <td>571.570025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>76.296099</td>\n",
+              "      <td>82.972839</td>\n",
+              "      <td>109.416939</td>\n",
+              "      <td>64.556578</td>\n",
+              "      <td>188.076455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>418.000000</td>\n",
+              "      <td>447.900000</td>\n",
+              "      <td>435.300000</td>\n",
+              "      <td>483.700000</td>\n",
+              "      <td>480.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>470.200000</td>\n",
+              "      <td>510.900000</td>\n",
+              "      <td>501.600000</td>\n",
+              "      <td>526.100000</td>\n",
+              "      <td>580.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>533.300000</td>\n",
+              "      <td>567.100000</td>\n",
+              "      <td>598.200000</td>\n",
+              "      <td>565.100000</td>\n",
+              "      <td>680.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>797.300000</td>\n",
+              "      <td>809.400000</td>\n",
+              "      <td>985.000000</td>\n",
+              "      <td>801.700000</td>\n",
+              "      <td>1000.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         NU_NOTA_CN    NU_NOTA_CH    NU_NOTA_MT    NU_NOTA_LC  NU_NOTA_REDACAO\n",
+              "count  92758.000000  98043.000000  92758.000000  98043.000000     98043.000000\n",
+              "mean     477.964015    507.365912    523.555206    520.463928       571.570025\n",
+              "std       76.296099     82.972839    109.416939     64.556578       188.076455\n",
+              "min        0.000000      0.000000      0.000000      0.000000         0.000000\n",
+              "25%      418.000000    447.900000    435.300000    483.700000       480.000000\n",
+              "50%      470.200000    510.900000    501.600000    526.100000       580.000000\n",
+              "75%      533.300000    567.100000    598.200000    565.100000       680.000000\n",
+              "max      797.300000    809.400000    985.000000    801.700000      1000.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FMaMoSUyUD_8",
+        "outputId": "d4d74a79-fa6f-41f5-d9b2-635792e0f59d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "dados[\"NU_NOTA_LC\"].quantile(0.1)"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "434.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "03RSXG1vVQOi",
+        "outputId": "9e52973a-376b-4253-c59b-ced202a7cefa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 392
+        }
+      },
+      "source": [
+        "dados[\"NU_NOTA_LC\"].plot.box(grid = True, figsize=(8,6))"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3913717dd8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "y_62npBVWvW5",
+        "outputId": "8109f5e3-587d-4f45-fabe-2040e3ebd2b7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 501
+        }
+      },
+      "source": [
+        "dados[provas].boxplot(grid=True, figsize= (10,8))"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f39136a5668>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2iyYYnhjNzRr"
+      },
+      "source": [
+        "Desafio01: Proporção dos inscritos por idade.\n",
+        "\n",
+        "Desafio02: Descobrir de quais estados são os inscritos com 13 anos.\n",
+        "\n",
+        "Desafio03: Adicionar título no gráfico\n",
+        "\n",
+        "Desafio04: Plotar os Histogramas das idades dos do treineiro e não treineiros.\n",
+        "\n",
+        "Desafio05: Comparar as distribuições das provas em inglês espanhol\n",
+        "\n",
+        "Desafio06: Explorar a documentações e visualizações com matplotlib ou pandas e gerar novas visualizações."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BZlUU335HJZS"
+      },
+      "source": [
+        "## Aula 02"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nPG7t_YtHI5a",
+        "outputId": "fe0d5b6f-ad95-4a89-86b3-bde5b49dcd43",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 222
+        }
+      },
+      "source": [
+        "dados.query(\"NU_IDADE == 13\")"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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_INSCRICAO</th>\n",
+              "      <th>NU_ANO</th>\n",
+              "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>CO_UF_RESIDENCIA</th>\n",
+              "      <th>SG_UF_RESIDENCIA</th>\n",
+              "      <th>NU_IDADE</th>\n",
+              "      <th>TP_SEXO</th>\n",
+              "      <th>TP_ESTADO_CIVIL</th>\n",
+              "      <th>TP_COR_RACA</th>\n",
+              "      <th>TP_NACIONALIDADE</th>\n",
+              "      <th>CO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>CO_UF_NASCIMENTO</th>\n",
+              "      <th>SG_UF_NASCIMENTO</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>CO_ESCOLA</th>\n",
+              "      <th>CO_MUNICIPIO_ESC</th>\n",
+              "      <th>NO_MUNICIPIO_ESC</th>\n",
+              "      <th>CO_UF_ESC</th>\n",
+              "      <th>SG_UF_ESC</th>\n",
+              "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
+              "      <th>TP_LOCALIZACAO_ESC</th>\n",
+              "      <th>TP_SIT_FUNC_ESC</th>\n",
+              "      <th>IN_BAIXA_VISAO</th>\n",
+              "      <th>IN_CEGUEIRA</th>\n",
+              "      <th>IN_SURDEZ</th>\n",
+              "      <th>IN_DEFICIENCIA_AUDITIVA</th>\n",
+              "      <th>IN_SURDO_CEGUEIRA</th>\n",
+              "      <th>IN_DEFICIENCIA_FISICA</th>\n",
+              "      <th>IN_DEFICIENCIA_MENTAL</th>\n",
