Analise_Capes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 601,
   "id": "2a752aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "0df534ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://www.kaggle.com/datasets/voltdata/capes-bolsas\n",
    "data = pd.read_csv(r\"C:\\Users\\laura\\Downloads\\capes_bolsas.csv\", index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe19dc8",
   "metadata": {},
   "source": [
    "### Verificação e limpeza de dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "a21c5baa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 136114 entries, 1 to 136114\n",
      "Data columns (total 28 columns):\n",
      " #   Column                               Non-Null Count   Dtype  \n",
      "---  ------                               --------------   -----  \n",
      " 0   Ano                                  136114 non-null  int64  \n",
      " 1   UF                                   136114 non-null  object \n",
      " 2   Município                            136114 non-null  object \n",
      " 3   Região                               136114 non-null  object \n",
      " 4   Código Programa                      130415 non-null  object \n",
      " 5   Programa Fomento                     136114 non-null  object \n",
      " 6   IES                                  136113 non-null  object \n",
      " 7   Status Jurídico                      136114 non-null  object \n",
      " 8   Programa                             130415 non-null  object \n",
      " 9   Área Avaliação                       136114 non-null  object \n",
      " 10  Área Conhecimento                    136114 non-null  object \n",
      " 11  Grande Área                          136114 non-null  object \n",
      " 12  Codigo IES                           10664 non-null   float64\n",
      " 13  Coordenador Geral IsF                60923 non-null   float64\n",
      " 14  Coordenador Pedagógico IsF           60923 non-null   float64\n",
      " 15  DOUTORADO PLENO                      136114 non-null  int64  \n",
      " 16  Iniciação Científica                 60923 non-null   float64\n",
      " 17  JOVENS TALENTOS - A                  10664 non-null   float64\n",
      " 18  JOVENS TALENTOS - B                  10664 non-null   float64\n",
      " 19  MESTRADO                             136114 non-null  int64  \n",
      " 20  MESTRADO PROFISSIONAL                77088 non-null   float64\n",
      " 21  PESQUISADOR VISITANTE ESPECIAL       10664 non-null   float64\n",
      " 22  PROF VISITANTE NACIONAL SÊNIOR       84540 non-null   float64\n",
      " 23  PROFESSOR VISITANTE EXTERIOR PLENO   10664 non-null   float64\n",
      " 24  PROFESSOR VISITANTE EXTERIOR SENIOR  10664 non-null   float64\n",
      " 25  Professor IsF                        60923 non-null   float64\n",
      " 26  PÓS-DOUTORADO                        126713 non-null  float64\n",
      " 27  Total Linha                          136114 non-null  int64  \n",
      "dtypes: float64(13), int64(4), object(11)\n",
      "memory usage: 30.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info() #verificando informações gerais do dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "fc0a5d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano                                         0\n",
       "UF                                          0\n",
       "Município                                   0\n",
       "Região                                      0\n",
       "Código Programa                          5699\n",
       "Programa Fomento                            0\n",
       "IES                                         1\n",
       "Status Jurídico                             0\n",
       "Programa                                 5699\n",
       "Área Avaliação                              0\n",
       "Área Conhecimento                           0\n",
       "Grande Área                                 0\n",
       "Codigo IES                             125450\n",
       "Coordenador Geral IsF                   75191\n",
       "Coordenador Pedagógico IsF              75191\n",
       "DOUTORADO PLENO                             0\n",
       "Iniciação Científica                    75191\n",
       "JOVENS TALENTOS - A                    125450\n",
       "JOVENS TALENTOS - B                    125450\n",
       "MESTRADO                                    0\n",
       "MESTRADO PROFISSIONAL                   59026\n",
