{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02 - Análise de Dados\n",
    "\n",
    "Este notebook busca consumir os dados gerados das notas obtidas nas graduações e na pós-graduação para gerar os gráficos disponibilizados na página."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constantes e sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRADES_COLOR_MAP = {\n",
    "    'A': '#60D394',\n",
    "    'B': '#AAF683',\n",
    "    'C': '#FFD97D',\n",
    "    'D': '#FF9B85',\n",
    "    'F': '#EE6055' \n",
    "}\n",
    "\n",
    "# HEATMAP_PALETTE = 'RdYlGn'\n",
    "# HEATMAP_PALETTE = 'RdBu'\n",
    "# HEATMAP_PALETTE = 'Reds'\n",
    "# HEATMAP_PALETTE = 'Blues'\n",
    "HEATMAP_PALETTE = 'Teal'\n",
    "# HEATMAP_PALETTE = 'Magma_r'\n",
    "# HEATMAP_PALETTE = 'OrRd'\n",
    "\n",
    "BLUE =  '#73A1B2'\n",
    "GREEN = '#6E8658'\n",
    "BROWN = '#57473A'\n",
    "\n",
    "SUBPLOT_TITLES = ['Meus conceitos<br>&nbsp;', 'Projeção estimada<br>pela média', 'Projeção estimada<br>pela moda']\n",
    "\n",
    "# RENDERER = None  # Interativo\n",
    "RENDERER = 'png'  # Para ter preview no GitHub/Nbviewer\n",
    "\n",
    "# template = 'plotly'  # Padrão\n",
    "template = 'plotly_white'  # Fundo branco. Melhor para embarcar no html?\n",
    "\n",
    "pio.templates.default = template \n",
    "pio.templates[template].layout.font.family = 'Helvetica, sans-serif'  # Para usar a mesma fonte da página html\n",
    "pio.templates[template].layout.font.color = 'rgb(99, 99, 99)'  # Mesma cor do HTML\n",
    "\n",
    "# pd.options.mode.chained_assignment = 'warn'\n",
    "pd.options.mode.chained_assignment = None  # Para remover warnings desnecessários"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise dos dados da UFABC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leitura dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como primeiro passo, vamos fazer a leitura da base de dados das minhas notas (obtidas pela consulta ao Sigaa) e depois para as notas médias dos alunos que seguiram a mesma trajetória."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Ano</th>\n",
       "      <th>Código</th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Situação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIL0304-15</td>\n",
       "      <td>EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA</td>\n",
       "      <td>C</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BCS0001-15</td>\n",
       "      <td>BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIS0005-15</td>\n",
       "      <td>BASES COMPUTACIONAIS DA CIÊNCIA</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIK0102-15</td>\n",
       "      <td>ESTRUTURA DA MATÉRIA</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIS0003-15</td>\n",
       "      <td>BASES MATEMÁTICAS</td>\n",
       "      <td>C</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2023.2</td>\n",
       "      <td>ESTA017-17</td>\n",
       "      <td>LABORATÓRIO DE MÁQUINAS ELÉTRICAS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2023.2</td>\n",
       "      <td>ESTA011-17</td>\n",
       "      <td>AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2023.3</td>\n",
       "      <td>ESTA904-17</td>\n",
       "      <td>TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2023.3</td>\n",
       "      <td>ESTA022-17</td>\n",
       "      <td>TEORIA DE ACIONAMENTOS ELÉTRICOS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2023.3</td>\n",
       "      <td>ESTA008-17</td>\n",
       "      <td>SISTEMAS DE CONTROLE II</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ano      Código                                         Disciplina  \\\n",
       "0   2017.2  BIL0304-15         EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA   \n",
       "1   2017.2  BCS0001-15            BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS   \n",
       "2   2017.2  BIS0005-15                    BASES COMPUTACIONAIS DA CIÊNCIA   \n",
       "3   2017.2  BIK0102-15                               ESTRUTURA DA MATÉRIA   \n",
       "4   2017.2  BIS0003-15                                  BASES MATEMÁTICAS   \n",
       "..     ...         ...                                                ...   \n",
       "73  2023.2  ESTA017-17                  LABORATÓRIO DE MÁQUINAS ELÉTRICAS   \n",
       "74  2023.2  ESTA011-17                  AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS   \n",
       "75  2023.3  ESTA904-17  TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...   \n",
       "76  2023.3  ESTA022-17                   TEORIA DE ACIONAMENTOS ELÉTRICOS   \n",
       "77  2023.3  ESTA008-17                            SISTEMAS DE CONTROLE II   \n",
       "\n",
       "   Resultado  Situação  \n",
       "0          C  APROVADO  \n",
       "1          A  APROVADO  \n",
       "2          A  APROVADO  \n",
       "3          A  APROVADO  \n",
       "4          C  APROVADO  \n",
       "..       ...       ...  \n",
       "73         A  APROVADO  \n",
       "74         A  APROVADO  \n",
       "75         A  APROVADO  \n",
       "76         A  APROVADO  \n",
       "77         A  APROVADO  \n",
       "\n",
       "[78 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc = pd.read_csv('./data/notas-ufabc.csv', dtype={'Ano': str}, sep=';')\n",
    "df_ufabc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para enriquecermos nossa base de dados e podermos fazer análises referentes ao CR, precisamosadicionar informações como o número de créditos. Como esta informação não está disponível no Sigaa, vamos consultar uma base externa do [Catálogo de Disciplinas da UFABC](https://prograd.ufabc.edu.br/catalogos-de-disciplinas) (em específico a [edição de 2017](https://prograd.ufabc.edu.br/pdf/catalogo_disciplinas_graduacao_2017_2018_v2.xlsx)). Assim, vamos realizar a leitura dos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SIGLA</th>\n",
