atividade_pratica_1_aula5.ipynb

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   "source": [
    "# Atividade Prática 1 - Dataset Notas\n",
    "\n",
    "Vamos obter informações sobre o rendimento de alunos de um curso em algumas disciplinas ao longo dos anos.\n",
    "\n",
    "O primeiro arquivo contido no Dataset Notas, historico-alg1_SIGA_ANONIMIZADO.csv, refere-se ao aproveitamento de estudantes na disciplina ALGORITMOS 1 entre os anos de 2011 e 2022.\n",
    "\n",
    "A primeira coluna (\"matricula\") é composta por números inteiros, onde cada número representa um indivíduo. Assim, repetições nessa coluna indicam que o estudante fez mais de uma vez a mesma matéria.\n",
    "\n",
    "Atenção: R-nota indica REPROVAÇÃO POR NOTA e R-freq REPROVAÇÃO POR FALTA. Se houver outro \"status\" para representar reprovação, este dever ser trocado para o rótulo adequado (R-nota ou R-freq). Frequências < 75 causam reprovação por falta; Médias abaixo de 50 causam reprovação por nota.\n",
    "\n",
    "Analise o dataset do referido arquivo para responder as seguintes perguntas:\n",
    "\n",
    "1. Qual é a média de nota dos aprovados (no período total e por ano)?\n",
    "\n",
    "2. Qual é a média de nota dos reprovados por nota (período total e ano)?\n",
    "\n",
    "3. Qual é a frequência dos reprovados por nota (período total e por ano)?\n",
    "\n",
    "4. Qual a porcentagem de evasões (total e anual)?\n",
    "\n",
    "5. Como os anos de pandemia impactaram no rendimento dos estudantes em relação aos anos anteriores, considerando o rendimento dos aprovados, a taxa de cancelamento e as reprovações? Considere como anos de pandemia os anos de 2020 e 2021.\n",
    "\n",
    "6. Compare a volta às aulas híbrida (2022 período 1) com os anos de pandemia e os anos anteriores.\n",
    "\n",
    "7. Compare a volta às aulas presencial (2022 período 2) com a volta híbrida do item anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIGA = pd.read_csv('historico-alg1_SIGA_ANONIMIZADO.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CI055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS I</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>67</td>\n",
       "      <td>96</td>\n",
       "      <td>Aprovado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>C</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Conclusão formatura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>R-freq</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>A</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   matricula  codigo                          disciplina periodo   ano   \n",
       "0          0   CI055  ALGORITMOS E ESTRUTURAS DE DADOS I       1  2016  \\\n",
       "1          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2019   \n",
       "\n",
       "  curriculo  ch  nota  frequencia    status   tipo observacao     natureza   \n",
       "0       Sim  60    67          96  Aprovado  TURMA          C  Obrigatória  \\\n",
       "1       Sim  60     0          73    R-freq  TURMA        NaN  Obrigatória   \n",
       "\n",
       "      situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "0  Conclusão formatura      NaN                21A  \n",
       "1       Registro ativo        A                21A  "
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIGA.head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Qual é a média de nota dos aprovados (no período total e por ano)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aprovado', 'R-freq', 'R-nota', 'Cancelado', 'Matriculado',\n",
       "       'Reprovado'], dtype=object)"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIGA['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CI055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS I</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>67</td>\n",
       "      <td>96</td>\n",
       "      <td>Aprovado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>C</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Conclusão formatura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>CI055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS I</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>72</td>\n",
       "      <td>96</td>\n",
       "      <td>Aprovado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Conclusão formatura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   matricula codigo                          disciplina periodo   ano   \n",