       "PESQUISADOR VISITANTE ESPECIAL         125450\n",
       "PROF VISITANTE NACIONAL SÊNIOR          51574\n",
       "PROFESSOR VISITANTE EXTERIOR PLENO     125450\n",
       "PROFESSOR VISITANTE EXTERIOR SENIOR    125450\n",
       "Professor IsF                           75191\n",
       "PÓS-DOUTORADO                            9401\n",
       "Total Linha                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum() #verificandos colunas com informações nulas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "9bf13210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136114, 28)"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #verificando quantidade de linhas e colunas no dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "f6baaeaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9829 entries, 5 to 10664\n",
      "Data columns (total 28 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   Ano                                  9829 non-null   int64  \n",
      " 1   UF                                   9829 non-null   object \n",
      " 2   Município                            9829 non-null   object \n",
      " 3   Região                               9829 non-null   object \n",
      " 4   Código Programa                      9829 non-null   object \n",
      " 5   Programa Fomento                     9829 non-null   object \n",
      " 6   IES                                  9829 non-null   object \n",
      " 7   Status Jurídico                      9829 non-null   object \n",
      " 8   Programa                             9829 non-null   object \n",
      " 9   Área Avaliação                       9829 non-null   object \n",
      " 10  Área Conhecimento                    9829 non-null   object \n",
      " 11  Grande Área                          9829 non-null   object \n",
      " 12  Codigo IES                           9829 non-null   float64\n",
      " 13  Coordenador Geral IsF                9829 non-null   float64\n",
      " 14  Coordenador Pedagógico IsF           9829 non-null   float64\n",
      " 15  DOUTORADO PLENO                      9829 non-null   int64  \n",
      " 16  Iniciação Científica                 9829 non-null   float64\n",
      " 17  JOVENS TALENTOS - A                  9829 non-null   float64\n",
      " 18  JOVENS TALENTOS - B                  9829 non-null   float64\n",
      " 19  MESTRADO                             9829 non-null   int64  \n",
      " 20  MESTRADO PROFISSIONAL                9829 non-null   float64\n",
      " 21  PESQUISADOR VISITANTE ESPECIAL       9829 non-null   float64\n",
      " 22  PROF VISITANTE NACIONAL SÊNIOR       9829 non-null   float64\n",
      " 23  PROFESSOR VISITANTE EXTERIOR PLENO   9829 non-null   float64\n",
      " 24  PROFESSOR VISITANTE EXTERIOR SENIOR  9829 non-null   float64\n",
      " 25  Professor IsF                        9829 non-null   float64\n",
      " 26  PÓS-DOUTORADO                        9829 non-null   float64\n",
      " 27  Total Linha                          9829 non-null   int64  \n",
      "dtypes: float64(13), int64(4), object(11)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.dropna(inplace=True) #removendos todas as informações nulas do dataframe (há outras tratativas que podem ser feitas, mas para fim didáticos, iremos remover info nulas)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "db6dcd5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ano</th>\n",
       "      <th>UF</th>\n",
       "      <th>Município</th>\n",
       "      <th>Região</th>\n",
       "      <th>Código Programa</th>\n",
       "      <th>Programa Fomento</th>\n",
       "      <th>IES</th>\n",
       "      <th>Status Jurídico</th>\n",
       "      <th>Programa</th>\n",
       "      <th>Área Conhecimento</th>\n",
       "      <th>Grande Área</th>\n",
       "      <th>DOUTORADO PLENO</th>\n",
       "      <th>Iniciação Científica</th>\n",
       "      <th>MESTRADO</th>\n",
       "      <th>MESTRADO PROFISSIONAL</th>\n",
       "      <th>PÓS-DOUTORADO</th>\n",
       "      <th>Total Linha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>AC</td>\n",
       "      <td>RIO BRANCO</td>\n",
       "      <td>NORTE</td>\n",
       "      <td>11001011001P8</td>\n",
       "      <td>DS</td>\n",
       "      <td>UFAC</td>\n",
       "      <td>Federal</td>\n",
       "      <td>ECOLOGIA E MANEJO DE RECURSOS NATURAIS</td>\n",