+              "      <th>IN_DEFICIT_ATENCAO</th>\n",
+              "      <th>IN_DISLEXIA</th>\n",
+              "      <th>IN_DISCALCULIA</th>\n",
+              "      <th>IN_AUTISMO</th>\n",
+              "      <th>IN_VISAO_MONOCULAR</th>\n",
+              "      <th>...</th>\n",
+              "      <th>TX_RESPOSTAS_CH</th>\n",
+              "      <th>TX_RESPOSTAS_LC</th>\n",
+              "      <th>TX_RESPOSTAS_MT</th>\n",
+              "      <th>TP_LINGUA</th>\n",
+              "      <th>TX_GABARITO_CN</th>\n",
+              "      <th>TX_GABARITO_CH</th>\n",
+              "      <th>TX_GABARITO_LC</th>\n",
+              "      <th>TX_GABARITO_MT</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>Q001</th>\n",
+              "      <th>Q002</th>\n",
+              "      <th>Q003</th>\n",
+              "      <th>Q004</th>\n",
+              "      <th>Q005</th>\n",
+              "      <th>Q006</th>\n",
+              "      <th>Q007</th>\n",
+              "      <th>Q008</th>\n",
+              "      <th>Q009</th>\n",
+              "      <th>Q010</th>\n",
+              "      <th>Q011</th>\n",
+              "      <th>Q012</th>\n",
+              "      <th>Q013</th>\n",
+              "      <th>Q014</th>\n",
+              "      <th>Q015</th>\n",
+              "      <th>Q016</th>\n",
+              "      <th>Q017</th>\n",
+              "      <th>Q018</th>\n",
+              "      <th>Q019</th>\n",
+              "      <th>Q020</th>\n",
+              "      <th>Q021</th>\n",
+              "      <th>Q022</th>\n",
+              "      <th>Q023</th>\n",
+              "      <th>Q024</th>\n",
+              "      <th>Q025</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>57359</th>\n",
+              "      <td>190003297248</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>3504107</td>\n",
+              "      <td>Atibaia</td>\n",
+              "      <td>35</td>\n",
+              "      <td>SP</td>\n",
+              "      <td>13</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3556701.0</td>\n",
+              "      <td>Vinhedo</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>SP</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>CAEBCEBECAABCDBEEAEDDBCBACBCECAABDCACEABAEADC</td>\n",
+              "      <td>BAAEC99999BCCDDEDACBAADECADBEADBADDBECADEBCBDA...</td>\n",
+              "      <td>AABDCEACDDBEDABDDACBDECABACDDBAECDBECBBDEBCDA</td>\n",
+              "      <td>0</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>200.0</td>\n",
+              "      <td>200.0</td>\n",
+              "      <td>200.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>920.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>G</td>\n",
+              "      <td>C</td>\n",
+              "      <td>D</td>\n",
+              "      <td>3</td>\n",
+              "      <td>F</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>D</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95630</th>\n",
+              "      <td>190004825557</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>5107602</td>\n",
+              "      <td>Rondonópolis</td>\n",
+              "      <td>51</td>\n",
+              "      <td>MT</td>\n",
+              "      <td>13</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5107602.0</td>\n",
+              "      <td>Rondonópolis</td>\n",
+              "      <td>51.0</td>\n",
+              "      <td>MT</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>E</td>\n",
+              "      <td>E</td>\n",
+              "      <td>C</td>\n",
+              "      <td>D</td>\n",
+              "      <td>3</td>\n",
+              "      <td>F</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98197</th>\n",
+              "      <td>190004926952</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1600303</td>\n",
+              "      <td>Macapá</td>\n",
+              "      <td>16</td>\n",
+              "      <td>AP</td>\n",
+              "      <td>13</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1600303.0</td>\n",
+              "      <td>Macapá</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>AP</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>G</td>\n",
+              "      <td>E</td>\n",
+              "      <td>D</td>\n",
+              "      <td>B</td>\n",
+              "      <td>4</td>\n",
+              "      <td>K</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>D</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>E</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>116501</th>\n",
+              "      <td>190005659236</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>2917508</td>\n",
+              "      <td>Jacobina</td>\n",
+              "      <td>29</td>\n",
+              "      <td>BA</td>\n",
+              "      <td>13</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2304400.0</td>\n",
+              "      <td>Fortaleza</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>CE</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>ADCCCCDCEAAABCEEEDAD*CCCDEBADCDBBBCBEBDBDBECC</td>\n",
+              "      <td>EBBAD99999BDCBCCBCEBBCADEDDEDECDECDECECACDCECC...</td>\n",
+              "      <td>ECEADDBBABCCCDBDCAAECABDCBDBCECCECABCABBBCBAA</td>\n",
+              "      <td>0</td>\n",
+              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>500.0</td>\n",
+              "      <td>D</td>\n",
+              "      <td>F</td>\n",
+              "      <td>D</td>\n",
+              "      <td>B</td>\n",
+              "      <td>4</td>\n",
+              "      <td>D</td>\n",
+              "      <td>A</td>\n",