       "      <th>DISCIPLINA</th>\n",
       "      <th>TPI</th>\n",
       "      <th>RECOMENDAÇÃO</th>\n",
       "      <th>OBJETIVOS</th>\n",
       "      <th>EMENTA</th>\n",
       "      <th>BIBLIOGRAFIA BÁSICA</th>\n",
       "      <th>BIBLIOGRAFIA COMPLEMENTAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ESHR022-14</td>\n",
       "      <td>Abordagens Tradicionais das Relações Internaci...</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>Não há</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Contextualização histórica da emergência das t...</td>\n",
       "      <td>CARR, Edward Hallett. Vinte anos de crise 1919...</td>\n",
       "      <td>ARON, Raymond. Paz e Guerra entre as Nações. S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ESZM035-17</td>\n",
       "      <td>Aditivação de Polímeros</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>Síntese de Polímeros; Materiais Poliméricos</td>\n",
       "      <td>Adquirir habilidades sobre o entendimento dos ...</td>\n",
       "      <td>Tipos de aditivos e métodos para obtenção de f...</td>\n",
       "      <td>BART, J.C.J. Additives in Polymer: industrial ...</td>\n",
       "      <td>CANEVAROLO JR, S. V., Ciência dos Polímeros, A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ESZP041-14</td>\n",
       "      <td>Administração Pública e Reforma do Estado em P...</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>Não há</td>\n",
       "      <td>A disciplina visa apresentar aos alunos a vari...</td>\n",
       "      <td>Estado, política e administração pública; Cris...</td>\n",
       "      <td>BRESSER-PEREIRA, L. C. (1998). Reforma do esta...</td>\n",
       "      <td>ABRUCIO, Fernando Luiz; LOUREIRO, Maria Rita (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESTS016-17</td>\n",
       "      <td>Aerodinâmica I</td>\n",
       "      <td>4-0-5</td>\n",
       "      <td>Dinâmica de Gases</td>\n",
       "      <td>Familiarizar o aluno com a física associada à ...</td>\n",
       "      <td>Força de Sustenção e arrasto; Teoria do perfil...</td>\n",
       "      <td>ANDERSON, J. D. Fundamentals of Aerodynamics. ...</td>\n",
       "      <td>BARNARD, R. H. Road Vehicle Aerodynamic Design...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ESZS019-17</td>\n",
       "      <td>Aerodinâmica II</td>\n",
       "      <td>4-0-5</td>\n",
       "      <td>Aerodinâmica I</td>\n",
       "      <td>Familiarizar o aluno com a física de escoament...</td>\n",
       "      <td>Física do escoamento subsônico e hipersônico. ...</td>\n",
       "      <td>ANDERSON J. D. Hypersonic and High Temperature...</td>\n",
       "      <td>CHATTOT, J. J. Computational Aerodynamics and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>ESZA019-17</td>\n",
       "      <td>Visão Computacional</td>\n",
       "      <td>3-1-4</td>\n",
       "      <td>Fundamentos de Robótica</td>\n",
       "      <td>Compreender como se realizam diversas possibil...</td>\n",
       "      <td>Formação da imagem; extração de atributos; vis...</td>\n",
       "      <td>BORENSTEIN, J.; EVERETT, H. R.; FENG, Liqang; ...</td>\n",
       "      <td>JONES, Joseph L. Mobile Robots - Inspiration t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>MCZA031-13</td>\n",
       "      <td>Web Semântica</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>Inteligência Artificial</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Introdução à Web Semântica (WS). Linguagens pa...</td>\n",
       "      <td>HITZLER, P., KRÖTZSCH, M., RUDOLPH, S. Foundat...</td>\n",
       "      <td>ANTONIOU, G.; GROTH, P.; VAN HARMELEN, F.; HOE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>NHT1063-15</td>\n",
       "      <td>Zoologia de Invertebrados I</td>\n",
       "      <td>2-4-3</td>\n",
       "      <td>Sistemática e Biogeografia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fundamentos de sistemática; Origem de Metazoa ...</td>\n",
       "      <td>BRUSCA, Richard C.; BRUSCA, Gary J. Invertebra...</td>\n",
       "      <td>AMORIM, Dalton de Souza. Fundamentos de sistem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>NHT1064-15</td>\n",
       "      <td>Zoologia de Invertebrados II</td>\n",
       "      <td>2-4-3</td>\n",
       "      <td>Sistemática e Biogeografia; Zoologia de Invert...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Plano-básico de Deuterostomia; Filogenia de Ec...</td>\n",
       "      <td>BRUSCA, Richard C.; BRUSCA, Gary J. Invertebra...</td>\n",
       "      <td>AMORIM, Dalton de Souza. Fundamentos de sistem...</td>\n",
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       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>NHT1065-15</td>\n",
       "      <td>Zoologia de Vertebrados</td>\n",
       "      <td>4-2-3</td>\n",
       "      <td>Sistemática e Biogeografia; Zoologia de Invert...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Filogenia de Chordata (Urochordata, Cephalocho...</td>\n",
       "      <td>BRUSCA, Richard C.; BRUSCA, Gary J. Invertebra...</td>\n",
       "      <td>AMORIM, Dalton de Souza. Fundamentos de sistem...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1188 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "           SIGLA                                         DISCIPLINA    TPI  \\\n",
       "0     ESHR022-14  Abordagens Tradicionais das Relações Internaci...  4-0-4   \n",
       "1     ESZM035-17                            Aditivação de Polímeros  4-0-4   \n",
       "2     ESZP041-14  Administração Pública e Reforma do Estado em P...  4-0-4   \n",
       "3     ESTS016-17                                     Aerodinâmica I  4-0-5   \n",
       "4     ESZS019-17                                    Aerodinâmica II  4-0-5   \n",