       "      <td>ECOLOGIA</td>\n",
       "      <td>CIÊNCIAS BIOLÓGICAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>AC</td>\n",
       "      <td>RIO BRANCO</td>\n",
       "      <td>NORTE</td>\n",
       "      <td>11001011001P8</td>\n",
       "      <td>PNPD</td>\n",
       "      <td>UFAC</td>\n",
       "      <td>Federal</td>\n",
       "      <td>ECOLOGIA E MANEJO DE RECURSOS NATURAIS</td>\n",
       "      <td>ECOLOGIA</td>\n",
       "      <td>CIÊNCIAS BIOLÓGICAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>AC</td>\n",
       "      <td>RIO BRANCO</td>\n",
       "      <td>NORTE</td>\n",
       "      <td>11001011003P0</td>\n",
       "      <td>DS</td>\n",
       "      <td>UFAC</td>\n",
       "      <td>Federal</td>\n",
       "      <td>LETRAS- LINGUAGEM E IDENTIDADE</td>\n",
       "      <td>LETRAS</td>\n",
       "      <td>LINGÜÍSTICA, LETRAS E ARTES</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>AC</td>\n",
       "      <td>RIO BRANCO</td>\n",
       "      <td>NORTE</td>\n",
       "      <td>11001011003P0</td>\n",
       "      <td>PNPD</td>\n",
       "      <td>UFAC</td>\n",
       "      <td>Federal</td>\n",
       "      <td>LETRAS- LINGUAGEM E IDENTIDADE</td>\n",
       "      <td>LETRAS</td>\n",
       "      <td>LINGÜÍSTICA, LETRAS E ARTES</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>AC</td>\n",
       "      <td>RIO BRANCO</td>\n",
       "      <td>NORTE</td>\n",
       "      <td>11001011004P7</td>\n",
       "      <td>DS</td>\n",
       "      <td>UFAC</td>\n",
       "      <td>Federal</td>\n",
       "      <td>PRODUÇÃO VEGETAL</td>\n",
       "      <td>AGRONOMIA</td>\n",
       "      <td>CIÊNCIAS AGRÁRIAS</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ano  UF   Município Região Código Programa Programa Fomento   IES  \\\n",
       "5  2018  AC  RIO BRANCO  NORTE   11001011001P8               DS  UFAC   \n",
       "6  2018  AC  RIO BRANCO  NORTE   11001011001P8             PNPD  UFAC   \n",
       "7  2018  AC  RIO BRANCO  NORTE   11001011003P0               DS  UFAC   \n",
       "8  2018  AC  RIO BRANCO  NORTE   11001011003P0             PNPD  UFAC   \n",
       "9  2018  AC  RIO BRANCO  NORTE   11001011004P7               DS  UFAC   \n",
       "\n",
       "  Status Jurídico                                Programa Área Conhecimento  \\\n",
       "5         Federal  ECOLOGIA E MANEJO DE RECURSOS NATURAIS          ECOLOGIA   \n",
       "6         Federal  ECOLOGIA E MANEJO DE RECURSOS NATURAIS          ECOLOGIA   \n",
       "7         Federal          LETRAS- LINGUAGEM E IDENTIDADE            LETRAS   \n",
       "8         Federal          LETRAS- LINGUAGEM E IDENTIDADE            LETRAS   \n",
       "9         Federal                        PRODUÇÃO VEGETAL         AGRONOMIA   \n",
       "\n",
       "                   Grande Área  DOUTORADO PLENO  Iniciação Científica  \\\n",
       "5          CIÊNCIAS BIOLÓGICAS                0                   0.0   \n",
       "6          CIÊNCIAS BIOLÓGICAS                0                   0.0   \n",
       "7  LINGÜÍSTICA, LETRAS E ARTES                0                   0.0   \n",
       "8  LINGÜÍSTICA, LETRAS E ARTES                0                   0.0   \n",
       "9            CIÊNCIAS AGRÁRIAS               18                   0.0   \n",
       "\n",
       "   MESTRADO  MESTRADO PROFISSIONAL  PÓS-DOUTORADO  Total Linha  \n",
       "5         8                    0.0            0.0            8  \n",
       "6         0                    0.0            1.0            1  \n",
       "7        17                    0.0            0.0           17  \n",
       "8         0                    0.0            1.0            1  \n",
       "9         9                    0.0            0.0           27  "
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['PROFESSOR VISITANTE EXTERIOR PLENO', 'PROFESSOR VISITANTE EXTERIOR SENIOR', 'PROF VISITANTE NACIONAL SÊNIOR','PESQUISADOR VISITANTE ESPECIAL','JOVENS TALENTOS - A', 'JOVENS TALENTOS - B','Coordenador Pedagógico IsF','Coordenador Geral IsF', 'Área Avaliação', 'Professor IsF', 'Codigo IES'], axis=1, inplace=True) #removendos as colunas que não iremos analisar\n",
    "# data.columns = data.columns.str.replace(\" \", \"_\") #substiruir os espaços vazios por \"_\"\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36684ed3",
   "metadata": {},
   "source": [