+              "      <td>E</td>\n",
+              "      <td>E</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>D</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>4 rows × 136 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q023  Q024 Q025\n",
+              "57359   190003297248    2019                  3504107  ...    B     B    B\n",
+              "95630   190004825557    2019                  5107602  ...    A     A    B\n",
+              "98197   190004926952    2019                  1600303  ...    A     B    B\n",
+              "116501  190005659236    2019                  2917508  ...    B     C    B\n",
+              "\n",
+              "[4 rows x 136 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "b8Z64spVCxEd",
+        "outputId": "3100cf93-f7c0-4071-9e16-7de95d8e710a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 476
+        }
+      },
+      "source": [
+        "dados.query(\"NU_IDADE <= 14\")[\"SG_UF_RESIDENCIA\"].value_counts()"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PE    18\n",
+              "CE    15\n",
+              "BA    14\n",
+              "PA    12\n",
+              "SP    11\n",
+              "MG    11\n",
+              "MA     9\n",
+              "PB     7\n",
+              "PI     7\n",
+              "GO     7\n",
+              "MT     4\n",
+              "AM     4\n",
+              "DF     4\n",
+              "TO     3\n",
+              "PR     3\n",
+              "SE     3\n",
+              "AL     2\n",
+              "RO     2\n",
+              "SC     2\n",
+              "AP     1\n",
+              "RN     1\n",
+              "RS     1\n",
+              "ES     1\n",
+              "MS     1\n",
+              "RJ     1\n",
+              "AC     1\n",
+              "Name: SG_UF_RESIDENCIA, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sqS5PRDaIXVb",
+        "outputId": "c8b1e635-eea3-4978-d2c1-528d8ab48078",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 476
+        }
+      },
+      "source": [
+        "dados.query(\"NU_IDADE <= 14\")[\"SG_UF_RESIDENCIA\"].value_counts(normalize=True)"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PE    0.124138\n",
+              "CE    0.103448\n",
+              "BA    0.096552\n",
+              "PA    0.082759\n",
+              "SP    0.075862\n",
+              "MG    0.075862\n",
+              "MA    0.062069\n",
+              "PB    0.048276\n",
+              "PI    0.048276\n",
+              "GO    0.048276\n",
+              "MT    0.027586\n",
+              "AM    0.027586\n",
+              "DF    0.027586\n",
+              "TO    0.020690\n",
+              "PR    0.020690\n",
+              "SE    0.020690\n",
+              "AL    0.013793\n",
+              "RO    0.013793\n",
+              "SC    0.013793\n",
+              "AP    0.006897\n",
+              "RN    0.006897\n",
+              "RS    0.006897\n",
+              "ES    0.006897\n",
+              "MS    0.006897\n",
+              "RJ    0.006897\n",
+              "AC    0.006897\n",
+              "Name: SG_UF_RESIDENCIA, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TLl6Z1IeJJs8",
+        "outputId": "2ef23854-5731-4e35-e4e6-9b88c07b1cd0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 483
+        }
+      },
+      "source": [
+        "alunos_menor_quartoze = dados.query(\"NU_IDADE <= 14\")\n",
+        "alunos_menor_quartoze[\"SG_UF_RESIDENCIA\"].value_counts().plot.pie(figsize=(10,8))"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f39135d9cf8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 30
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OKGKHYz7KPcz",
+        "outputId": "6414a40b-5eda-42f3-c70a-28d8cc42eb2b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 506
+        }
+      },
+      "source": [
+        "alunos_menor_quartoze[\"SG_UF_RESIDENCIA\"].value_counts(normalize = True).plot.bar(figsize=(10,8))"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3913587e80>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8pZMPrlWLqxk",
+        "outputId": "9145c08a-b60a-4903-9ff1-01f4c1b0b457",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "len(alunos_menor_quartoze)"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "145"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dBj4lqiOM-tL",
+        "outputId": "c43d6ee5-af63-45f6-c723-27c01d539f3f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 530
+        }
+      },
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_MT\", data = dados)\n",
+        "plt.title(\"Boxplot das notas de matemática pela renda\")"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot das notas de matemática pela renda')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "d7ec4hqHOMz0"
+      },
+      "source": [
+        "renda_ordenada = dados[\"Q006\"].unique()\n",
+        "renda_ordenada.sort()"
+      ],
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gmKZikIfPPrD",
+        "outputId": "f14c2414-5b93-467a-8539-b13c5f75af18",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "renda_ordenada"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',\n",
+              "       'N', 'O', 'P', 'Q'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Sy5W5bGIPYNv",
+        "outputId": "b53e1782-b842-4b7d-a3fd-1bc085d65971",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 421
+        }
+      },
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_MT\", data = dados, order = renda_ordenada)\n",
+        "plt.title(\"Boxplot das notas de matemática pela renda\")"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot das notas de matemática pela renda')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "K_Qk_WbaQuuB",