       "...          ...                                                ...    ...   \n",
       "1183  ESZA019-17                                Visão Computacional  3-1-4   \n",
       "1184  MCZA031-13                                      Web Semântica  4-0-4   \n",
       "1185  NHT1063-15                        Zoologia de Invertebrados I  2-4-3   \n",
       "1186  NHT1064-15                       Zoologia de Invertebrados II  2-4-3   \n",
       "1187  NHT1065-15                            Zoologia de Vertebrados  4-2-3   \n",
       "\n",
       "                                           RECOMENDAÇÃO  \\\n",
       "0                                                Não há   \n",
       "1           Síntese de Polímeros; Materiais Poliméricos   \n",
       "2                                                Não há   \n",
       "3                                     Dinâmica de Gases   \n",
       "4                                        Aerodinâmica I   \n",
       "...                                                 ...   \n",
       "1183                            Fundamentos de Robótica   \n",
       "1184                            Inteligência Artificial   \n",
       "1185                         Sistemática e Biogeografia   \n",
       "1186  Sistemática e Biogeografia; Zoologia de Invert...   \n",
       "1187  Sistemática e Biogeografia; Zoologia de Invert...   \n",
       "\n",
       "                                              OBJETIVOS  \\\n",
       "0                                                   NaN   \n",
       "1     Adquirir habilidades sobre o entendimento dos ...   \n",
       "2     A disciplina visa apresentar aos alunos a vari...   \n",
       "3     Familiarizar o aluno com a física associada à ...   \n",
       "4     Familiarizar o aluno com a física de escoament...   \n",
       "...                                                 ...   \n",
       "1183  Compreender como se realizam diversas possibil...   \n",
       "1184                                                NaN   \n",
       "1185                                                NaN   \n",
       "1186                                                NaN   \n",
       "1187                                                NaN   \n",
       "\n",
       "                                                 EMENTA  \\\n",
       "0     Contextualização histórica da emergência das t...   \n",
       "1     Tipos de aditivos e métodos para obtenção de f...   \n",
       "2     Estado, política e administração pública; Cris...   \n",
       "3     Força de Sustenção e arrasto; Teoria do perfil...   \n",
       "4     Física do escoamento subsônico e hipersônico. ...   \n",
       "...                                                 ...   \n",
       "1183  Formação da imagem; extração de atributos; vis...   \n",
       "1184  Introdução à Web Semântica (WS). Linguagens pa...   \n",
       "1185  Fundamentos de sistemática; Origem de Metazoa ...   \n",
       "1186  Plano-básico de Deuterostomia; Filogenia de Ec...   \n",
       "1187  Filogenia de Chordata (Urochordata, Cephalocho...   \n",
       "\n",
       "                                    BIBLIOGRAFIA BÁSICA  \\\n",
       "0     CARR, Edward Hallett. Vinte anos de crise 1919...   \n",
       "1     BART, J.C.J. Additives in Polymer: industrial ...   \n",
       "2     BRESSER-PEREIRA, L. C. (1998). Reforma do esta...   \n",
       "3     ANDERSON, J. D. Fundamentals of Aerodynamics. ...   \n",
       "4     ANDERSON J. D. Hypersonic and High Temperature...   \n",
       "...                                                 ...   \n",
       "1183  BORENSTEIN, J.; EVERETT, H. R.; FENG, Liqang; ...   \n",
       "1184  HITZLER, P., KRÖTZSCH, M., RUDOLPH, S. Foundat...   \n",
       "1185  BRUSCA, Richard C.; BRUSCA, Gary J. Invertebra...   \n",
       "1186  BRUSCA, Richard C.; BRUSCA, Gary J. Invertebra...   \n",
       "1187  BRUSCA, Richard C.; BRUSCA, Gary J. Invertebra...   \n",
       "\n",
       "                              BIBLIOGRAFIA COMPLEMENTAR  \n",
       "0     ARON, Raymond. Paz e Guerra entre as Nações. S...  \n",
       "1     CANEVAROLO JR, S. V., Ciência dos Polímeros, A...  \n",
       "2     ABRUCIO, Fernando Luiz; LOUREIRO, Maria Rita (...  \n",
       "3     BARNARD, R. H. Road Vehicle Aerodynamic Design...  \n",
       "4     CHATTOT, J. J. Computational Aerodynamics and ...  \n",
       "...                                                 ...  \n",
       "1183  JONES, Joseph L. Mobile Robots - Inspiration t...  \n",
       "1184  ANTONIOU, G.; GROTH, P.; VAN HARMELEN, F.; HOE...  \n",
       "1185  AMORIM, Dalton de Souza. Fundamentos de sistem...  \n",
       "1186  AMORIM, Dalton de Souza. Fundamentos de sistem...  \n",
       "1187  AMORIM, Dalton de Souza. Fundamentos de sistem...  \n",
       "\n",
       "[1188 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_creds = pd.read_excel('./data/catalogo_disciplinas_graduacao_2017_2018_v2.xlsx')\n",
    "df_ufabc_creds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para a nossa análise, só será necessário ter conhecimento da sigla e do TPI, informação essa que traz o número de créditos (soma de T e P)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>SIGLA</th>\n",
       "      <th>TPI</th>\n",
       "      <th>Créditos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ESHR022-14</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ESZM035-17</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ESZP041-14</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESTS016-17</td>\n",
       "      <td>4-0-5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ESZS019-17</td>\n",
       "      <td>4-0-5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>ESZA019-17</td>\n",
       "      <td>3-1-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>MCZA031-13</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>NHT1063-15</td>\n",
