analise_docentes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Microdados da Educação Superior - Docentes de todas as universidades do país"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd\r\n",
    "import random\r\n",
    "import matplotlib.pyplot as plt \r\n",
    "import numpy as np\r\n",
    "\r\n",
    "\r\n",
    "df = pd.read_csv(\r\n",
    "    'SUP_DOCENTE_2019.csv',\r\n",
    "    encoding = \"ISO-8859-1\",\r\n",
    "    sep = '|'\r\n",
    ")\r\n",
    "\r\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   NU_ANO_CENSO  CO_IES  TP_CATEGORIA_ADMINISTRATIVA  \\\n",
       "0          2019       1                            1   \n",
       "1          2019       1                            1   \n",
       "2          2019       1                            1   \n",
       "3          2019       1                            1   \n",
       "4          2019       1                            1   \n",
       "\n",
       "   TP_ORGANIZACAO_ACADEMICA  CO_DOCENTE_IES                        ID_DOCENTE  \\\n",
       "0                         1         1178692  EC5AA8E675AD29C50386820A3F3E9F97   \n",
       "1                         1         1178691  DDA849C5C6275470F9175628F502218E   \n",
       "2                         1         1178693  2FF35722D03A9C6459FB5470F17F6709   \n",
       "3                         1         1180431  C5AB8500E4EC686BFFF71870108EA10A   \n",
       "4                         1         1178686  60FD7BF0222B1B16D19D9E4E08D4178A   \n",
       "\n",
       "   TP_SITUACAO  TP_ESCOLARIDADE  TP_REGIME_TRABALHO  TP_SEXO  ...  \\\n",
       "0            1                5                 1.0        2  ...   \n",
       "1            1                4                 1.0        2  ...   \n",
       "2            1                3                 1.0        2  ...   \n",
       "3            1                5                 1.0        2  ...   \n",
       "4            1                5                 1.0        2  ...   \n",
       "\n",
       "   IN_ATUACAO_GRAD_PRESENCIAL  IN_ATUACAO_POS_EAD  IN_ATUACAO_POS_PRESENCIAL  \\\n",
       "0                         1.0                 0.0                        1.0   \n",
       "1                         1.0                 0.0                        0.0   \n",
       "2                         1.0                 0.0                        0.0   \n",
       "3                         1.0                 0.0                        0.0   \n",
       "4                         0.0                 0.0                        0.0   \n",
       "\n",
       "   IN_ATUACAO_SEQUENCIAL  IN_ATUACAO_PESQUISA  IN_BOLSA_PESQUISA  \\\n",
       "0                    0.0                  1.0                1.0   \n",
       "1                    0.0                  1.0                0.0   \n",
       "2                    0.0                  1.0                0.0   \n",
       "3                    0.0                  0.0                NaN   \n",
       "4                    0.0                  1.0                0.0   \n",
       "\n",
       "   IN_SUBSTITUTO  IN_EXERCICIO_DATA_REFERENCIA  IN_VISITANTE  \\\n",
       "0            0.0                           1.0           0.0   \n",
       "1            0.0                           1.0           0.0   \n",
       "2            0.0                           1.0           0.0   \n",
       "3            0.0                           1.0           0.0   \n",
       "4            0.0                           0.0           0.0   \n",
       "\n",
       "   TP_VISITANTE_IFES_VINCULO  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "\n",
       "[5 rows x 41 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_ANO_CENSO</th>\n",
       "      <th>CO_IES</th>\n",
       "      <th>TP_CATEGORIA_ADMINISTRATIVA</th>\n",
       "      <th>TP_ORGANIZACAO_ACADEMICA</th>\n",
       "      <th>CO_DOCENTE_IES</th>\n",
       "      <th>ID_DOCENTE</th>\n",
       "      <th>TP_SITUACAO</th>\n",
       "      <th>TP_ESCOLARIDADE</th>\n",
       "      <th>TP_REGIME_TRABALHO</th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th>...</th>\n",
       "      <th>IN_ATUACAO_GRAD_PRESENCIAL</th>\n",
       "      <th>IN_ATUACAO_POS_EAD</th>\n",
       "      <th>IN_ATUACAO_POS_PRESENCIAL</th>\n",
       "      <th>IN_ATUACAO_SEQUENCIAL</th>\n",
       "      <th>IN_ATUACAO_PESQUISA</th>\n",
       "      <th>IN_BOLSA_PESQUISA</th>\n",
       "      <th>IN_SUBSTITUTO</th>\n",
       "      <th>IN_EXERCICIO_DATA_REFERENCIA</th>\n",
       "      <th>IN_VISITANTE</th>\n",