+        "outputId": "b4e575ba-a0fb-4c6b-a9bf-7d94e7468c9f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 119
+        }
+      },
+      "source": [
+        "dados[provas].sum()"
+      ],
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "NU_NOTA_CN         44334986.1\n",
+              "NU_NOTA_CH         49743676.1\n",
+              "NU_NOTA_MT         48563933.8\n",
+              "NU_NOTA_LC         51027844.9\n",
+              "NU_NOTA_REDACAO    56038440.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eqS_hIkvRA0O",
+        "outputId": "4888a882-2390-45fe-8ed8-335a0f649f25",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        }
+      },
+      "source": [
+        "\n",
+        "dados[\"NU_NOTA_TOTAL\"] = dados[provas].sum(axis=1)\n",
+        "dados.head()"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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_INSCRICAO</th>\n",
+              "      <th>NU_ANO</th>\n",
+              "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>CO_UF_RESIDENCIA</th>\n",
+              "      <th>SG_UF_RESIDENCIA</th>\n",
+              "      <th>NU_IDADE</th>\n",
+              "      <th>TP_SEXO</th>\n",
+              "      <th>TP_ESTADO_CIVIL</th>\n",
+              "      <th>TP_COR_RACA</th>\n",
+              "      <th>TP_NACIONALIDADE</th>\n",
+              "      <th>CO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>CO_UF_NASCIMENTO</th>\n",
+              "      <th>SG_UF_NASCIMENTO</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>CO_ESCOLA</th>\n",
+              "      <th>CO_MUNICIPIO_ESC</th>\n",
+              "      <th>NO_MUNICIPIO_ESC</th>\n",
+              "      <th>CO_UF_ESC</th>\n",
+              "      <th>SG_UF_ESC</th>\n",
+              "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
+              "      <th>TP_LOCALIZACAO_ESC</th>\n",
+              "      <th>TP_SIT_FUNC_ESC</th>\n",
+              "      <th>IN_BAIXA_VISAO</th>\n",
+              "      <th>IN_CEGUEIRA</th>\n",
+              "      <th>IN_SURDEZ</th>\n",
+              "      <th>IN_DEFICIENCIA_AUDITIVA</th>\n",
+              "      <th>IN_SURDO_CEGUEIRA</th>\n",
+              "      <th>IN_DEFICIENCIA_FISICA</th>\n",
+              "      <th>IN_DEFICIENCIA_MENTAL</th>\n",
+              "      <th>IN_DEFICIT_ATENCAO</th>\n",
+              "      <th>IN_DISLEXIA</th>\n",
+              "      <th>IN_DISCALCULIA</th>\n",
+              "      <th>IN_AUTISMO</th>\n",
+              "      <th>IN_VISAO_MONOCULAR</th>\n",
+              "      <th>...</th>\n",
+              "      <th>TX_RESPOSTAS_LC</th>\n",
+              "      <th>TX_RESPOSTAS_MT</th>\n",
+              "      <th>TP_LINGUA</th>\n",
+              "      <th>TX_GABARITO_CN</th>\n",
+              "      <th>TX_GABARITO_CH</th>\n",
+              "      <th>TX_GABARITO_LC</th>\n",
+              "      <th>TX_GABARITO_MT</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>Q001</th>\n",
+              "      <th>Q002</th>\n",
+              "      <th>Q003</th>\n",
+              "      <th>Q004</th>\n",
+              "      <th>Q005</th>\n",
+              "      <th>Q006</th>\n",
+              "      <th>Q007</th>\n",
+              "      <th>Q008</th>\n",
+              "      <th>Q009</th>\n",
+              "      <th>Q010</th>\n",
+              "      <th>Q011</th>\n",
+              "      <th>Q012</th>\n",
+              "      <th>Q013</th>\n",
+              "      <th>Q014</th>\n",
+              "      <th>Q015</th>\n",
+              "      <th>Q016</th>\n",
+              "      <th>Q017</th>\n",
+              "      <th>Q018</th>\n",
+              "      <th>Q019</th>\n",
+              "      <th>Q020</th>\n",
+              "      <th>Q021</th>\n",
+              "      <th>Q022</th>\n",
+              "      <th>Q023</th>\n",
+              "      <th>Q024</th>\n",
+              "      <th>Q025</th>\n",
+              "      <th>NU_NOTA_TOTAL</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>190001004661</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1506138</td>\n",
+              "      <td>Redenção</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>17</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1506138.0</td>\n",
+              "      <td>Redenção</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999CCCABBCAADDBCEBCCADBEEBDECBAABDEACACAEABB...</td>\n",
+              "      <td>DEEDCAECDDEEECBCBECABEBAECBBCDAECAEBBBBBDCCDB</td>\n",
+              "      <td>1</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>420.0</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>4</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2288.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>190001004674</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1504208</td>\n",
+              "      <td>Marabá</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>23</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1504208.0</td>\n",
+              "      <td>Marabá</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999ABCEBCEDDECEEEBBCCEECCABDABEEBCDEAEBBEAEA...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
+              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>400.0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>4</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>1184.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>190001004722</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1501402</td>\n",