       "      <td>2-4-3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>NHT1064-15</td>\n",
       "      <td>2-4-3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>NHT1065-15</td>\n",
       "      <td>4-2-3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1188 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           SIGLA    TPI  Créditos\n",
       "0     ESHR022-14  4-0-4         4\n",
       "1     ESZM035-17  4-0-4         4\n",
       "2     ESZP041-14  4-0-4         4\n",
       "3     ESTS016-17  4-0-5         4\n",
       "4     ESZS019-17  4-0-5         4\n",
       "...          ...    ...       ...\n",
       "1183  ESZA019-17  3-1-4         4\n",
       "1184  MCZA031-13  4-0-4         4\n",
       "1185  NHT1063-15  2-4-3         6\n",
       "1186  NHT1064-15  2-4-3         6\n",
       "1187  NHT1065-15  4-2-3         6\n",
       "\n",
       "[1188 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_creds = df_ufabc_creds[['SIGLA', 'TPI']]\n",
    "df_ufabc_creds['Créditos'] = df_ufabc_creds['TPI'].apply(lambda s: int(s.split('-')[0]) + int(s.split('-')[1]))\n",
    "df_ufabc_creds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feito isso, podemos juntar as bases e ver se há alguma matéria que não teve correspondência."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ano</th>\n",
       "      <th>Código</th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Situação</th>\n",
       "      <th>TPI</th>\n",
       "      <th>Créditos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIL0304-15</td>\n",
       "      <td>EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA</td>\n",
       "      <td>C</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>3-0-4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BCS0001-15</td>\n",
       "      <td>BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>0-3-2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017.2</td>\n",
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       "      <td>0-2-2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIK0102-15</td>\n",
       "      <td>ESTRUTURA DA MATÉRIA</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>3-0-4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017.2</td>\n",
       "      <td>BIS0003-15</td>\n",
       "      <td>BASES MATEMÁTICAS</td>\n",
       "      <td>C</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>4-0-5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2023.2</td>\n",
       "      <td>ESTA017-17</td>\n",
       "      <td>LABORATÓRIO DE MÁQUINAS ELÉTRICAS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>0-2-4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2023.2</td>\n",
       "      <td>ESTA011-17</td>\n",
       "      <td>AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>1-3-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2023.3</td>\n",
       "      <td>ESTA904-17</td>\n",
       "      <td>TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>0-2-4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2023.3</td>\n",
       "      <td>ESTA022-17</td>\n",
       "      <td>TEORIA DE ACIONAMENTOS ELÉTRICOS</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>4-0-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2023.3</td>\n",
       "      <td>ESTA008-17</td>\n",
       "      <td>SISTEMAS DE CONTROLE II</td>\n",
       "      <td>A</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>3-2-4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ano      Código                                         Disciplina  \\\n",
       "0   2017.2  BIL0304-15         EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA   \n",
       "1   2017.2  BCS0001-15            BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS   \n",
       "2   2017.2  BIS0005-15                    BASES COMPUTACIONAIS DA CIÊNCIA   \n",
       "3   2017.2  BIK0102-15                               ESTRUTURA DA MATÉRIA   \n",
       "4   2017.2  BIS0003-15                                  BASES MATEMÁTICAS   \n",
       "..     ...         ...                                                ...   \n",
       "73  2023.2  ESTA017-17                  LABORATÓRIO DE MÁQUINAS ELÉTRICAS   \n",
       "74  2023.2  ESTA011-17                  AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS   \n",
       "75  2023.3  ESTA904-17  TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...   \n",
       "76  2023.3  ESTA022-17                   TEORIA DE ACIONAMENTOS ELÉTRICOS   \n",
       "77  2023.3  ESTA008-17                            SISTEMAS DE CONTROLE II   \n",
       "\n",
       "   Resultado  Situação    TPI  Créditos  \n",
       "0          C  APROVADO  3-0-4         3  \n",
       "1          A  APROVADO  0-3-2         3  \n",
       "2          A  APROVADO  0-2-2         2  \n",
       "3          A  APROVADO  3-0-4         3  \n",
       "4          C  APROVADO  4-0-5         4  \n",
       "..       ...       ...    ...       ...  \n",
       "73         A  APROVADO  0-2-4         2  \n",
       "74         A  APROVADO  1-3-4         4  \n",
       "75         A  APROVADO  0-2-4         2  \n",
       "76         A  APROVADO  4-0-4         4  \n",
       "77         A  APROVADO  3-2-4         5  \n",
       "\n",
       "[78 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc = df_ufabc.join(df_ufabc_creds.set_index('SIGLA'), on='Código', how='left')\n",
    "df_ufabc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Ano</th>\n",
       "      <th>Código</th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Situação</th>\n",
       "      <th>TPI</th>\n",