       "0          0  CI055  ALGORITMOS E ESTRUTURAS DE DADOS I       1  2016  \\\n",
       "9          2  CI055  ALGORITMOS E ESTRUTURAS DE DADOS I       1  2015   \n",
       "\n",
       "  curriculo  ch  nota  frequencia    status   tipo observacao     natureza   \n",
       "0       Sim  60    67          96  Aprovado  TURMA          C  Obrigatória  \\\n",
       "9       Sim  60    72          96  Aprovado  TURMA        NaN  Obrigatória   \n",
       "\n",
       "      situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "0  Conclusão formatura      NaN                21A  \n",
       "9  Conclusão formatura      NaN                21A  "
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aprovados = SIGA[SIGA['status'] == 'Aprovado']\n",
    "aprovados.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.017628205128204"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_aprovados_geral = aprovados['nota'].mean()\n",
    "media_aprovados_geral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ano\n",
       "2011    50.000000\n",
       "2013    70.375000\n",
       "2014    72.142857\n",
       "2015    63.678571\n",
       "2016    67.391304\n",
       "2017    74.710145\n",
       "2018    70.869565\n",
       "2019    31.450495\n",
       "2020    72.400000\n",
       "2021    83.882353\n",
       "2022    76.428571\n",
       "Name: nota, dtype: float64"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_aprovados_por_ano = aprovados.groupby('ano')['nota'].mean()\n",
    "media_aprovados_por_ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ano'>"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vV05OjuLj4xUdHa3s7Gx5PJ6W5gQAAO3EcZeP9957T3/5y1/Uv39/v+W5ublavXq1VqxYoQ0bNmjfvn0aN25ci4MCAID24bjKx3fffadrrrlGixYtUteuXX3La2trtXjxYj3++OMaOXKkMjIyVFRUpI0bN2rTpk1BCw0AAJzruMpHTk6OxowZo6ysLL/lZWVlOnjwoN/y9PR0paSkqLS0tGVJAQBAuxAW6AuWLVumbdu26b333jtirLq6WhEREerSpYvf8oSEBFVXVze5Pa/XK6/X65uvq6sLNBIAAHCQgM58VFVV6Y9//KOef/55RUVFBSVAQUGB4uLifFNycnJQtgsAANqmgMpHWVmZ9u/frwEDBigsLExhYWHasGGD5s2bp7CwMCUkJKihoUE1NTV+r/N4PEpMTGxym/n5+aqtrfVNVVVVx/3NAACAti+gt10uvPBCffjhh37LJk2apPT0dN19991KTk5WeHi4SkpKlJ2dLUkqLy9XZWWlMjMzm9xmZGSkIiMjjzM+AABwmoDKR0xMjPr16+e3rHPnzoqPj/ctnzx5svLy8tStWzfFxsbqtttuU2ZmpoYMGRK81AAAwLECvuD0WObOnSu3263s7Gx5vV6NGjVK8+fPD/aXAQAADtXi8vHmm2/6zUdFRamwsFCFhYUt3TQAAGiHeLYLAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALAqLNQBAAC/TOq0V1tt23vnjGm1bQM/x5kPAABgVUDlY8GCBerfv79iY2MVGxurzMxMvf76677x+vp65eTkKD4+XtHR0crOzpbH4wl6aAAA4FwBlY+ePXtqzpw5Kisr09atWzVy5Ehdfvnl+uijjyRJubm5Wr16tVasWKENGzZo3759GjduXKsEBwAAzhTQNR+XXXaZ3/wDDzygBQsWaNOmTerZs6cWL16s4uJijRw5UpJUVFSk3r17a9OmTRoyZEjwUgMAAMc67ms+Dh8+rGXLlunAgQPKzMxUWVmZDh48qKysLN866enpSklJUWlpaVDCAgAA5wv4bpcPP/xQmZmZqq+vV3R0tFatWqU+ffpo+/btioiIUJcuXfzWT0hIUHV1dbPb83q98nq9vvm6urpAIwEAAAcJ+MzH6aefru3bt2vz5s2aMmWKJk6cqJ07dx53gIKCAsXFxfmm5OTk494WAABo+wIuHxERETrllFOUkZGhgoICnXnmmXryySeVmJiohoYG1dTU+K3v8XiUmJjY7Pby8/NVW1vrm6qqqgL+JgAAgHO0+HM+Ghsb5fV6lZGRofDwcJWUlPjGysvLVVlZqczMzGZfHxkZ6bt196cJAAC0XwFd85Gfn6/Ro0crJSVF3377rYqLi/Xmm29q7dq1iouL0+TJk5WXl6du3bopNjZWt912mzIzM7nTBQAA+ARUPvbv369rr71WX375peLi4tS/f3+tXbtWF110kSRp7ty5crvdys7Oltfr1ahRozR//vxWCQ4AAJwpoPKxePHio45HRUWpsLBQhYWFLQoFAADaL57tAgAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMAqygcAALCK8gEAAKyifAAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AAMCqgMpHQUGBBg0apJiYGHXv3l1jx45VeXm53zr19fXKyclRfHy8oqOjlZ2dLY/HE9TQAADAuQIqHxs2bFBOTo42bdqkv/71rzp48KB++9vf6sCBA751cnNztXr1aq1YsUIbNmzQvn37NG7cuKAHBwAAzhQWyMpr1qzxm1+yZIm6d++usrIynXfeeaqtrdXixYtVXFyskSNHSpKKiorUu3dvbdq0SUOGDAlecgAA4EgtuuajtrZWktStWzdJUllZmQ4ePKisrCzfOunp6UpJSVFpaWlLvhQAAGgnAjrz8a8aGxs1depUDRs2TP369ZMkVVdXKyIiQl26dPFbNyEhQdXV1U1ux+v1yuv1+ubr6uqONxIAAHCA4z7zkZOTox07dmjZsmUtClBQUKC4uDjflJyc3KLtAQCAtu24ysett96qV155RW+88YZ69uzpW56YmKiGhgbV1NT4re/xeJSYmNjktvLz81VbW+ubqqqqjicSAABwiIDKhzFGt956q1atWqX169crLS3NbzwjI0Ph4eEqKSnxLSsvL1dlZaUyMzOb3GZkZKRiY2P9JgAA0H4FdM1HTk6OiouL9T//8z+KiYnxXccRFxenjh07Ki4uTpMnT1ZeXp66deum2NhY3XbbbcrMzOROFwAAICnA8rFgwQJJ0ogRI/yWFxUV6brrrpMkzZ07V263W9nZ2fJ6vRo1apTmz58flLAAAMD5AiofxphjrhMVFaXCwkIVFhYedygAANB+8WwXAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFYFXD7eeustXXbZZUpKSpLL5dJLL73kN26M0cyZM9WjRw917NhRWVlZqqioCFZeAADgcAGXjwMHDujMM89UYWFhk+MPP/yw5s2bp4ULF2rz5s3q3LmzRo0apfr6+haHBQAAzhcW6AtGjx6t0aNHNzlmjNETTzyh6dOn6/LLL5ckLV26VAkJCXrppZc0fvz4lqUFAACOF9RrPvbs2aPq6mplZWX5lsXFxWnw4MEqLS0N5pcCAAAOFfCZj6Oprq6WJCUkJPgtT0hI8