    "### Analisando os dados:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4698d4e0",
   "metadata": {},
   "source": [
    "##### 1) Analisando as bolsas concedidas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "id": "da2b9a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Linha</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IES</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USP</th>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFRJ</th>\n",
       "      <td>3479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFRGS</th>\n",
       "      <td>3330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFMG</th>\n",
       "      <td>3085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFSC</th>\n",
       "      <td>2838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Linha\n",
       "IES               \n",
       "USP           5169\n",
       "UFRJ          3479\n",
       "UFRGS         3330\n",
       "UFMG          3085\n",
       "UFSC          2838"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Por IES:\n",
    "\n",
    "dados_IES = data.loc(axis=1)[['IES', 'Total Linha']] #criado um df apenas com as colunas 'Grande Área' e 'Iniciação Científica'\n",
    "dados_IES_1 = dados_IES.groupby('IES').sum('Total Linha') #agrupando as informações por Área de Conhecimento \n",
    "dados_IES_1.sort_values(by='Total Linha', ascending=False, inplace=True) #organizando por ordem decrescente em Total Linha\n",
    "dados_IES_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "id": "2a2879c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Qnt de bolsas')"
      ]
     },
     "execution_count": 574,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "grafico_dados_IES = dados_IES_1.head().plot(kind=\"barh\")\n",
    "grafico_dados_IES.set_title('Qnt de bolsas x IES')\n",
    "grafico_dados_IES.set_ylabel('IES')\n",
    "grafico_dados_IES.set_xlabel('Qnt de bolsas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "id": "a30df020",
   "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>Total Linha</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grande Área</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS HUMANAS</th>\n",
       "      <td>13772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS DA SAÚDE</th>\n",
       "      <td>13430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS AGRÁRIAS</th>\n",
       "      <td>13079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS EXATAS E DA TERRA</th>\n",
       "      <td>10640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGENHARIAS</th>\n",
       "      <td>10327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Total Linha\n",
       "Grande Área                            \n",
       "CIÊNCIAS HUMANAS                  13772\n",
       "CIÊNCIAS DA SAÚDE                 13430\n",
       "CIÊNCIAS AGRÁRIAS                 13079\n",
       "CIÊNCIAS EXATAS E DA TERRA        10640\n",
       "ENGENHARIAS                       10327"
      ]
     },
     "execution_count": 581,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Por Grande Área:\n",
    "\n",
    "dados_Area = data.loc(axis=1)[['Grande Área', 'Total Linha']] #filtrando pelas colunas desejadas\n",
    "dados_Area_1 = dados_Area.groupby('Grande Área').sum('Total Linha') #agrupando por Grande Área e somando por Total Linha\n",
    "dados_Area_1.sort_values(by='Total Linha', ascending=False, inplace=True) #organizando por ordem decrescente em Total Linha\n",
    "dados_Area_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "id": "afb6bef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Qnt de bolsas')"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "grafico_dados_area = dados_Area_1.head().plot(kind=\"barh\")\n",
    "grafico_dados_area.set_title('Qnt de bolsas x Grande Área')\n",
    "grafico_dados_area.set_ylabel('Grande Área')\n",
    "grafico_dados_area.set_xlabel('Qnt de bolsas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "id": "6cf6adca",
   "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>Total Linha</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Área Conhecimento</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGRONOMIA</th>\n",
       "      <td>5652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDICINA</th>\n",
       "      <td>5228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTERDISCIPLINAR</th>\n",
       "      <td>4091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCAÇÃO</th>\n",