       "      <th>TP_VISITANTE_IFES_VINCULO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2019</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2019</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
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       "      <td>1</td>\n",
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     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### **Observação**: Importante salientar que esses envolvem informações de docentes de todas as instituições públicas e privadas que participam do censo anual."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### **Quantidade da amostra**"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Professores x Professoras"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "qt_mulheres = df.loc[df['TP_SEXO'] == 1].count()[0]\r\n",
    "qt_homens = df.loc[df['TP_SEXO'] == 2].count()[0]\r\n",
    "\r\n",
    "mulheres = df.loc[df['TP_SEXO'] == 1]\r\n",
    "\r\n",
    "labels = ['Mulheres', 'Homens']\r\n",
    "values = [qt_mulheres, qt_homens]\r\n",
    "\r\n",
    "fig, ax = plt.subplots() # Retorna Figures e Axes\r\n",
    "\r\n",
    "ax.pie(values, labels = labels, colors=['#64B7CC', '#D0E4AE'], autopct='%1.1f%%', shadow=False, startangle=90)\r\n",
    "\r\n",
    "ax.axis('equal')\r\n",
    "\r\n",
    "plt.title('Docentes de Instituições Privadas e Públicas por sexo')\r\n",
    "\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### É possível observar que há uma quantidade maior de docentes do sexo masculino"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## **Relação de docentes por raça e sexo**"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "brancos = df.loc[df['TP_COR_RACA'] == 1].count()[0]\r\n",
    "pretos = df.loc[df['TP_COR_RACA'] == 2].count()[0]\r\n",
    "pardos = df.loc[df['TP_COR_RACA'] == 3].count()[0]\r\n",
    "amarelos = df.loc[df['TP_COR_RACA'] == 4].count()[0]\r\n",
    "indigenas = df.loc[df['TP_COR_RACA'] == 5].count()[0]\r\n",
    "no_info = df.loc[df['TP_COR_RACA'] == (9 or 0)].count()[0]\r\n",
    "\r\n",
    "x = np.char.array(['Brancos', 'Pretos', 'Pardos', 'Amarelos', 'Indígenas'])\r\n",
    "y = np.array()\r\n",
    "\r\n",
    "colors = (['lightskyblue','lightcoral','gold','darkblue','red'])\r\n",
    "porcent = 100.*y/y.sum()\r\n",
    "\r\n",
    "patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)\r\n",
    "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(x, porcent)]\r\n",
    "\r\n",
    "plt.legend(patches, labels, bbox_to_anchor=(-0.1, 1.),\r\n",
    "           fontsize=8)\r\n",
    "\r\n",
    "plt.title('Docentes de todo o país por raça')\r\n",
    "\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### É possível observar que, entre os docentes, há uma grande predominância de pessoas brancas, o que é um indicativo de todo histórico desigual do país.\r\n",
    "\r\n",
    "### Para uma visão mais aprofundada, vamos dividir essa relação por gênero."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "mulheres = df.loc[df['TP_SEXO'] == 1]\r\n",
    "\r\n",
    "mulheres_B = mulheres.loc[mulheres['TP_COR_RACA'] == 1].count()[0] # Mulheres Brancas\r\n",
    "mulheres_P = mulheres.loc[mulheres['TP_COR_RACA'] == 2].count()[0] # Mulheres Pretas\r\n",
    "mulheres_Pa = mulheres.loc[mulheres['TP_COR_RACA'] == 3].count()[0] # Mulheres Pardas\r\n",
    "mulheres_A = mulheres.loc[mulheres['TP_COR_RACA'] == 4].count()[0] # Mulheres Amarelas\r\n",
    "mulheres_In = mulheres.loc[mulheres['TP_COR_RACA'] == 5].count()[0] # Mulheres Indígenas\r\n",
    "mulheres_ND = mulheres.loc[mulheres['TP_COR_RACA'] == (9 or 0)].count()[0] # Mulheres que não declararam ou não há essa informação\r\n",
    "\r\n",
    "x_axis = ['Mulheres brancas', 'Mulheres pretas', 'Mulheres pardas', 'Mulheres amarelas', 'Mulheres indígenas', 'Não informado/Não declarado']\r\n",
    "y_axis = [mulheres_B, mulheres_P, mulheres_Pa, mulheres_A, mulheres_In, mulheres_ND]\r\n",
    "\r\n",
    "plt.title('Docentes mulheres de Instituições Públicas e Privadas por raça')\r\n",
    "\r\n",
    "plt.barh(x_axis, y_axis)\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "mulheres brancas:  99039\n",
      "mulheres pretas:  3506\n",
      "mulheres pardas: 26863\n",
      "mulheres amarelas: 1880\n",
      "mulheres indígenas: 200\n",
      "mulheres n definidas:  0\n"
     ]
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"
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      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "homens = df.loc[df['TP_SEXO'] == 2]\r\n",
    "\r\n",
    "homens_B = homens.loc[homens['TP_COR_RACA'] == 1].count()[0] # Homens brancos\r\n",
    "homens_P = homens.loc[homens['TP_COR_RACA'] == 2].count()[0] # Homens pretos\r\n",