I39nNfrldfr9c3X1dUFMxIAAGhjQn63S0FBgeLi4nxTcnJyqCMBAIBWFNTykZiYKEnyeDx+yz0ej2/s5/Lz81VbW+ubqqqqghkJAAC0MUEtH2lpaUpMTFRJSYlvWV1dnTZv3qzMzMwmXxMZGanY2Fi/CQAAtF8BX/Px3Xff6ZNPPvHN79mzR9u3b1e3bt2UkpKiqVOn6v7779epp56qtLQ0zZgxQ0lJSRo7dmwwcwMAAIcKuHxs3bpVF1xwgW8+Ly9PkjRx4kQtWbJEd911lw4cOKCbbrpJNTU1Gj58uNasWaOoqKjgpQYAAI4VcPkYMWKEjDHNjrtcLs2ePVuzZ89uUTAAANA+hfxuFwAA8OtC+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABYRfkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVa1WPgoLC5WamqqoqCgNHjxYW7Zsaa0vBQAAHKRVysd///d/Ky8vT7NmzdK2bdt05plnatSoUdq/f39rfDkAAOAgrVI+Hn/8cd14442aNGmS+vTpo4ULF6pTp0565plnWuPLAQAABwkL9gYbGhpUVlam/Px83zK3262srCyVlpYesb7X65XX6/XN19bWSpLq6up+8dds9H7fgsTNCyRDoMj8T2T211qZpdbLTWZ/ZP4nMvtz4s+OX5r5p/WMMcde2QTZF198YSSZjRs3+i2/8847zTnnnHPE+rNmzTKSmJiYmJiYmNrBVFVVdcyuEPQzH4HKz89XXl6eb76xsVF///vfFR8fL5fLFdSvVVdXp+TkZFVVVSk2Njao224tZLaDzHaQ2R4n5iazHa2V2Rijb7/9VklJScdcN+jl4ze/+Y06dOggj8fjt9zj8SgxMfGI9SMjIxUZGem3rEuXLsGO5Sc2NtYxB8lPyGwHme0gsz1OzE1mO1ojc1xc3C9aL+gXnEZERCgjI0MlJSW+ZY2NjSopKVFmZmawvxwAAHCYVnnbJS8vTxMnTtTAgQN1zjnn6IknntCBAwc0adKk1vhyAADAQVqlfFx11VX66quvNHPmTFVXV+uss87SmjVrlJCQ0Bpf7heLjIzUrFmzjnibpy0jsx1ktoPM9jgxN5ntaAuZXcb8kntiAAAAgoNnuwAAAKsoHwAAwCrKBwAAsIryAQAArKJ8AAAAqygfDmCM0eHDh0MdIyBLlizxPSQQAI7l5w8ZRftG+WhDDh06pOnTp+v888/XrFmzJEmPPPKIoqOj1alTJ02cOFENDQ0hTvnL3HTTTdq3b1+oYzRpy5YtfmXulVde0fnnn68TTzxRAwcO1NKlS0OY7perqKhQSUmJPvnkk1BHaVJMTIwmT56sjRs3hjpKwH5e9rds2aJNmzY54pdjZWWlNm/erPfee0/ffPNNqOMc1V//+lddcskl6tq1qzp16qROnTqpa9euuuSSS7Ru3bpQxwvYrl27dNJJJ4U6xhE++OAD3X///Zo/f76+/vprv7G6ujpdf/319kMF5VG2DrR9+3bjdrtDHcPP9OnTTUJCgsnLyzN9+vQxN998s0lOTjbPPfecefbZZ82JJ55oHnrooVDH9NO1a9cmJ5fLZeLi4nzzbYnb7TYej8cYY8zLL79s3G63ufbaa01hYaG54YYbTFhYmFm5cmWIU/p78MEHzbp164wxxvz97383F154oXG5XMblchm3220uvvhi849//CO0IX/G5XKZvn37GpfLZdLT082jjz5q9u/fH+pYR7V3716TkZFhOnToYC6++GJTW1trsrKyfPv6pJNOMuXl5aGO2aTCwkKTkpJi3G633zRs2DCzdevWUMc7wpIlS0xYWJgZP368KSoqMq+99pp57bXXTFFRkZkwYYIJDw83S5cuDXXMgLTF3ytr1641ERERpm/fviYlJcXEx8eb9evX+8arq6tDkvlX+yFjH3zwgc4++2w1NjaGOorPySefrCeffFKXXnqpPvnkE51++ukqLi7WVVddJUlavny57rvvPn344YchTvpPMTExOv/883XFFVf4lhljdMMNN2j27Nk68cQTJUkTJ04MVcQjuN1uVVdXq3v37jr33HM1fPhwFRQU+MYffPBBrV69WqWlpSFM6S85OVkvv/yyzj77bN14440qKyvT4sWL1bt3b5WXl+vmm29W37599fTTT4c6qs9P+/nLL7/U008/reLiYn333Xe69NJLdcMNN+jiiy8O+pOrW+r3v/+9vv76a91xxx36r//6L33xxRcKDw/Xc889J7fbrUmTJqljx45atWpVqKP6efTRRzV37lzl5+crKipKjz/+uCZMmKBBgwapuLhYL774ojZs2KCBAweGOqrPaaedpj/+8Y/Kyclpcnz+/PmaO3euKioqLCdr3r8+gb0pX331lYqLi9vU2+RDhw7VBRdcoAceeEDGGD3yyCO67777tGLFCl188cXyeDxKSkqyn9l63bHkd7/73VGnkSNHtrmGGhUVZSorK/3md+3a5Zv/7LPPTExMTCiiNauiosIMGjTIXHvttebbb7/1LQ8LCzMfffRRCJM1z+Vy+c58dO/e/Yi/Cnfv3m26dOkSimjNioyMNHv37jXGGJOammo2bNjgN75161bTo0ePUERr1r/uZ2OMqa+vN8XFxebCCy80brfb9OzZ08yYMSOECY90wgknmPfff98YY0xNTY1xuVzm7bff9o2XlZWZhISEEKVrXmpqqnnttdd88+Xl5SY+Pt4cPHjQGGPM7bffbi666KJQxWtSZGSk2b17d7Pju3fvNlFRURYTHZvb7TYDBgwwI0aMaHIaOHBgm/u9Ehsbaz755BO/Zc8//7zp3LmzWb16dcjOfLTbaz5Wr16t+vp6xcXFNTlFR0eHOuIR4uLiVFNT45sfMGCAYmJifPNer7fN/aV4yimnaOPGjUpMTNRZZ52ld999N9SRfpGdO3fqb3/7mzp27Njk2a9Dhw6FIFXzevXqpR07dkiSXC6XwsL8H8vUoUMHHThwIBTRmvXzYzUyMlITJkzQunXr9Omnn+q6667TkiVLQhOuGT/9zJB+PKvXoUMHv/+DsbGx+v7770MVr1n79+9X7969ffOnnnqqamtr9dVXX0mSrr/++jZ1Jk+S+vbtq8WLFzc7/swzz6hPnz4WEx3bKaecotzcXL3xxhtNTosWLQp1xCNERkb6/V6RpKuvvlpPP/20rrrqqtCdxbNedyw544wzzNNPP93s+Pvvv9/mGuoFF1xglixZ0uz48uXLTUZGhsVEgSkpKTEpKSkmPz/fhIeHt+kzH2632/c+/ty5c/3GX3jhBdOnT5/QhGvGI488Ynr37m0qKirMY489ZjIzM31/zXz22WdmxIgR5ve//32IU/r7+ZmPpjQ2NlpK88sMGTLETJ8+3RhjzDPPPGMSEhLMtGnTfOOzZ89uk/8HzzrrLPPUU0/55ktKSkynTp18+3f37t1t7qzpG2+8YTp37mzOOOMMk5uba+bMmWPmzJljcnNzTf/+/U10dPQRZ/hC7eqrrzZTp05tdnz79u3G5XJZTHRsF110kXnkkUeaHCsuLjbh4eEh+V3YKk+1bQsyMjK0bds2TZ48ucnxyMhIpaSkWE51dAsXLlR4eHiz4wcPHtRdd91lMVFgRo4cqW3btunGG29U586d1aFDh1BHatKePXv85n9+FqyhoUF33323zUjHdMcdd6iyslJ9+vTRySefrL179+q0005TWFiYDh06pAEDBuiFF14IdUw/s2bNOuYZxrZ2Ju+ee+7R2LFj9fDDD8vtdmvt2rW68cYbtX79erndbr333nsqLi4Odcwj5Ofn6w9/+IPWrVunqKgorVy5Urfffrtv/7755pvq169fiFP6GzFihHbs2KEFCxZo06ZNqq6uliQlJiZq9OjRuvnmm5WamhrakD/z2GOPHfWOpzPPPLNNXUcoSVOmTNFbb73V5NiECRNkjAnJGZt2e8Gp1+vV4cOH1alTp1BHAYJm165deuWVV/TZZ5+psbFRPXr00LBhw5SVldXmfpE71d69e1VWVqaMjAylpqbK4/GosLBQ33//vcaMGaMLLrgg1BGb9Prrr+u5556T1+vVqFGjdOONN/rGfrrlNj4+PlTxAD/ttny0R4cOHdK+ffva3BmboyEzmsN+Bn692u0Fp8dy6NAhVVZWhjpGQD766COlpaWFOkZAyGzHwYMHOZ4tcOLPDant5p4/f76ysrJ05ZVXqqSkxG/s66+/bpMf2EXm4PjVlg8n/uADmrNz506OZwuc+nOjLeaeN2+e7rzzTqWnpysyMlKXXHKJ3+ftHD58WJ9//nkIEx6JzMHTbi84daIBAwYcdfyHH36wlOSXIzOaw37G0fzlL3/RokWLdPXVV0v68cLIsWPH6ocfftDs2bNDnK5pZA6edls+nPiDb+fOnRo/fnyzf6F8+eWX+