analise_ensino_medio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Autor: Caique Nascimento\n",
    "Análise sobre incritos no ENEM:\n",
    "    Terminaram Ensino Médio\n",
    "    Não estão cursando ou não terminaram Ensino Médio\n",
    "\n",
    "Dados disponibilizados pelo INEP:\n",
    "    http://inep.gov.br/microdados'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bibliotecas utilizadas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Configuraçõs iniciais\n",
    "%matplotlib inline\n",
    "low_memory=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variáveis\n",
    "#Como havia muitos arquivos com colunas e delimitadores\n",
    "#diferentes, optei por analisar cada arquivo separadamente\n",
    "f1 = 'MICRODADOS_ENEM_2014.csv'\n",
    "f2 = 'MICRODADOS_ENEM_2015.csv'\n",
    "f3 = 'MICRODADOS_ENEM_2016.csv'\n",
    "f4 = 'MICRODADOS_ENEM_2017.csv'\n",
    "f5 = 'MICRODADOS_ENEM_2018.csv'\n",
    "anos = ['2014', '2015', '2016', '2017', '2018']\n",
    "insc = []\n",
    "naoter = []\n",
    "term = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analisando as colunas dos arquivos\n",
    "df = pd.read_csv(f1, usecols=['ST_CONCLUSAO', 'NU_INSCRICAO'], encoding='iso-8859-1', sep=',')\n",
    "naoter.append(df.query('(ST_CONCLUSAO == 4)')['ST_CONCLUSAO'].count())\n",
    "term.append(df.query('(ST_CONCLUSAO == 1)')['ST_CONCLUSAO'].count())\n",
    "insc.append(len(df['NU_INSCRICAO']))\n",
    "\n",
    "df = pd.read_csv(f2, usecols=['TP_ST_CONCLUSAO', 'NU_INSCRICAO'], encoding='iso-8859-1', sep=',')\n",
    "naoter.append(df.query('(TP_ST_CONCLUSAO == 4 )')['TP_ST_CONCLUSAO'].count())\n",
    "term.append(df.query('(TP_ST_CONCLUSAO == 1)')['TP_ST_CONCLUSAO'].count())\n",
    "insc.append(len(df['NU_INSCRICAO']))\n",
    "\n",
    "df = pd.read_csv(f3, usecols=['TP_ST_CONCLUSAO', 'NU_INSCRICAO'], encoding='iso-8859-1', sep=';')\n",
    "naoter.append(df.query('(TP_ST_CONCLUSAO == 4)')['TP_ST_CONCLUSAO'].count())\n",
    "term.append(df.query('(TP_ST_CONCLUSAO == 1)')['TP_ST_CONCLUSAO'].count())\n",
    "insc.append(len(df['NU_INSCRICAO']))\n",
    "\n",
    "df = pd.read_csv(f4, usecols=['TP_ST_CONCLUSAO', 'NU_INSCRICAO'], encoding='iso-8859-1', sep=';')\n",
    "naoter.append(df.query('(TP_ST_CONCLUSAO == 4)')['TP_ST_CONCLUSAO'].count())\n",
    "term.append(df.query('(TP_ST_CONCLUSAO == 1)')['TP_ST_CONCLUSAO'].count())\n",
    "insc.append(len(df['NU_INSCRICAO']))\n",
    "\n",
    "df = pd.read_csv(f5, usecols=['TP_ST_CONCLUSAO', 'NU_INSCRICAO'], encoding='iso-8859-1', sep=';')\n",
    "naoter.append(df.query('(TP_ST_CONCLUSAO == 4)')['TP_ST_CONCLUSAO'].count())\n",
    "term.append(df.query('(TP_ST_CONCLUSAO == 1)')['TP_ST_CONCLUSAO'].count())\n",
    "insc.append(len(df['NU_INSCRICAO']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotar no gráfico\n",
    "#Utilizei alguns modelos de gráficos para que os dados pudessem ser visto de várias formas\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(anos, insc, lw = 2, color = '#4F5C9B', alpha = 1, label = 'Inscritos' )\n",
    "ax.plot(anos, term, lw = 2, color = '#004F26', alpha = 1, label = 'Terminaram' )\n",
    "ax.plot(anos, naoter, lw = 2, color = '#F47A1A', alpha = 1, label = 'Não Terminaram')\n",
    "ax.grid()\n",
    "plt.legend()\n",
    "ax.set_title('Inscritos x Terminaram x Não Terminaram Ensino Médio')\n",
    "ax.set_xlabel('Anos')\n",
    "ax.set_ylabel('Quantidade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Outra plotagem\n",
    "\n",
    "barWidth = 0.3\n",
    "bars1 = insc\n",
    "bars2 = term\n",
    "bars3 = naoter\n",
    "yer1 = [2014, 2015, 2016, 2017, 2018]\n",
    "yer2 = [2014, 2015, 2016, 2017, 2018]\n",
    "yer3 = [2014, 2015, 2016, 2017, 2018]\n",
    " \n",
    "r1 = np.arange(len(bars1))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    " \n",
    "plt.bar(r1, bars1, width = barWidth, color = '#4F5C9B', edgecolor = 'black', yerr=yer1, capsize=7, label='Inscritos')\n",
    "plt.bar(r2, bars2, width = barWidth, color = '#004F26', edgecolor = 'black', yerr=yer2, capsize=7, label='Terminaram')\n",
    "plt.bar(r3, bars3, width = barWidth, color = '#F47A1A', edgecolor = 'black', yerr=yer3, capsize=7, label='Não Terminaram')\n",
    " \n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['2014', '2015', '2016', '2017', '2018'])\n",
    "plt.title('Inscritos x Terminaram x Não Terminaram Ensino Médio')\n",
    "plt.ylabel('Quantidade')\n",
    "plt.xlabel('Anos')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Outra plotagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=anos\n",
    "y= insc\n",
    "\n",
    "plt.stackplot(x,y, labels=['Inscritos'], edgecolor = 'black')\n",
    "plt.stackplot(x, term, labels=['Terminaram'])\n",
    "plt.stackplot(x, naoter, labels=['Não Terminaram'])\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Inscritos x Terminaram x Não Terminaram Ensino Médio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Inscritos</td>\n",
       "      <td>8722248</td>\n",
       "      <td>7746427</td>\n",
       "      <td>8627367</td>\n",
       "      <td>6731341</td>\n",
       "      <td>5513747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Terminaram</td>\n",
       "      <td>4990184</td>\n",
       "      <td>4492044</td>\n",
       "      <td>4928251</td>\n",
       "      <td>4272215</td>\n",
       "      <td>3233968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Não Terminaram</td>\n",
       "      <td>537273</td>\n",
       "      <td>446963</td>\n",
       "      <td>472753</td>\n",
       "      <td>74730</td>\n",
       "      <td>52741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   2014     2015     2016     2017     2018\n",
       "Inscritos       8722248  7746427  8627367  6731341  5513747\n",
       "Terminaram      4990184  4492044  4928251  4272215  3233968\n",
       "Não Terminaram   537273   446963   472753    74730    52741"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dados mostrados em uma tabela\n",
    "dadostb = {}\n",
    "for x in range(5):    \n",
    "    dadostb.update({anos[x]: [insc[x], term[x], naoter[x]]})\n",
    "dfdt = pd.DataFrame(dadostb, columns=anos, index=['Inscritos', 'Terminaram', 'Não Terminaram'])\n",
    "dfdt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}