       "      <th>Créditos</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Ano, Código, Disciplina, Resultado, Situação, TPI, Créditos]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc[df_ufabc.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feito isso, para conseguirmos comparar o desempenho com base nos valores médios dos demais alunos, vamos fazer a leitura da base de dados obtida pelo scraping do next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>Nota provável</th>\n",
       "      <th>Conceito provável</th>\n",
       "      <th>Conceito moda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>17.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.929000</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS</td>\n",
       "      <td>84.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.846000</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BASES COMPUTACIONAIS DA CIÊNCIA</td>\n",
       "      <td>39.2</td>\n",
       "      <td>26.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2.813000</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESTRUTURA DA MATÉRIA</td>\n",
       "      <td>17.5</td>\n",
       "      <td>29.5</td>\n",
       "      <td>31.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.314685</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BASES MATEMÁTICAS</td>\n",
       "      <td>8.7</td>\n",
       "      <td>15.4</td>\n",
       "      <td>25.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1.482482</td>\n",
       "      <td>D</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS</td>\n",
       "      <td>36.4</td>\n",
       "      <td>35.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.965000</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>3.201000</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>TEORIA DE ACIONAMENTOS ELÉTRICOS</td>\n",
       "      <td>26.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>22.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.493493</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>SISTEMAS DE CONTROLE II</td>\n",
       "      <td>6.4</td>\n",
       "      <td>32.1</td>\n",
       "      <td>34.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.062937</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>INSTRUMENTAÇÃO E METROLOGIA ÓPTICA</td>\n",
       "      <td>31.9</td>\n",
       "      <td>25.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1.925000</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Disciplina     A     B     C     D  \\\n",
       "0          EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA  31.0  43.1  17.4   4.8   \n",
       "1             BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS  84.6  15.4   0.0   0.0   \n",
       "2                     BASES COMPUTACIONAIS DA CIÊNCIA  39.2  26.9  18.3   7.2   \n",
       "3                                ESTRUTURA DA MATÉRIA  17.5  29.5  31.4  10.4   \n",
       "4                                   BASES MATEMÁTICAS   8.7  15.4  25.8  15.5   \n",
       "..                                                ...   ...   ...   ...   ...   \n",
       "73                  AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS  36.4  35.9  19.0   5.2   \n",
       "74  TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...  60.0  26.7   0.0   0.0   \n",
       "75                   TEORIA DE ACIONAMENTOS ELÉTRICOS  26.1  30.4  22.1   9.3   \n",
       "76                            SISTEMAS DE CONTROLE II   6.4  32.1  34.6  15.4   \n",
       "77                 INSTRUMENTAÇÃO E METROLOGIA ÓPTICA  31.9  25.9  30.4   4.4   \n",
       "\n",
       "       F  Nota provável Conceito provável Conceito moda  \n",
       "0    3.7       2.929000                 B             B  \n",
       "1    0.0       3.846000                 A             A  \n",
       "2    8.4       2.813000                 B             A  \n",
       "3   11.3       2.314685                 C             C  \n",
       "4   34.5       1.482482                 D             F  \n",
       "..   ...            ...               ...           ...  \n",
       "73   3.5       2.965000                 B             A  \n",
       "74  13.3       3.201000                 A             A  \n",
       "75  12.0       2.493493                 B             B  \n",
       "76  11.6       2.062937                 C             C  \n",
       "77   7.4       1.925000                 C             A  \n",
       "\n",
       "[78 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_next = pd.read_csv('./data/notas-next.csv', dtype={'Ano': str}, sep=';')\n",
    "df_next"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para facilitar, vamos cruzar com a base de dados do Sigaa para obter os códigos e o CR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Disciplina</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "      <th>Nota provável</th>\n",
       "      <th>Conceito provável</th>\n",
       "      <th>Conceito moda</th>\n",
       "      <th>Código</th>\n",
       "      <th>Créditos</th>\n",
       "      <th>Ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>17.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.929000</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>BIL0304-15</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS</td>\n",
       "      <td>84.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.846000</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>BCS0001-15</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BASES COMPUTACIONAIS DA CIÊNCIA</td>\n",
       "      <td>39.2</td>\n",
       "      <td>26.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2.813000</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>BIS0005-15</td>\n",
       "      <td>2</td>\n",
       "      <td>2017.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESTRUTURA DA MATÉRIA</td>\n",
       "      <td>17.5</td>\n",
       "      <td>29.5</td>\n",