vjjjy2nOjoy28HxbIcT97PkzNx79uzR0KFDffNDhw7V+vXrlZWVpYMHD2rq1KmhC9cMMgdPuy0fTvzB169fPw0ePFhTpkxpcnz79u1t7kNsyGwHx7MdTtzPkjNz/+Y3v1FVVZXf7bT9+vXT+vXrNXLkyDb5YEoyB0+7LR9O/ME3bNgwlZeXNzseExOj8847z2KiYyOzHRzPdjhxP0vOzD18+HCtXLlS5557rt/yPn36qKSkpE3e0kzm4Gm35cOJP/iefPLJo46ffPLJeuONNyyl+WXIbAfHsx1O3M+SM3NPmzZNZWVlTY717dtX69ev14svvmg51dGROXj4nA8AAGBVuz3z4WRbtmxRaWmp38cNZ2Zm6pxzzglxsuaRGc1hP+NonHh8kLnl2v2Zj7a2w49m//79ys7O1rvvvquUlBQlJCRIkjwejyorKzVs2DC9+OKL6t69e4iT/hOZ7eJ4tsNJ+/lfOSn3/v37NW7cOG3cuNExxweZg8j6o+ws8Xg8Zvjw4cblcplevXqZc845x5xzzjmmV69exuVymeHDhx/ziZu2ZWdnm8zMTLN79+4jxnbv3m2GDh3a5p5cSmY7OJ7tcOJ+NsaZuZ14fJA5eNpt+WirO/xooqOjzbZt25od37p1q4mOjraY6NjIbAfHsx1O3M/GODO3E48PMgdPu73mY+3atXrrrbd0+umnHzF2+umna968eRoxYoT9YEcRGRmpurq6Zse//fZbRUZGWkx0bGS2g+PZDifuZ8mZuZ14fJA5eNrts13a6g4/mquuukoTJ07UqlWr/LLX1dVp1apVmjRpkiZMmBDChEcisx0cz3Y4cT9LzsztxOODzEFk/VyLJbfccovp1auXWblypamtrfUtr62tNStXrjSpqanm1ltvDWHCI9XX15ubb77ZREREGLfbbaKiokxUVJRxu90mIiLCTJkyxdTX14c6ph8y28HxbIcT97MxzsztxOODzMHTbu928Xq9mjp1qp555hkdOnRIERERkqSGhgaFhYVp8uTJmjt3bpv7a0D6sZGWlZX5XbGekZGh2NjYECdrHplbF8ezHU7dz07NLTnr+PgJmVuu3ZaPn7S1HQ60BMezHU7dz07NjV+fdl8+nOaHH35QWVmZunXrpj59+viN1dfXa/ny5br22mtDlK5pZEZz2M84GiceH2QOEutv9Fj0/fffm7ffftt89NFHR4z98MMP5tlnnw1BquaVl5f77st3u93mvPPOM1988YVvvLq62rjd7hAmPBKZ7eF4tsNp+/knTsvtxOODzMHTbu92+fjjj9W7d2+dd955OuOMM3T++ef7PTq4trZWkyZNCmHCI919993q16+f9u/fr/LycsXExGj48OGqrKwMdbRmkdkOjmc7nLifJWfmduLxQeYgsl53LBk7dqwZM2aM+eqrr0xFRYUZM2aMSUtLM59//rkxpm021O7du5u//e1vvvnGxkZz8803m5SUFPPpp5+SOUicmJnj2Q4n7mdjnJnbiccHmYOn3ZaPtrrDjyYmJsbs3LnziOU5OTmmZ8+e5q233iJzEDgxM8ezHU7cz8Y4M7cTjw8yB0+7LR9tdYcfzaBBg8zSpUubHMvJyTFdunQhcxA4MTPHsx1O3M/GODO3E48PMgdPuy0fbXWHH82DDz5oRo8e3ez4lClTjMvlspjo2MhsB8ezHU7cz8Y4M7cTjw8yB0+7vdW2oKBAb7/9tl577bUmx2+55RYtXLhQjY2NlpMBgeN4tsOp+9mpufHr1W7LBwAAaJva7a22AACgbaJ8AAAAqygfAADAKsoHAACwivIBAACsonwAAACrKB8AWmzNmjUaPny4unTpovj4eF166aX69NNPJUl79+6Vy+XSypUrdcEFF6hTp04688wzVVpa6reNF198UX379lVkZKRSU1P12GOPheJbAWAB5QNAix04cEB5eXnaunWrSkpK5Ha79bvf/c7vQ63+8z//U3fccYe2b9+u0047TRMmTNChQ4ckSWVlZbryyis1fvx4ffjhh7rnnns0Y8YMLVmyJETfEYDWxIeMAQi6r7/+WieccII+/PBDRUdHKy0tTU8//bQmT54sSdq5c6f69u2rXbt2KT09Xddcc42++uor/e///q9vG3fddZdeffVVffTRR6H6NgC0Es58AGixiooKTZgwQSeddJJiY2OVmpoqSaqsrPSt079/f9+/e/ToIUnav3+/JGnXrl0aNmyY3zaHDRumiooKHT58uJXTA7AtLNQBADjfZZddpl69emnRokVKSkpSY2Oj+vXrp4aGBt864eHhvn+7XC5J4lkjwK8U5QNAi3zzzTcqLy/XokWLdO6550qS3nnnnYC20bt3b7377rt+y959912ddtpp6tChQ9CyAmgbKB8AWqRr166Kj4/XU089pR49eqiyslLTpk0LaBv/8R//oUGDBum+++7TVVddpdLSUv35z3/W/PnzWyk1gFDimg8ALeJ2u7Vs2TKVlZWpX79+ys3N1SOPPBLQNgYMGKDly5dr2bJl6tevn2bOnKnZs2fruuuua53QAEKKu10AAIBVnPkAAABWUT4AAIBVlA8AAGAV5QMAAFhF+QAAAFZRPgAAgFWUDwAAYBXlAwAAWEX5AAAAVlE+AACAVZQPAABgFeUDAABY9f/ywWoAAukIkwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_aprovados_por_ano.plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Qual é a média de nota dos reprovados por nota (período total e ano)?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Ajuste de registros  dos alunos cujo status está apenas como 'Reprovado', sem discriminação de se se trata de reprovação por nota ('R-nota') ou frequência ('R-freq')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>R-freq</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>A</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>R-nota</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>B</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>R-freq</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>A</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>21</td>\n",
       "      <td>80</td>\n",
       "      <td>R-nota</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>D</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>R-freq</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC2</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   matricula  codigo                          disciplina periodo   ano   \n",
       "1          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2019  \\\n",
       "2          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2019   \n",
       "5          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2021   \n",
       "6          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2021   \n",
       "7          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2022   \n",
       "\n",
       "  curriculo  ch  nota  frequencia  status   tipo observacao     natureza   \n",
       "1       Sim  60     0          73  R-freq  TURMA        NaN  Obrigatória  \\\n",