+              "      <td>Belém</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>35</td>\n",
+              "      <td>F</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1501402.0</td>\n",
+              "      <td>Belém</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999ECBCECEBBABECCCECECCEECCDECEEEDADDDDECEDD...</td>\n",
+              "      <td>DDBEADEBBCADCEEACABECBCEBADECADCEDAACBDABABCE</td>\n",
+              "      <td>1</td>\n",
+              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
+              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
+              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
+              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>560.0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>5</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2350.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>190001004735</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1507300</td>\n",
+              "      <td>São Félix do Xingu</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>23</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1505437.0</td>\n",
+              "      <td>Ourilândia do Norte</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999BABCDABBCBDDAAECADBDDADDCBCEEDCAEBCEAEABD...</td>\n",
+              "      <td>CBDEBBCBDEBCABCDBDEDADCCBEDBCAECEBEBDEBAEECBC</td>\n",
+              "      <td>1</td>\n",
+              "      <td>BEEAAEBEEBADEADDADAEABCEDDDBCBCBCCACBCDADCCEB</td>\n",
+              "      <td>EEBCEEDBADBBCBABCCADCEBACDBBACCACACBEADBBADCB</td>\n",
+              "      <td>ADBBEDCABAABBCBCDAAECDDDBAAAECADECDCEBDEEAECBD...</td>\n",
+              "      <td>BEDEEEAADBEBACABCDBABECECACADCBDCCEDCDABECDDD</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>500.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2555.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>190001004776</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1500800</td>\n",
+              "      <td>Ananindeua</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>16</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1500800.0</td>\n",
+              "      <td>Ananindeua</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
+              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
+              "      <td>0</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>780.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>E</td>\n",
+              "      <td>B</td>\n",
+              "      <td>D</td>\n",
+              "      <td>3</td>\n",
+              "      <td>E</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>D</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2803.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 137 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q024  Q025 NU_NOTA_TOTAL\n",
+              "0  190001004661    2019                  1506138  ...    B     B        2288.9\n",
+              "1  190001004674    2019                  1504208  ...    A     B        1184.6\n",
+              "2  190001004722    2019                  1501402  ...    A     B        2350.5\n",
+              "3  190001004735    2019                  1507300  ...    A     B        2555.7\n",
+              "4  190001004776    2019                  1500800  ...    A     B        2803.5\n",
+              "\n",
+              "[5 rows x 137 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PbqC-d1mRcdQ",
+        "outputId": "a9f5da79-52a8-42d8-ae69-4ec0027ba446",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 421
+        }
+      },
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_TOTAL\", data = dados, order = renda_ordenada)\n",
+        "plt.title(\"Boxplot das notas de total pela renda\")"
+      ],
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot das notas de total pela renda')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 39
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "M1dZw-EUSf03",
+        "outputId": "9eaab3fd-f77d-4e07-c6b6-b7664e4f75b6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 386
+        }
+      },
+      "source": [
+        "sns.displot(dados, x =\"NU_NOTA_TOTAL\")"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.FacetGrid at 0x7f39134ab940>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 360x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HX2i1uknS2uT",
+        "outputId": "2d49d509-3575-4426-95c6-cc8f7c10dc81",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        }
+      },
+      "source": [
+        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
+        "provas.append(\"NU_NOTA_TOTAL\")\n",
+        "dados[provas].query(\"NU_NOTA_TOTAL == 0\")"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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_CN</th>\n",
+              "      <th>NU_NOTA_CH</th>\n",
+              "      <th>NU_NOTA_MT</th>\n",
+              "      <th>NU_NOTA_LC</th>\n",
+              "      <th>NU_NOTA_REDACAO</th>\n",
+              "      <th>NU_NOTA_TOTAL</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127351</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127365</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127366</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127368</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>127374</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>29162 rows × 6 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        NU_NOTA_CN  NU_NOTA_CH  ...  NU_NOTA_REDACAO  NU_NOTA_TOTAL\n",
+              "10             NaN         NaN  ...              NaN            0.0\n",