    "homens_Pa = homens.loc[homens['TP_COR_RACA'] == 3].count()[0] # Homens pardos\r\n",
    "homens_A = homens.loc[homens['TP_COR_RACA'] == 4].count()[0] # Homens amarelos\r\n",
    "homens_In = homens.loc[homens['TP_COR_RACA'] == 5].count()[0] # Homens Indígenas\r\n",
    "homens_ND = homens.loc[homens['TP_COR_RACA'] == (9 or 0)].count()[0] # Homens que não declararam ou não há essa informação\r\n",
    "\r\n",
    "x_axis = ['Homens brancos', 'Homens pretos', 'Homens pardos', 'Homens amarelas', 'Homens indígenas', 'Não informado/Não declarado']\r\n",
    "y_axis = [homens_B, homens_P, homens_Pa, homens_A, homens_In, homens_ND]\r\n",
    "\r\n",
    "plt.title('Docentes homens de Instituições Públicas e Privadas por raça')\r\n",
    "\r\n",
    "plt.barh(x_axis, y_axis)\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "mulheres brancas:  110998\n",
      "mulheres pretas:  4583\n",
      "mulheres pardas: 31679\n",
      "mulheres amarelas: 2143\n",
      "mulheres indígenas: 305\n",
      "mulheres n definidas:  0\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Com números absolutos se torna muito mais clara a discrepância. Esses números mostram que, dos 399.428 docentes registrados no Censo Universitário de 2019, 299.571 são brancos."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "---\r\n",
    "# **Agora, uma análise da escolaridade de professores de Universidades públicas e particulares**"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "docentes_publi = df.loc[df['TP_CATEGORIA_ADMINISTRATIVA'] == (1 or 2 or 3)]\r\n",
    "# docentes_part = df.loc[df['TP_CATEGORIA_ADMINISTRATIVA'] == (4 or 5)].count()[0]\r\n",
    "\r\n",
    "sem_grad = docentes_publi.loc[docentes_publi['TP_ESCOLARIDADE'] == 1].count()[0]\r\n",
    "graduados = docentes_publi.loc[docentes_publi['TP_ESCOLARIDADE'] == 2].count()[0]\r\n",
    "especializados = docentes_publi.loc[docentes_publi['TP_ESCOLARIDADE'] == 3].count()[0]\r\n",
    "mestres = docentes_publi.loc[docentes_publi['TP_ESCOLARIDADE'] == 4].count()[0]\r\n",
    "doutores = docentes_publi.loc[docentes_publi['TP_ESCOLARIDADE'] == 5].count()[0]\r\n",
    "\r\n",
    "x = np.char.array(['Graduados', 'Com Especialização', 'Mestres','Sem Graduação', 'Doutores'])\r\n",
    "y = np.array([graduados, especializados, mestres, sem_grad, doutores])\r\n",
    "\r\n",
    "colors = (['yellowgreen','red','gold','lightskyblue','lightcoral'])\r\n",
    "porcent = 100.*y/y.sum()\r\n",
    "\r\n",
    "patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)\r\n",
    "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(x, porcent)]\r\n",
    "\r\n",
    "plt.legend(patches, labels, bbox_to_anchor=(-0.1, 1.),\r\n",
    "           fontsize=8)\r\n",
    "\r\n",
    "plt.title('Grau de escolaridade de Professores de Instituições Públicas')\r\n",
    "\r\n",
    "plt.show()\r\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "docentes_part = df.loc[df['TP_CATEGORIA_ADMINISTRATIVA'] == (4 or 5)]\r\n",
    "\r\n",
    "sem_grad = docentes_part.loc[docentes_part['TP_ESCOLARIDADE'] == 1].count()[0]\r\n",
    "graduados = docentes_part.loc[docentes_part['TP_ESCOLARIDADE'] == 2].count()[0]\r\n",
    "especializados = docentes_part.loc[docentes_part['TP_ESCOLARIDADE'] == 3].count()[0]\r\n",
    "mestres = docentes_part.loc[docentes_part['TP_ESCOLARIDADE'] == 4].count()[0]\r\n",
    "doutores = docentes_part.loc[docentes_part['TP_ESCOLARIDADE'] == 5].count()[0]\r\n",
    "\r\n",
    "x = np.char.array(['Graduados', 'Com Especialização', 'Mestres','Sem Graduação', 'Doutores'])\r\n",
    "y = np.array([graduados, especializados, mestres, sem_grad, doutores])\r\n",
    "\r\n",
    "colors = (['yellowgreen','red','gold','lightskyblue','lightcoral'])\r\n",
    "porcent = 100.*y/y.sum()\r\n",
    "\r\n",
    "patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)\r\n",
    "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(x, porcent)]\r\n",
    "\r\n",
    "plt.legend(patches, labels, bbox_to_anchor=(-0.1, 1.),\r\n",
    "           fontsize=8)\r\n",
    "\r\n",
    "plt.title('Grau de escolaridade de Professores de Instituições Particulares')\r\n",
    "\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## É possível observar que há um percentual muito maior de docentes doutores em instituições públicas proporcionalmente. Entretanto, em Instituições Públicas a quantidade a docentes apenas com graduação é maior, o que é esperado, tendo em vista que muitos alunos que estão fazendo mestrado conseguem uma vaga como docentes nessas instituições.\r\n"
   ],
   "metadata": {}
  }
 ],
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