       "2       Sim  60    11          76  R-nota  TURMA        NaN  Obrigatória   \n",
       "5       Sim  60     3          17  R-freq  TURMA        NaN  Obrigatória   \n",
       "6       Sim  60    21          80  R-nota  TURMA        NaN  Obrigatória   \n",
       "7       Sim  60     0          25  R-freq  TURMA        NaN  Obrigatória   \n",
       "\n",
       "  situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "1   Registro ativo        A                21A  \n",
       "2   Registro ativo        B                21A  \n",
       "5   Registro ativo        A                21A  \n",
       "6   Registro ativo        D                21A  \n",
       "7   Registro ativo     BCC2                21A  "
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotulos_reprovados = ['R-nota', 'Reprovado', 'R-freq']\n",
    "\n",
    "reprovados = SIGA[SIGA['status'].isin(rotulos_reprovados)]\n",
    "reprovados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "R-nota       162\n",
       "R-freq        49\n",
       "Reprovado      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reprovados['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>95</td>\n",
       "      <td>CI055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS I</td>\n",
       "      <td>Anual</td>\n",
       "      <td>2019</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Reprovado</td>\n",
       "      <td>APROVEITAMENTO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     matricula codigo                          disciplina periodo   ano   \n",
       "165         95  CI055  ALGORITMOS E ESTRUTURAS DE DADOS I   Anual  2019  \\\n",
       "\n",
       "    curriculo  ch  nota  frequencia     status            tipo observacao   \n",
       "165       Sim  60     5           0  Reprovado  APROVEITAMENTO        NaN  \\\n",
       "\n",
       "    natureza situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "165        -   Registro ativo      NaN                21A  "
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reprovados[reprovados['status'] == 'Reprovado']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "R-nota    162\n",
       "R-freq     50\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reprovados.loc[reprovados['status'] == 'Reprovado', 'status'] = 'R-freq'\n",
    "reprovados['status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Médias dos reprovados por nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>R-nota</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>B</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>21</td>\n",
       "      <td>80</td>\n",
       "      <td>R-nota</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>D</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>CI055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS I</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Não</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>R-nota</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>D</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    matricula  codigo                          disciplina periodo   ano   \n",
       "2           1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2019  \\\n",
       "6           1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2021   \n",
       "13          5   CI055  ALGORITMOS E ESTRUTURAS DE DADOS I       1  2016   \n",
       "\n",
       "   curriculo  ch  nota  frequencia  status   tipo observacao     natureza   \n",
       "2        Sim  60    11          76  R-nota  TURMA        NaN  Obrigatória  \\\n",
       "6        Sim  60    21          80  R-nota  TURMA        NaN  Obrigatória   \n",
       "13       Não  60     1          83  R-nota  TURMA        NaN            -   \n",
       "\n",
       "   situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "2    Registro ativo        B                21A  \n",
       "6    Registro ativo        D                21A  \n",
       "13   Registro ativo        D                21A  "
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reprovados_por_nota = reprovados[reprovados['status'] == 'R-nota']\n",
    "reprovados_por_nota.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.037037037037038"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_geral_reprovados_por_nota = reprovados_por_nota['nota'].mean()\n",
    "media_geral_reprovados_por_nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ano\n",