+              "11             NaN         NaN  ...              NaN            0.0\n",
+              "26             NaN         NaN  ...              NaN            0.0\n",
+              "38             NaN         NaN  ...              NaN            0.0\n",
+              "40             NaN         NaN  ...              NaN            0.0\n",
+              "...            ...         ...  ...              ...            ...\n",
+              "127351         NaN         NaN  ...              NaN            0.0\n",
+              "127365         NaN         NaN  ...              NaN            0.0\n",
+              "127366         NaN         NaN  ...              NaN            0.0\n",
+              "127368         NaN         NaN  ...              NaN            0.0\n",
+              "127374         NaN         NaN  ...              NaN            0.0\n",
+              "\n",
+              "[29162 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GKw2G33sUiHQ",
+        "outputId": "53769b51-981e-4b00-b9ea-fd33c4668edc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        }
+      },
+      "source": [
+        "dados_sem_notas_zero = dados.query(\"NU_NOTA_TOTAL != 0\")\n",
+        "dados_sem_notas_zero.head()"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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_INSCRICAO</th>\n",
+              "      <th>NU_ANO</th>\n",
+              "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
+              "      <th>CO_UF_RESIDENCIA</th>\n",
+              "      <th>SG_UF_RESIDENCIA</th>\n",
+              "      <th>NU_IDADE</th>\n",
+              "      <th>TP_SEXO</th>\n",
+              "      <th>TP_ESTADO_CIVIL</th>\n",
+              "      <th>TP_COR_RACA</th>\n",
+              "      <th>TP_NACIONALIDADE</th>\n",
+              "      <th>CO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
+              "      <th>CO_UF_NASCIMENTO</th>\n",
+              "      <th>SG_UF_NASCIMENTO</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>CO_ESCOLA</th>\n",
+              "      <th>CO_MUNICIPIO_ESC</th>\n",
+              "      <th>NO_MUNICIPIO_ESC</th>\n",
+              "      <th>CO_UF_ESC</th>\n",
+              "      <th>SG_UF_ESC</th>\n",
+              "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
+              "      <th>TP_LOCALIZACAO_ESC</th>\n",
+              "      <th>TP_SIT_FUNC_ESC</th>\n",
+              "      <th>IN_BAIXA_VISAO</th>\n",
+              "      <th>IN_CEGUEIRA</th>\n",
+              "      <th>IN_SURDEZ</th>\n",
+              "      <th>IN_DEFICIENCIA_AUDITIVA</th>\n",
+              "      <th>IN_SURDO_CEGUEIRA</th>\n",
+              "      <th>IN_DEFICIENCIA_FISICA</th>\n",
+              "      <th>IN_DEFICIENCIA_MENTAL</th>\n",
+              "      <th>IN_DEFICIT_ATENCAO</th>\n",
+              "      <th>IN_DISLEXIA</th>\n",
+              "      <th>IN_DISCALCULIA</th>\n",
+              "      <th>IN_AUTISMO</th>\n",
+              "      <th>IN_VISAO_MONOCULAR</th>\n",
+              "      <th>...</th>\n",
+              "      <th>TX_RESPOSTAS_LC</th>\n",
+              "      <th>TX_RESPOSTAS_MT</th>\n",
+              "      <th>TP_LINGUA</th>\n",
+              "      <th>TX_GABARITO_CN</th>\n",
+              "      <th>TX_GABARITO_CH</th>\n",
+              "      <th>TX_GABARITO_LC</th>\n",
+              "      <th>TX_GABARITO_MT</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>Q001</th>\n",
+              "      <th>Q002</th>\n",
+              "      <th>Q003</th>\n",
+              "      <th>Q004</th>\n",
+              "      <th>Q005</th>\n",
+              "      <th>Q006</th>\n",
+              "      <th>Q007</th>\n",
+              "      <th>Q008</th>\n",
+              "      <th>Q009</th>\n",
+              "      <th>Q010</th>\n",
+              "      <th>Q011</th>\n",
+              "      <th>Q012</th>\n",
+              "      <th>Q013</th>\n",
+              "      <th>Q014</th>\n",
+              "      <th>Q015</th>\n",
+              "      <th>Q016</th>\n",
+              "      <th>Q017</th>\n",
+              "      <th>Q018</th>\n",
+              "      <th>Q019</th>\n",
+              "      <th>Q020</th>\n",
+              "      <th>Q021</th>\n",
+              "      <th>Q022</th>\n",
+              "      <th>Q023</th>\n",
+              "      <th>Q024</th>\n",
+              "      <th>Q025</th>\n",
+              "      <th>NU_NOTA_TOTAL</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>190001004661</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1506138</td>\n",
+              "      <td>Redenção</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>17</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1506138.0</td>\n",
+              "      <td>Redenção</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999CCCABBCAADDBCEBCCADBEEBDECBAABDEACACAEABB...</td>\n",
+              "      <td>DEEDCAECDDEEECBCBECABEBAECBBCDAECAEBBBBBDCCDB</td>\n",
+              "      <td>1</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>420.0</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>4</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2288.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>190001004674</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1504208</td>\n",
+              "      <td>Marabá</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>23</td>\n",
+              "      <td>M</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1504208.0</td>\n",
+              "      <td>Marabá</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999ABCEBCEDDECEEEBBCCEECCABDABEEBCDEAEBBEAEA...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