       "      <td>31.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.314685</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>BIK0102-15</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BASES MATEMÁTICAS</td>\n",
       "      <td>8.7</td>\n",
       "      <td>15.4</td>\n",
       "      <td>25.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1.482482</td>\n",
       "      <td>D</td>\n",
       "      <td>F</td>\n",
       "      <td>BIS0003-15</td>\n",
       "      <td>4</td>\n",
       "      <td>2017.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS</td>\n",
       "      <td>36.4</td>\n",
       "      <td>35.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.965000</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>ESTA011-17</td>\n",
       "      <td>4</td>\n",
       "      <td>2023.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>3.201000</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>ESTA904-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>TEORIA DE ACIONAMENTOS ELÉTRICOS</td>\n",
       "      <td>26.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>22.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.493493</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>ESTA022-17</td>\n",
       "      <td>4</td>\n",
       "      <td>2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>SISTEMAS DE CONTROLE II</td>\n",
       "      <td>6.4</td>\n",
       "      <td>32.1</td>\n",
       "      <td>34.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.062937</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>ESTA008-17</td>\n",
       "      <td>5</td>\n",
       "      <td>2023.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>INSTRUMENTAÇÃO E METROLOGIA ÓPTICA</td>\n",
       "      <td>31.9</td>\n",
       "      <td>25.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1.925000</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>ESZA013-17</td>\n",
       "      <td>4</td>\n",
       "      <td>2022.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Disciplina     A     B     C     D  \\\n",
       "0          EVOLUÇÃO E DIVERSIFICAÇÃO DA VIDA NA TERRA  31.0  43.1  17.4   4.8   \n",
       "1             BASE EXPERIMENTAL DAS CIÊNCIAS NATURAIS  84.6  15.4   0.0   0.0   \n",
       "2                     BASES COMPUTACIONAIS DA CIÊNCIA  39.2  26.9  18.3   7.2   \n",
       "3                                ESTRUTURA DA MATÉRIA  17.5  29.5  31.4  10.4   \n",
       "4                                   BASES MATEMÁTICAS   8.7  15.4  25.8  15.5   \n",
       "..                                                ...   ...   ...   ...   ...   \n",
       "73                  AUTOMAÇÃO DE SISTEMAS INDUSTRIAIS  36.4  35.9  19.0   5.2   \n",
       "74  TRABALHO DE GRADUAÇÃO III EM ENGENHARIA DE INS...  60.0  26.7   0.0   0.0   \n",
       "75                   TEORIA DE ACIONAMENTOS ELÉTRICOS  26.1  30.4  22.1   9.3   \n",
       "76                            SISTEMAS DE CONTROLE II   6.4  32.1  34.6  15.4   \n",
       "77                 INSTRUMENTAÇÃO E METROLOGIA ÓPTICA  31.9  25.9  30.4   4.4   \n",
       "\n",
       "       F  Nota provável Conceito provável Conceito moda      Código  Créditos  \\\n",
       "0    3.7       2.929000                 B             B  BIL0304-15         3   \n",
       "1    0.0       3.846000                 A             A  BCS0001-15         3   \n",
       "2    8.4       2.813000                 B             A  BIS0005-15         2   \n",
       "3   11.3       2.314685                 C             C  BIK0102-15         3   \n",
       "4   34.5       1.482482                 D             F  BIS0003-15         4   \n",
       "..   ...            ...               ...           ...         ...       ...   \n",
       "73   3.5       2.965000                 B             A  ESTA011-17         4   \n",
       "74  13.3       3.201000                 A             A  ESTA904-17         2   \n",
       "75  12.0       2.493493                 B             B  ESTA022-17         4   \n",
       "76  11.6       2.062937                 C             C  ESTA008-17         5   \n",
       "77   7.4       1.925000                 C             A  ESZA013-17         4   \n",
       "\n",
       "       Ano  \n",
       "0   2017.2  \n",
       "1   2017.2  \n",
       "2   2017.2  \n",
       "3   2017.2  \n",
       "4   2017.2  \n",
       "..     ...  \n",
       "73  2023.2  \n",
       "74  2023.3  \n",
       "75  2023.3  \n",
       "76  2023.3  \n",
       "77  2022.3  \n",
       "\n",
       "[78 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_next = df_next.join(df_ufabc.set_index('Disciplina')[['Código', 'Créditos', 'Ano']], on='Disciplina', how='left')\n",
    "df_next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>Nota provável</th>\n",
       "      <th>Conceito provável</th>\n",
       "      <th>Conceito moda</th>\n",
       "      <th>Código</th>\n",
       "      <th>Créditos</th>\n",
       "      <th>Ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Disciplina, A, B, C, D, F, Nota provável, Conceito provável, Conceito moda, Código, Créditos, Ano]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_next[df_next.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparação da proporções dos conceitos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como primeiro passo, vamos verificar a proporção de notas e fazer uma comparação com aquelas verificadas no next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado\n",
      "A    0.717949\n",
      "B    0.243590\n",
      "C    0.038462\n",
      "D    0.000000\n",
      "F    0.000000\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Conceito provável\n",
      "A    0.179487\n",
      "B    0.371795\n",
      "C    0.346154\n",
      "D    0.102564\n",
      "F    0.000000\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Conceito moda\n",
      "A    0.423077\n",
      "B    0.179487\n",