       "2012    24.000000\n",
       "2013    30.000000\n",
       "2014    25.000000\n",
       "2015    19.833333\n",
       "2016    15.400000\n",
       "2017    25.904762\n",
       "2018    23.136364\n",
       "2019    23.321429\n",
       "2020    25.875000\n",
       "2021    19.589744\n",
       "2022    19.777778\n",
       "Name: nota, dtype: float64"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_por_ano_reprovados_por_nota = reprovados_por_nota.groupby('ano')['nota'].mean()\n",
    "media_por_ano_reprovados_por_nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ano'>"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_por_ano_reprovados_por_nota.plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Qual é a frequência dos reprovados por nota (período total e por ano)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.85802469135803"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia_geral_reprovados_por_nota = reprovados_por_nota['frequencia'].mean()\n",
    "frequencia_geral_reprovados_por_nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ano\n",
       "2012     98.000000\n",
       "2013    100.000000\n",
       "2014     90.000000\n",
       "2015     94.666667\n",
       "2016     86.200000\n",
       "2017     93.666667\n",
       "2018     93.454545\n",
       "2019     90.892857\n",
       "2020     96.125000\n",
       "2021     94.923077\n",
       "2022     95.592593\n",
       "Name: frequencia, dtype: float64"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia_por_ano_reprovados_por_nota = reprovados_por_nota.groupby('ano')['frequencia'].mean()\n",
    "frequencia_por_ano_reprovados_por_nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ano'>"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequencia_por_ano_reprovados_por_nota.plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Qual a porcentagem de evasões (total e anual)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "situacaoDiscente\n",
       "Registro ativo         610\n",
       "Evasão                 142\n",
       "Conclusão formatura    103\n",
       "Trancamento             54\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIGA['situacaoDiscente'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.62156215621562"
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porcentagem_geral_evasoes = SIGA[SIGA['situacaoDiscente'] == 'Evasão']['situacaoDiscente'].count()/SIGA['situacaoDiscente'].count() * 100\n",
    "porcentagem_geral_evasoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ano\n",
       "2011      1\n",
       "2012      4\n",
       "2013     11\n",
       "2014     13\n",
       "2015     35\n",
       "2016     53\n",
       "2017     92\n",
       "2018     96\n",
       "2019    238\n",
       "2020    106\n",
       "2021    138\n",
       "2022    122\n",
       "Name: situacaoDiscente, dtype: int64"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIGA.groupby('ano')['situacaoDiscente'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ano   situacaoDiscente   \n",
       "2011  Conclusão formatura    100.000000\n",
       "2012  Conclusão formatura     50.000000\n",
       "      Registro ativo          50.000000\n",
       "2013  Evasão                  63.636364\n",
       "      Registro ativo          18.181818\n",
       "      Conclusão formatura     18.181818\n",
       "2014  Conclusão formatura     61.538462\n",
       "      Evasão                  23.076923\n",
       "      Registro ativo          15.384615\n",
       "2015  Conclusão formatura     45.714286\n",
       "      Registro ativo          28.571429\n",
       "      Evasão                  20.000000\n",
       "      Trancamento              5.714286\n",
       "2016  Conclusão formatura     54.716981\n",
       "      Registro ativo          20.754717\n",
       "      Evasão                  15.094340\n",
       "      Trancamento              9.433962\n",
       "2017  Registro ativo          50.000000\n",
       "      Conclusão formatura     25.000000\n",
       "      Evasão                  18.478261\n",
       "      Trancamento              6.521739\n",
       "2018  Registro ativo          65.625000\n",
       "      Evasão                  22.916667\n",
       "      Trancamento              6.250000\n",
       "      Conclusão formatura      5.208333\n",
       "2019  Registro ativo          70.168067\n",