+              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>400.0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>4</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>1184.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>190001004722</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1501402</td>\n",
+              "      <td>Belém</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>35</td>\n",
+              "      <td>F</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1501402.0</td>\n",
+              "      <td>Belém</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999ECBCECEBBABECCCECECCEECCDECEEEDADDDDECEDD...</td>\n",
+              "      <td>DDBEADEBBCADCEEACABECBCEBADECADCEDAACBDABABCE</td>\n",
+              "      <td>1</td>\n",
+              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
+              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
+              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
+              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>560.0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>5</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2350.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>190001004735</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1507300</td>\n",
+              "      <td>São Félix do Xingu</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>23</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1505437.0</td>\n",
+              "      <td>Ourilândia do Norte</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>99999BABCDABBCBDDAAECADBDDADDCBCEEDCAEBCEAEABD...</td>\n",
+              "      <td>CBDEBBCBDEBCABCDBDEDADCCBEDBCAECEBEBDEBAEECBC</td>\n",
+              "      <td>1</td>\n",
+              "      <td>BEEAAEBEEBADEADDADAEABCEDDDBCBCBCCACBCDADCCEB</td>\n",
+              "      <td>EEBCEEDBADBBCBABCCADCEBACDBBACCACACBEADBBADCB</td>\n",
+              "      <td>ADBBEDCABAABBCBCDAAECDDDBAAAECADECDCEBDEEAECBD...</td>\n",
+              "      <td>BEDEEEAADBEBACABCDBABECECACADCBDCCEDCDABECDDD</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>500.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2555.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>190001004776</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>1500800</td>\n",
+              "      <td>Ananindeua</td>\n",
+              "      <td>15</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>16</td>\n",
+              "      <td>F</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1500800.0</td>\n",
+              "      <td>Ananindeua</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>PA</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
+              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
+              "      <td>0</td>\n",
+              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
+              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
+              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
+              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>780.0</td>\n",
+              "      <td>E</td>\n",
+              "      <td>E</td>\n",
+              "      <td>B</td>\n",
+              "      <td>D</td>\n",
+              "      <td>3</td>\n",
+              "      <td>E</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>C</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>D</td>\n",
+              "      <td>A</td>\n",
+              "      <td>A</td>\n",
+              "      <td>B</td>\n",
+              "      <td>2803.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 137 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q024  Q025 NU_NOTA_TOTAL\n",
+              "0  190001004661    2019                  1506138  ...    B     B        2288.9\n",
+              "1  190001004674    2019                  1504208  ...    A     B        1184.6\n",
+              "2  190001004722    2019                  1501402  ...    A     B        2350.5\n",
+              "3  190001004735    2019                  1507300  ...    A     B        2555.7\n",
+              "4  190001004776    2019                  1500800  ...    A     B        2803.5\n",
+              "\n",
+              "[5 rows x 137 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fUEzdhqiXBpv",
+        "outputId": "df897583-6593-4fb6-de01-56c61840b502",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 421
+        }
+      },
+      "source": [
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_TOTAL\", data = dados_sem_notas_zero, order = renda_ordenada)\n",
+        "plt.title(\"Boxplot das notas de total pela renda\")"
+      ],
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot das notas de total pela renda')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 43
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OeFkN4ttXiOH",
+        "outputId": "563b27ec-e88f-4caf-9c3a-f6b7c52dba98",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 530
+        }
+      },
+      "source": [
+        "plt.figure(figsize=(14, 8))\n",
+        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_TOTAL\", data = dados_sem_notas_zero, \n",
+        "            hue = \"IN_TREINEIRO\", order = renda_ordenada)\n",
+        "plt.title(\"Boxplot das notas de total pela renda\")"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot das notas de total pela renda')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1008x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FH5KDYQDM_Wi"
+      },
+      "source": [