       "      <td>3295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUÍMICA</th>\n",
       "      <td>3113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Total Linha\n",
       "Área Conhecimento             \n",
       "AGRONOMIA                 5652\n",
       "MEDICINA                  5228\n",
       "INTERDISCIPLINAR          4091\n",
       "EDUCAÇÃO                  3295\n",
       "QUÍMICA                   3113"
      ]
     },
     "execution_count": 577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Por Área de Conhecimento:\n",
    "\n",
    "dados_area_conhecimento = data.loc(axis=1)[['Área Conhecimento', 'Total Linha']]  #filtrando pelas colunas desejadas\n",
    "dados_area_conhecimento_1 = dados_area_conhecimento.groupby('Área Conhecimento').sum('Total Linha') #agrupando por Área Conhecimento e somando por Total Linha\n",
    "dados_area_conhecimento_1.sort_values(by='Total Linha', ascending=False, inplace=True) #organizando por ordem decrescente em Total Linha\n",
    "dados_area_conhecimento_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707375ee",
   "metadata": {},
   "source": [
    "#### 2) Analisando bolsas de IC concedidas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "id": "7c1b676a",
   "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>Iniciação Científica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grande Área</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS BIOLÓGICAS</th>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS EXATAS E DA TERRA</th>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS HUMANAS</th>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIÊNCIAS AGRÁRIAS</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGENHARIAS</th>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Iniciação Científica\n",
       "Grande Área                                     \n",
       "CIÊNCIAS BIOLÓGICAS                        166.0\n",
       "CIÊNCIAS EXATAS E DA TERRA                 108.0\n",
       "CIÊNCIAS HUMANAS                            82.0\n",
       "CIÊNCIAS AGRÁRIAS                           66.0\n",
       "ENGENHARIAS                                 54.0"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Por Grande Área:\n",
    "\n",
    "data_area_IC = data.loc(axis=1)[['Grande Área', 'Iniciação Científica']] #filtrando pelas colunas desejadas\n",
    "data_area_IC_1 = data_area_IC.groupby('Grande Área').sum('Iniciação Científica') #agrupando por Grande Área e somando por Inciciação Científica\n",
    "data_area_IC_1.sort_values(by='Iniciação Científica', ascending=False, inplace=True) #organizando por ordem decrescente em Iniciação Científica\n",
    "data_area_IC_1.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "id": "f7e23d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Qnt de bolsas de IC')"
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "grafico_data_area_IC = data_area_IC_1.head().groupby('Grande Área').sum('Iniciação Científica').plot(kind='barh')\n",
    "grafico_data_area_IC.set_title('Qnt de bolsas de IC x Grande Área')\n",
    "grafico_data_area_IC.set_ylabel('Grande Área')\n",
    "grafico_data_area_IC.set_xlabel('Qnt de bolsas de IC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "id": "9ab98ec5",
   "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>Iniciação Científica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Área Conhecimento</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ECOLOGIA</th>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARASITOLOGIA</th>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FÍSICA</th>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGRONOMIA</th>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOCIÊNCIAS</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Iniciação Científica\n",
       "Área Conhecimento                      \n",
       "ECOLOGIA                           48.0\n",
       "PARASITOLOGIA                      38.0\n",
       "FÍSICA                             36.0\n",
       "AGRONOMIA                          32.0\n",
       "GEOCIÊNCIAS                        26.0"
      ]
     },
     "execution_count": 591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Por Área de Conhecimento:\n",
    "data_area_esp_IC = data.loc(axis=1)[['Área Conhecimento', 'Iniciação Científica']] #filtrando pelas colunas desejadas\n",