       "      Evasão                  16.386555\n",
       "      Conclusão formatura      7.142857\n",
       "      Trancamento              6.302521\n",
       "2020  Registro ativo          78.301887\n",
       "      Evasão                  17.924528\n",
       "      Trancamento              3.773585\n",
       "2021  Registro ativo          79.710145\n",
       "      Evasão                  12.318841\n",
       "      Trancamento              7.971014\n",
       "2022  Registro ativo          93.442623\n",
       "      Trancamento              4.098361\n",
       "      Evasão                   2.459016\n",
       "dtype: float64"
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porcentagem_evasoes_por_ano = SIGA.groupby('ano')['situacaoDiscente'].value_counts()/SIGA.groupby('ano')['situacaoDiscente'].count() * 100\n",
    "porcentagem_evasoes_por_ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ano,situacaoDiscente'>"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "porcentagem_evasoes_por_ano.plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Como os anos de pandemia impactaram no rendimento dos estudantes em relação aos anos anteriores, considerando o rendimento dos aprovados, a taxa de cancelamento e as reprovações? Considere como anos de pandemia os anos de 2020 e 2021."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De acordo com o que pode ser observado nos gráficos a respeito dos anos da pandameia em que houve mais distanciamento social (2020 e 2021), tem-se que as taxas de evasão foram menores do que a média dos anos anteriores. A frequência dos aprovados foi semelhante à dos períodos sem distanciamento, porém as notas dos aprovados foram superiores."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Compare a volta às aulas híbrida (2022 período 1) com os anos de pandemia e os anos anteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>R-freq</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC2</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Matriculado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC1</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   matricula  codigo                          disciplina periodo   ano   \n",
       "7          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2022  \\\n",
       "8          1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2022   \n",
       "\n",
       "  curriculo  ch  nota  frequencia       status   tipo observacao     natureza   \n",
       "7       Sim  60     0          25       R-freq  TURMA        NaN  Obrigatória  \\\n",
       "8       Sim  60     0           0  Matriculado  TURMA        NaN  Obrigatória   \n",
       "\n",
       "  situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "7   Registro ativo     BCC2                21A  \n",
       "8   Registro ativo     BCC1                21A  "
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turmas_2022 = SIGA[SIGA['ano'] == 2022]\n",
    "turmas_2022.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>R-freq</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC2</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>Aprovado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC1</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    matricula  codigo                          disciplina periodo   ano   \n",
       "7           1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2022  \\\n",
       "10          3  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       1  2022   \n",
       "\n",
       "   curriculo  ch  nota  frequencia    status   tipo observacao     natureza   \n",
       "7        Sim  60     0          25    R-freq  TURMA        NaN  Obrigatória  \\\n",
       "10       Sim  60    72         100  Aprovado  TURMA        NaN  Obrigatória   \n",
       "\n",
       "   situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "7    Registro ativo     BCC2                21A  \n",
       "10   Registro ativo     BCC1                21A  "
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turmas_2022_1 = turmas_2022[turmas_2022['periodo'] == '1']\n",
    "\n",
    "turmas_2022_1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.42857142857143"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nota_media_aprovados_turmas_2022_1 = turmas_2022_1[turmas_2022_1['status'] == 'Aprovado']['nota'].mean()\n",