+        "MEGA DESAFIO DA THAINÁ: Pegar a amostra completa dos alunos de 13 e 14 anos\n",
+        "\n",
+        "Desafio do Gui bonzinho: aumentar a amostra para alunos menor de idade e compara a proporção por estado.\n",
+        "\n",
+        "Desafio 3: Criar uma função para plotar o boxplot do seaborn\n",
+        "\n",
+        "Desafio 4: Verificar se quem zerou a prova foi eliminado ou não estava presente\n",
+        "\n",
+        "Desafio 5: Quem é eliminado tira zero ou será NaN (não teve registro de notas)\n",
+        "\n",
+        "DEsafio 6: Verificar a proporção dos participantes de rendas mais altas e mais baixas como treineiro e não treineiro.\n",
+        "\n",
+        "Desafio 7: Fazer o mesmo boxplot olhando para a questão 25 (tem internet ou não) e fazer uma reflexão sobre o assunto e o contexto de pandemia."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "m7O1U2rA1XTe",
+        "outputId": "f47a12c3-8c28-4caf-ddfc-e8ef1496a4c9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 522
+        }
+      },
+      "source": [
+        "# Desafio do Gui\n",
+        "menores = dados.query('NU_IDADE < 18')\n",
+        "menores['SG_UF_RESIDENCIA'].value_counts().plot.bar(figsize=(10,8))\n",
+        "plt.title('CANDIDATOS MENORES DE IDADE POR ESTADO')"
+      ],
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'CANDIDATOS MENORES DE IDADE POR ESTADO')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 79
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "K13NHuNowwQl",
+        "outputId": "a7b3ade9-15ce-4d7e-9b7b-391a9cbaebf4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 404
+        }
+      },
+      "source": [
+        "# Desafio03\n",
+        "# Função para plotar o boxplot do seaborn\n",
+        "\n",
+        "def enem_boxplot(x, y, data, order=None):\n",
+        "  plt.figure(figsize=(10,6))\n",
+        "  sns.boxplot(x = x, y = y, data = data, order = order)\n",
+        "  plt.title(f'Boxplot de {y} por {x}')\n",
+        "\n",
+        "enem_boxplot(\"Q006\", \"NU_NOTA_TOTAL\", dados_sem_notas_zero, renda_ordenada)"
+      ],
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "f3Yvbwbt2mx6",
+        "outputId": "adf4d086-4283-4d92-9dd5-7e4a9c40b360",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "# Desafio04 e 05\n",
+        "dados_nota_zero = dados.query('NU_NOTA_TOTAL == 0')\n",
+        "# Usa método dropna() para visualizar só linhas com dados completos.\n",
+        "# Mostra que há pessoas que tiraram 0 mas que estavam presentes.\n",
+        "# Mas sem o dropna() haveria vários registros NaN na coluna TP_STATUS_REDACAO.\n",
+        "dados_nota_zero[['TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC', 'TP_PRESENCA_MT', 'TP_STATUS_REDACAO']].dropna().head()"
+      ],
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>TP_PRESENCA_CN</th>\n",
+              "      <th>TP_PRESENCA_CH</th>\n",
+              "      <th>TP_PRESENCA_LC</th>\n",
+              "      <th>TP_PRESENCA_MT</th>\n",
+              "      <th>TP_STATUS_REDACAO</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>7741</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8693</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19386</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20881</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30037</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       TP_PRESENCA_CN  TP_PRESENCA_CH  ...  TP_PRESENCA_MT  TP_STATUS_REDACAO\n",
+              "7741                0               1  ...               0                4.0\n",
+              "8693                1               1  ...               1                6.0\n",
+              "19386               0               1  ...               0                4.0\n",
+              "20881               0               1  ...               0                4.0\n",
+              "30037               0               1  ...               0                4.0\n",
+              "\n",
+              "[5 rows x 5 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 80
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Dyw7DfM1GSmr",
+        "outputId": "cb3109b5-ad8b-4e30-c3c9-b6b870f3cea6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        }
+      },
+      "source": [
+        "# Desafio06\n",
+        "\n",
+        "nao_treineiro_renda = dados.query('IN_TREINEIRO == 0')['Q006'].value_counts(normalize=True).sort_index()\n",
+        "treineiro_renda = dados.query('IN_TREINEIRO == 1')['Q006'].value_counts(normalize=True).sort_index()\n",
+        "nao_treineiro_renda.plot(kind='bar', color='blue', grid=True, alpha=.8, figsize=(10, 8))\n",
+        "treineiro_renda.plot(kind='bar', color='orange', grid=True, alpha=.5, figsize=(10,8))\n",
+        "plt.legend(['Não treineiro', 'Treineiro'])\n",
+        "plt.title('Proporção de treineiro e não treineiro por renda familiar')\n",
+        "plt.ylabel('Frequência %')\n",
+        "plt.xlabel('Renda familiar, em ordem crescente')"
+      ],
+      "execution_count": 100,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 0, 'Renda familiar, em ordem crescente')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 100
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Wy_NompS3vP9",
+        "outputId": "e15a3010-2971-4ba2-98e5-2adc17127d4b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 404
+        }
+      },
+      "source": [
+        "# Desafio07\n",
+        "dados['Q025'] = dados['Q025'].replace({'A' : 'Sem internet', 'B' : 'Tem internet'})\n",
+        "enem_boxplot('Q025', 'NU_NOTA_TOTAL', dados)"
+      ],
+      "execution_count": 109,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file