    "data_area_esp_IC_1 = data_area_esp_IC.groupby('Área Conhecimento').sum('Iniciação Científica') #agrupando por Área Conhecimento e somando por Inciciação Científica\n",
    "data_area_esp_IC_1.sort_values(by='Iniciação Científica', ascending=False, inplace=True) #organizando por ordem decrescente em Iniciação Científica\n",
    "data_area_esp_IC_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "id": "a4300406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Qnt de bolsas de IC')"
      ]
     },
     "execution_count": 593,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "grafic_area_esp_IC = data_area_esp_IC_1.head().plot(kind='barh')\n",
    "grafic_area_esp_IC.set_title('Qnt de bolsas de IC x Área de Conhecimento')\n",
    "grafic_area_esp_IC.set_ylabel('Área de Conhecimento')\n",
    "grafic_area_esp_IC.set_xlabel('Qnt de bolsas de IC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "id": "9a461065",
   "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>DOUTORADO PLENO</th>\n",
       "      <th>Iniciação Científica</th>\n",
       "      <th>MESTRADO</th>\n",
       "      <th>MESTRADO PROFISSIONAL</th>\n",
       "      <th>PÓS-DOUTORADO</th>\n",
       "      <th>Total Linha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DOUTORADO PLENO</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036757</td>\n",
       "      <td>0.696313</td>\n",
       "      <td>-0.007404</td>\n",
       "      <td>-0.239698</td>\n",
       "      <td>0.937057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iniciação Científica</th>\n",
       "      <td>-0.036757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.053392</td>\n",
       "      <td>-0.001685</td>\n",
       "      <td>0.021946</td>\n",
       "      <td>-0.007002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESTRADO</th>\n",
       "      <td>0.696313</td>\n",
       "      <td>-0.053392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009390</td>\n",
       "      <td>-0.307744</td>\n",
       "      <td>0.894522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESTRADO PROFISSIONAL</th>\n",
       "      <td>-0.007404</td>\n",
       "      <td>-0.001685</td>\n",
       "      <td>-0.009390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110330</td>\n",
       "      <td>0.012615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PÓS-DOUTORADO</th>\n",
       "      <td>-0.239698</td>\n",
       "      <td>0.021946</td>\n",
       "      <td>-0.307744</td>\n",
       "      <td>0.110330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Linha</th>\n",
       "      <td>0.937057</td>\n",
       "      <td>-0.007002</td>\n",
       "      <td>0.894522</td>\n",
       "      <td>0.012615</td>\n",
       "      <td>-0.214852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       DOUTORADO PLENO  Iniciação Científica  MESTRADO  \\\n",
       "DOUTORADO PLENO               1.000000             -0.036757  0.696313   \n",
       "Iniciação Científica         -0.036757              1.000000 -0.053392   \n",
       "MESTRADO                      0.696313             -0.053392  1.000000   \n",
       "MESTRADO PROFISSIONAL        -0.007404             -0.001685 -0.009390   \n",
       "PÓS-DOUTORADO                -0.239698              0.021946 -0.307744   \n",
       "Total Linha                   0.937057             -0.007002  0.894522   \n",
       "\n",
       "                       MESTRADO PROFISSIONAL  PÓS-DOUTORADO  Total Linha  \n",
       "DOUTORADO PLENO                    -0.007404      -0.239698     0.937057  \n",
       "Iniciação Científica               -0.001685       0.021946    -0.007002  \n",
       "MESTRADO                           -0.009390      -0.307744     0.894522  \n",
       "MESTRADO PROFISSIONAL               1.000000       0.110330     0.012615  \n",
       "PÓS-DOUTORADO                       0.110330       1.000000    -0.214852  \n",
       "Total Linha                         0.012615      -0.214852     1.000000  "
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlacao = data.drop(['Ano'], axis=1).corr() #verificando co-relação entre as variáveis\n",
    "correlacao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "id": "ecb7cedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.drop(['Ano'], axis=1).corr(), xticklabels=correlacao.columns, yticklabels=correlacao.columns, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34d3d2a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}