    "nota_media_aprovados_turmas_2022_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.77777777777778"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nota_media_reprovados_por_nota_turmas_2022_1 = turmas_2022_1[turmas_2022_1['status'] == 'R-nota']['nota'].mean()\n",
    "nota_media_reprovados_por_nota_turmas_2022_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.5925925925926"
      ]
     },
     "execution_count": 635,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia_turmas_2022_1_reprovados_nota = turmas_2022_1[turmas_2022_1['status'] == 'R-nota']['frequencia'].mean()\n",
    "frequencia_turmas_2022_1_reprovados_nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1578947368421053"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porcentagem_evasoes_turmas_2022_1 = turmas_2022_1[turmas_2022_1['situacaoDiscente'] == 'Evasão']['situacaoDiscente'].count()/turmas_2022_1['situacaoDiscente'].count() * 100\n",
    "porcentagem_evasoes_turmas_2022_1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A volta às aulas híbrida teve a menor taxa de evasão da série histórica. A frequência dos reprovados por nota foi bastante alta, a maior da série histórica. A nota dos aprovados no período híbrido foi maior do que a média histórica, sendo menor apenas que a do período remoto."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Compare a volta às aulas presencial (2022 período 2) com a volta híbrida do item anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matricula</th>\n",
       "      <th>codigo</th>\n",
       "      <th>disciplina</th>\n",
       "      <th>periodo</th>\n",
       "      <th>ano</th>\n",
       "      <th>curriculo</th>\n",
       "      <th>ch</th>\n",
       "      <th>nota</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>status</th>\n",
       "      <th>tipo</th>\n",
       "      <th>observacao</th>\n",
       "      <th>natureza</th>\n",
       "      <th>situacaoDiscente</th>\n",
       "      <th>nomeTuma</th>\n",
       "      <th>codigoCurriculoSie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Matriculado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC1</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>CI1055</td>\n",
       "      <td>ALGORITMOS E ESTRUTURAS DE DADOS 1</td>\n",
       "      <td>2</td>\n",
       "      <td>2022</td>\n",
       "      <td>Sim</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Matriculado</td>\n",
       "      <td>TURMA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Obrigatória</td>\n",
       "      <td>Registro ativo</td>\n",
       "      <td>BCC1</td>\n",
       "      <td>21A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    matricula  codigo                          disciplina periodo   ano   \n",
       "8           1  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2022  \\\n",
       "12          4  CI1055  ALGORITMOS E ESTRUTURAS DE DADOS 1       2  2022   \n",
       "\n",
       "   curriculo  ch  nota  frequencia       status   tipo observacao   \n",
       "8        Sim  60     0           0  Matriculado  TURMA        NaN  \\\n",
       "12       Sim  60     0           0  Matriculado  TURMA        NaN   \n",
       "\n",
       "       natureza situacaoDiscente nomeTuma codigoCurriculoSie  \n",
       "8   Obrigatória   Registro ativo     BCC1                21A  \n",
       "12  Obrigatória   Registro ativo     BCC1                21A  "
      ]
     },
     "execution_count": 637,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turmas_2022_2 = turmas_2022[turmas_2022['periodo'] == '2']\n",
    "turmas_2022_2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nota\n",
       "0    27\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turmas_2022_2['nota'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nota_media_aprovados_turmas_2022_2 = turmas_2022_2[turmas_2022_2['status'] == 'Aprovado']['nota'].mean()\n",
    "nota_media_aprovados_turmas_2022_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nota_media_reprovados_por_nota_turmas_2022_2 = turmas_2022_2[turmas_2022_2['status'] == 'Aprovado']['nota'].mean()\n",
    "nota_media_reprovados_por_nota_turmas_2022_2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados do período de volta às aulas presencial, segundo semestre de 2022 não está finalizado no banco de dados utilizado, o que impossibilita sua análise, bem como a comparação com o período anterior."
   ]
  }
 ],
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