      "C    0.217949\n",
      "D    0.012821\n",
      "F    0.166667\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ufabc_grade_prop = df_ufabc['Resultado'].value_counts(normalize=True).reindex(['A', 'B', 'C', 'D', 'F'], fill_value=0.0)  # Para garantir que tenha todas as notas\n",
    "next_prob_grade_prop = df_next['Conceito provável'].value_counts(normalize=True).reindex(['A', 'B', 'C', 'D', 'F'], fill_value=0.0)\n",
    "next_mode_grade_prop = df_next['Conceito moda'].value_counts(normalize=True).reindex(['A', 'B', 'C', 'D', 'F'], fill_value=0.0)\n",
    "\n",
    "print(ufabc_grade_prop, end='\\n\\n')\n",
    "print(next_prob_grade_prop, end='\\n\\n')\n",
    "print(next_mode_grade_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos fazer esta comparação tanto por meio de gráfico de barras, como por meio de gráfico de pizza:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ahg4d6oIqBVz6Vf+tW7fOHnvsMRs2bFhSsARYSTGxEwIIIIAAAggggAACCCCAAAIIIFChQNAB1j//+U+7++673X/VqlWzLVu22N/+9jc744wz7K677rL8/HwbOHCgQ3rggQdszZo11rVrV1u4cKENGTLEXn/9dZs9e7YNHz7cJkyYYD179rTGjRtXiKodCLCSYmInBBBAAAEEEEAAAQQQQAABBBBAoEKBoAMshU/z58+3pk2buhFXGl115plnuqmE11xzjXXq1MnOOusshzRz5kybPn263XrrrS68Ovvss23RokVu9FWzZs3czwcNGlQhaLQDAVbSVOyIAAIIIIAAAggggAACCCCAAAII7Fcg6ADr0Ucftb/+9a9u1NRJJ51kL774otWvX9+NqOrfv79deOGF1rFjRweksOq+++6zyZMn28qVK23OnDnWqlUr69Chg40ZM8b69evnRnEtXbrUTTusXbu2+9y2HcV7Aefnmd2xbrr9a+uHWXn6NTvoSBvY6AdWw6plZf2pNAII7FugemG+FRbkwZMgsLvEbPvOva/jICGAAAKZKFCzeoHp35ls/xUoKi6xnUW7IUEAAQSyQqBWjYKsqGemVjLoAOvhhx+2V1991U0XrFmzpq1evdpuvvlmGz9+vF133XVulNW5557r+kbrY0UjsBI7S4HV4sWLrXPnzi7IatKkiZtqqDLq1Klj27YX7R1g5edlf4B19HkEWJn6t5Z6IVBFgerVCgiwyti5AGvH3tfxKhLzMQQQQMCrQM0ahQRYZYRdgLWLBxFeTzwKRwCBlAnUqlmYsrJysaCgA6znnnvOXnjhBbcGlja9kVALtN9000127733uqmBffv2dT/TGwbXr19vgwcPLj0PSkpK3CLwmlI4depUN3pLgdf9999vjRo1sm7dupV7zjCFMBf/OtFmBBBAAAEEEEAAAQQQQAABBBDwIRB0gLV27Vq78cYbrXfv3ta+fXt78skn7c0333RvH5wxY4YbcTV69GjbsGGD3Xnnnda9e/fSNbGErfWzNNqqV69eNmvWLFu+fLkra+LEidajRw9r06YNAZaPs5IyEUAAAQQQQAABBBBAAAEEEEAAgQSBoAMstXPatGkurNKmtw7+8pe/dMFTUVGRjR071latWuV+1qJFCzc6S/toKy4uthEjRrj1smrVqmVbt251bypctmyZtWvXzvr06VO6777OKEZg8fcMAQQQQAABBBBAAAEEEEAAAQQQSI2AtwDryy+/tKeeesreffdd++qrr6xevXr23e9+177zne/sN/hJTbP2LEXh06ZNm9wUQC3Enrhp9FVBQYHVrVt3jz/ftWuXG5nVoEGDKlWJAKtKbHwIAQQQQAABBBBAAAEEEEAAAQQQ2EvAS4C1fft2N5ppx44dbtHzgw8+2N577z03iunUU0+1yy67LPiuIMAKvotpIAIIIIAAAggggAACCCCAAAIIxCTgJcDSwulPP/20W19KI6+ibdGiRXbfffe5Pz/yyCNjamJ6DkOAlR53jooAAggggAACCCCAAAIIIIAAAuEJeAmwHnroIfvkk09s2LBhe4jt3r3b+vXrZ/3797e2bduGp5nQIgKsoLuXxiGAAAIIIIAAAggggAACCCCAQIwCXgIsLZo+e/Zs97a/gw46qLQ5CxYssMmTJ9sNN9xgjRo1irGZ8R+KACt+c46IAAIIIIAAAggggAACCCCAAAJhCngJsD7//HO76qqr3ILpJ510kjVt2tRWrlxpS5YssZYtW9qQIUPC1ExoFQFW8F1MAxFAAAEEEEAAAQQQQAABBBBAICYBLwGW6r5u3Tp75JFH3OLt0XbCCSdY375993rjX0xtjfUwBFixcnMwBBBAAAEEEEAAAQQQQAABBBAIWMBbgBWZlZSUuLcP1q5d2/Ly8mze7EBiRwAAIABJREFUvHl28sknu9+HvBFghdy7tA0BBBBAAAEEEEAAAQQQQAABBOIUSFmAVVRUZJ9++qk1aNDA1X/jxo1uHayvvvqqtD1axH3hwoVucfcWLVrE2c7Yj0WAFTs5B0QAAQQQQAABBBBAAAEEEEAAgUAFUhZgbdmyxa6++mq75JJLrGHDhjZ8+HArLCw0BVv6taCgwHbs2GHHHnusXXnlle7PQt4IsELuXdqGAAIIIIAAAggggAACCCCAAAJxCqQswFKlJ02aZIcddpgdeeSRNm3aNBs/frw9/vjj9tlnn9mgQYPsmWeecSOwxowZ46YThrwRYIXcu7QNAQQQQAABBBBAAAEEEEAAAQTiFEhpgNW/f3+7/PLL3dsG3333XbvuuuvslVdesZkzZ9q4cePcCKwBAwbYyJEj3SitkDcCrJB7l7YhgAACCCCAAAIIIIAAAggggECcAikNsIYMGeKmECqouueee1yApXWvRo0aZTfddJPVrFnThg4datqvVatWcbYz9mMRYMVOzgERQAABBBBAAAEEEEAAAQQQQCBQgZQGWKtXr7Y1a9ZYu3bt7KqrrrLNmzfb5MmT3ZpXmzZtcoRa+0rhVn5+fqCk/2kWAVbQ3UvjEEAAAQQQQAABBBBAAAEEEEAgRoGUBliJ9d65c6cp0Dr++OPt888/t5deesm9kfDMM890a2SFvhFghd7DtA8BBBBAAAEEEEAAAQQQQAABBOIS8BZgaeH21157zU477TSrV6+eLV682L2B8IgjjoirbWk9DgFWWvk5OAIIIIAAAggggAACCCCAAAIIBCTgJcDasmWLXXHFFW79qxEjRljjxo3t3nvvtTfeeMPOPvts69GjR0CE+24KAVbwXUwDEUAAAQQQQAABBBBAAAEEEEAgJgEvAdarr75qDz/8sN1yyy1u9JW2kpISmz59uj3//PN22223ubWxjj76aOvZs2dMTY33MARY8XpzNAQQQAABBBBAAAEEEEAAAQQQCFfAS4Cl8GrVqlV244037iGnkVmDBw+2hg0b2vbt292aWDfccEOQ0woJsML9S0PLEEAAAQQQQAABBBBAAAEEEEAgXgEvAdacOXPs8ccfd28iPO6440pbNG3aNJsxY4Z16NDBjcx699137ac//ak1atQo3lbHcDQCrBiQOQQCCCCAAAIIIIAAAggggAACCOSEgJcAa9euXTZ8+HDbtGmTHXrooe6tg++//77t2LHDWrVqZUOGDHG4K1assBYtWlheXl5w2ARYwXUpDUIAAQQQQAABBBBAAAEEEEAAgTQJeAmw1JadO3faCy+8YG+++aZ9/vnnVrduXWvTpo117drVCgoK0tTc+A5LgBWfNUdCAAEEEEAAAQQQQAABBBBAAIGwBbwEWPPmzbO33nrL+vfvH7beflpHgJWzXU/DEUAAAQQQQAABBBBAAAEEEEAgxQJeAqzFixfbpEmT7Oqrr7bmzZunuMrZURwBVnb0E7VEAAEEEEAAAQQQQAABBBBAAIHMF/ASYG3cuNFGjhzpWt+lSxc76qij9pA45ZRTrLCwMPN1DqCGBFgHgMdHEUAAAQQQQAABBBBAAAEEEEAAgQQBLwHWiy++aFOmTCkX+vbbb7dDDjkk6I4gwAq6e2kcAggggAACCCCAAAIIIIAAAgjEKOAlwCouLrbt27eX24zatWvH2MT0HIoAKz3uHBUBBBBAAAEEEEAAAQQQQAABBMIT8BJgiamkpMQWLlxo7777rm3bts2OPPJIa9++vfs1FzYCrFzoZdqIAAIIIIAAAggggAACCCCAAAJxCHgJsBRejRkzxt577z3XBq13VVRU5P7/ggsusM6dO8fRtrQegwArrfwcHAEEEEAAAQQQQAABBBBAAAEEAhLwEmC99tpr9uCDD1r//v1NC7bn5eXZpk2b7LnnnrN58+bZbbfdZnXq1AmIce+mEGAF3b00DgEEEEAAAQQQQAABBBBAAAEEYhTwEmA98sgjtnbtWrv22mv3aIrWxrrsssvs8ssvtxNPPDHGZsZ/KAKs+M05IgIIIIAAAggggAACCCCAAAIIhCngJcCaOnWqLVq0yMaOHetGX0XbypUr7ZZbbrFf//rX9o1vfCNM0f9rFQFW0N1L4xBAAAEEEEAAAQQQQAABBBBAIEYBLwHW6tWr7eabb7ZWrVrZ6aefbo0bN3brYT3//POuaQqxQt8IsELvYdqHAAIIIIAAAggggAACCCCAAAJxCXgJsFR5rYP16KOPli7erj+rUaOGmz54/PHHx9W+tB2HACtt9BwYAQQQQAABBBBAAAEEEEAAAQQCE/AWYMlpx44dbi2szz77zOrWrWtNmjSxatWqBUa47+YQYOVEN9NIBBBAAAEEEEAAAQQQQAABBBCIQcBbgPXxxx+b1sLS1MHt27db/fr17Vvf+padc845VlBQEEPT0nsIAqz0+nN0BBBAAAEEEEAAAQQQQAABBBAIR8BLgKW3DQ4ePNiNwDrhhBOsdu3atnz5ctu8ebNbF2vIkCHhCJbTEgKs4LuYBiKAAAIIIIAAAggggAACCCCAQEwCXgKsl156yR5//HG3WPvhhx9e2pTZs2fbU089ZePHj7fDDjsspiam5zAEWOlx56gIIIAAAggggAACCCCAAAIIIBCegJcAS1MHly5daiNHjtxDTCOyBgwY4EZnffOb3wxPM6FFBFhBdy+NQwABBBBAAAEEEEAAAQQQQACBGAW8BFirVq2yUaNG2ZVXXrnHGwfnzp1rv//97+3uu+92byQMeSPACrl3aRsCCCCAAAIIIIAAAggggAACCMQp4CXA+ve//20TJkyw3bt3u7cPHnHEEbZx40b3NkIFV40bNy5t47Bhw+Jsb2zHIsCKjZoDIYAAAggggAACCCCAAAIIIIBA4AJeAqy3337bnnvuuaToNEorxI0AK8RepU0IIIAAAggggAACCCCAAAIIIJAOAS8BVmJDtO7VV199ZYceeqjl5eWlo41pOSYBVlrYOSgCCCCAAAIIIIAAAggggAACCAQo4C3Aeuutt+zBBx+0L774opTtlFNOsQsuuMCFWaFvBFih9zDtQwABBBBAAAEEEEAAAQQQQACBuAS8BFiffPKJXXPNNXbUUUfZaaedZrVr17Z33nnH/vGPf7j1sPR2woKCgrjamJbjEGClhZ2DIoAAAggggAACCCCAAAIIIIBAgAJeAqxnnnnGXnnlFbeQe2FhYSnbhx9+aCNGjHD/JS7kHqCrEWCF2Ku0CQEEEEAAAQQQQAABBBBAAAEE0iHgJcB6+OGH7eOPP7aybxjUelgDBgywSy+91I3MCnkjwAq5d2kbAggggAACCCCAAAIIIIAAAgjEKeAlwHrppZfs8ccft2uvvXaPkVbTpk2zGTNmuJFZoa+DRYAV52nMsRBAAAEEEEAAAQQQQAABBBBAIGQBLwFWUVGRmyaotbAOOeQQO+aYY2zVqlXubYSdOnWyiy66KGRT1zYCrOC7mAYigAACCCCAAAIIIIAAAggggEBMAl4CLNV9165d9sILL9jSpUvdmwg14urb3/62de7cOfgF3AmwYjp7OQwCCCCAAAIIIIAAAggggAACCOSEgJcAa968efbWW29Z//79cwJxX41kBFbOdj0NRwABBBBAAAEEEEAAAQQQQACBFAt4CbAWL15skyZNsquvvtqaN2+e4ipnR3EEWNnRT9QSAQQQQAABBBBAAAEEEEAAAQQyX8BLgLVx40YbOXKka32XLl3sqKOO2kPilFNOscLCwszXOYAaEmAdAB4fRQABBBBAAAEEEEAAAQQQQAABBBIEvARYL774ok2ZMqVc6Ntvv90t7h7yRoAVcu/SNgQQQAABBBBAAAEEEEAAAQQQiFPAS4BVXFxs27dvL7cdtWvXjrONaTkWAVZa2DkoAggggAACCCCAAAIIIIAAAggEKJDyAEvrX7399tt29NFHW7t27SwXwqp9nRcEWAH+baFJCCCAAAIIIIAAAggggAACCCCQFoGUBlgzZ860p59+eo+GDB061Fq2bJmWxqXzoARY6dTn2AgggAACCCCAAAIIIIAAAgggEJJASgMshVUNGjSwAQMG2OrVq23ixInWpEkTu/LKK0MyS6otBFhJMbETAggggAACCCCAAAIIIIAAAgggUKFAygKszz//3H7zm9+48KpNmzbuwJMmTbKVK1farbfeWmFFQtuBACu0HqU9CCCAAAIIIIAAAggggAACCCCQLoGUBVgbNmywq666yq655ho79thjXXumTZtm8+bNswkTJqSrfWk7LgFW2ug5MAIIIIAAAggggECOC2wu+sq27t6RtQo186rZYdXCf/FV1nYQFUcAgbQIEGB5YifA8gRLsQgggAACCCCAAAIIVCCwcttHNmntn21XSXFWWl3QoKN9+9DjsrLuVBoBBBDwJZDyAKt9+/ZWr149V98333zTPvjgA+vWrdse9dfvq1ev7qtNGVEuAVZGdAOVQAABBBBAAAEEEMhBgXe2rbdbVz2btQFWv0ZnWPvDcu9FWDl4qtJkBBCohEDKA6xkjn377bfbIYccksyuWbsPAVbWdh0VRwABBBBAAAEEEMhyAQKsLO9Aqo8AAgjsQyBlAVZJSYnt2JHcPPOaNWvG3hkaCXbXXXe5Nbrq1Knjjr9kyRKbO3euFRcXm0aOnX766e7PN23aZAsWLLDmzZtby5b/ffLxxBNPWJcuXUpHmO2vEQRYsXcxB0QAAQQQQAABBBBAwAkQYHEiIIAAAuEJpCzAymSaoqIiGzZsmH3xxRfujYh169a1FStW2Lhx46xt27YupJoyZYpdfPHF1rp1axs6dKgLqhRw6Vf9t27dOnvsscdcOclsBFjJKLEPAggggAACCCCAAAKpFyDASr0pJSKAAALpFsiJAOuRRx6xjz76yN55553SAOuOO+6w/Px8GzhwoOuDBx54wNasWWNdu3a1hQsX2pAhQ+z111+32bNn2/Dhw92bFHv27GmNGzdOqs8IsJJiYicEEEAAAQQQQAABBFIuQICVclIKRAABBNIuEHyAtXTpUrv//vvd1MHrrruuNMDS7zt16mRnnXWW64SZM2fa9OnT3c8VXp199tm2aNEiN/qqWbNm7ueDBg1KusMIsJKmYkcEEEAAAQQQQAABBFIqQICVUk4KQwABBDJCIOgAa/PmzW46oEZZNWzY0P1/NIWwf//+duGFF1rHjh1dRyisuu+++2zy5Mm2cuVKmzNnjrVq1co6dOhgY8aMsX79+lm1atVMgZimHdauXdt9btuOvV/Nm59ndse66favrR9mRCdXthLNDjrSBjb6gdWwapX9KPsjgEAGC1QvzLfCgrwMrmH8VdtdYrZ9Z3a+Yj1+LY6IAALpFqhZvcD070y2/woUFZfYzqLde5Gs2fWx3b5meta+hfAXR59hJ9c6jq5GAIHABGrVKAisRfE2J+gA684777T169fbz372M1OY9fDDD1ufPn3sxBNPtGuvvdaNsjr33HOd+Lx580pHYCV2gQKrxYsXW+fOnV2Q1aRJEzfVcPz48W4x+G3bi/bqsfz8vOwPsI4+jwAr3r+LHA0B7wLVqxUQYJVRdgHWjr2v4947gwMggAACVRCoWaOQAKuMmwuwdu39IGLNrk/s9vezOMBqqACreRXOEj6CAAKZLFCrZmEmVy/j6xZ0gDVx4kRbtWqV64Tdu3fb1q1b7aCDDrJLLrnEnnnmGTc1sG/fvu7nesOgwq7BgweXdprerHj99de7KYVTp061+vXru8BLUxIbNWpk3bp1K7eDmUKY8ec+FUQAAQQQQAABBBAIVIAphIF2LM1CAIGcFgg6wErs2S+//NKuuOKK0imEM2bMcCOuRo8ebRs2bDCN1urevXvpmlj67Pz5891oq169etmsWbNs+fLl1rt3b1Mw1qNHD2vTpg0BVk7/9aHxCCCAAAIIIIAAApkoQICVib1CnRBAAIEDE8jZAKuoqMjGjh1bOkKrRYsWbo0svZlQW3FxsY0YMcK9gbBWrVpu9JbeVLhs2TJr166dm4oY7buvLmAE1oGdmHwaAQQQQAABBBBAAIGqChBgVVWOzyGAAAKZK5AzAVZ5XaDRVwUFBVa3bt09dtm1a5cbmdWgQYMq9R4BVpXY+BACCCCAAAIIIIAAAgcsQIB1wIQUgAACCGScQM4HWL56hADLlyzlIoAAAggggAACCCCwfwECLM4QBBBAIDwBAixPfUqA5QmWYhFAAAEEEEAAAQQQqECAAItTBAEEEAhPgADLU58SYHmCpVgEEEAAAQQQQAABBAiwOAcQQACBnBMgwPLU5QRYnmApFgEEEEAAAQQQQAABAizOAQQQQCDnBAiwPHU5AZYnWIpFAAEEEEAAAQQQQIAAi3MAAQQQyDkBAixPXU6A5QmWYhFAAAEEUi6w076yNUXLbKdtS3nZcRRYyw6zZoWt4zgUx0AAgSwRYA2sLOkoqokAAghUQoAAqxJYldmVAKsyWuyLAAIIIJBOge0lW23ejj/a5yUfp7MaVT52w/yW9p0aPav8eT6IAALhCRBghdentAgBBBAgwPJ0DhBgeYKlWAQQQACBlAsQYKWclAIRQCDNAgRYae4ADo8AAgh4ECDA8oCqIgmwPMFSLAIIIIBAygUIsFJOSoEIIJBmAQKsNHcAh0cAAQQ8CBBgeUAlwPKESrEIIIAAAl4ECLC8sFIoAgikUYAAK434HBoBBBDwJECA5QmWEVieYCkWAQQQQCDlAgRYKSelQAQQSLMAAVaaO4DDI4AAAh4ECLA8oKpIAixPsBSLAAIIIJByAQKslJNSIAIIpFmAACvNHcDhEUAAAQ8CBFgeUAmwPKFSLAIIIICAFwECLC+sFIoAAmkUIMBKIz6HRgABBDwJEGB5gmUElidYikUAAQQQSLkAAVbKSSkQAQTSLECAleYO4PAIIICABwECLA+oKpIAyxMsxSKAAAIIpFwgJwOsr1ablexKuWUsBeZVMzuoaSyH4iAIZKsAAVa29hz1RgABBMoXIMDydHYQYHmCpVgEEEAAgZQL5FyAVVJktvYhs+1rU24ZS4E1G5sd08csrzCWw3EQBLJRgAArG3uNOiOAAAL7FyDA8nSGEGB5gqVYBBBAAIGUCxBgpZzUb4EEWH59KT0IAQKsILqRRiCAAAJ7CBBgeTohCLA8wVIsAggggEDKBQiwUk7qt0ACLL++lB6EAAFWEN1IIxBAAAECrDjOAQKsOJQ5BgIIIIBAKgQIsFKhGGMZBFgxYnOobBUgwMrWnqPeCCCAQPkCjMDydHYQYHmCpVgEEEAAgZQLEGClnNRvgQRYfn0pPQgBAqwgupFGIIAAAnsIEGB5OiEIsDzBUiwCCCCAQMoFCLBSTuq3QAIsv76UHoQAAVYQ3UgjEEAAAQKsOM4BAqw4lDkGAggggEAqBAiwUqEYYxkEWDFic6hsFSDAytaeo94IIIBA+QKMwPJ0dhBgeYKlWAQQQACBlAsQYKWc1G+BBFh+fSk9CAECrCC6kUYggAACewgQYHk6IQiwPMFSLAIIIIBAygUIsFJO6rdAAiy/vpQehAABVhDdSCMQQAABAqw4zgECrDiUOQYCCCCAQCoECLBSoRhjGQRYMWJzqGwVIMDK1p6j3ggggED5AozA8nR2EGB5gqVYBBBAAIGUCxBgpZzUb4EEWH59KT0IAQKsILqRRiCAAAJ7CBBgeTohCLA8wVIsAggggEDKBQiwUk7qt0ACLL++lB6EAAFWEN1IIxBAAAECrDjOAQKsOJQ5BgIIIIBAKgQIsFKhGGMZBFgxYnOobBUgwMrWnqPeCCCAQPkCjMDydHYQYHmCpVgEEEAAgZQLEGClnNRvgQRYfn0pPQgBAqwgupFGIIAAAnsIEGB5OiEIsDzBUiwCCCCAQMoFCLBSTuq3QAIsv76UHoQAAVYQ3UgjEEAAAQKsOM4BAqw4lDkGAggggEAqBAiwUqEYYxkEWDFic6hsFSDAytaeo94IIIBA+QKMwPJ0dhBgeYKlWAQQQACBlAsQYKWc1G+BBFh+fSk9CAECrCC6kUYggAACewgQYHk6IQiwPMFSLAIIIIBAygUIsFJO6rdAAiy/vpQehAABVhDdSCMQQAABAqw4zgECrDiUOQYCCCCAQCoECLBSoRhjGQRYMWJzqGwVIMDK1p6j3ggggED5AozA8nR2EGB5gqVYBBBAAIGUCxBgpZzUb4EEWH59KT0IAQKsILqRRiCAAAJ7CBBgeTohCLA8wVIsAggggEDKBQiwUk7qt0ACLL++lB6EAAFWEN1IIxBAAAECrDjOAQKsOJQ5BgIIIIBAKgQIsFKhGGMZBFgxYnOobBUgwMrWnqPeCCCAQPkCjMDydHYQYHmCpVgEEEAAgZQLEGClnNRvgQRYfn0pPQgBAqwgupFGIIAAAnsIEGB5OiEIsDzBUiwCCCCAQMoFCLBSTuq3QAIsv76UHoQAAVYQ3UgjEEAAAQKsOM4BAqw4lDkGAggggEAqBAiwUqEYYxkEWDFic6hsFSDAytaeo94IIIBA+QKMwPJ0dhBgeYKlWAQQQACBlAsQYKWc1G+BBFh+fSk9CAECrCC6kUYggAACewgQYHk6IQiwPMFSLAIIIIBAygUIsFJO6rdAAiy/vpQehAABVhDdSCMQQAABAqw4zgECrDiUOQYCCCCAQCoECLBSoRhjGQRYMWJzqGwVIMDK1p6j3ggggED5AozA8nR2EGB5gqVYBBBAAIGUCxBgpZzUb4EEWH59KT0IAQKsILqRRiCAAAJ7CBBgeTohCLA8wVIsAggggEDKBQiwUk7qt0ACLL++lB6EAAFWEN1IIxBAAAECrDjOAQKsOJQ5BgIIIIBAKgQIsFKhGGMZBFgxYnOobBUgwMrWnqPeCCCAQPkCjMDydHYQYHmCpVgEEEAAgZQLEGClnNRvgQRYfn0pPQgBAqwgupFGIIAAAnsIEGB5OiEIsDzBUiwCCCCAQMoFCLBSTuq3QAIsv76UHoQAAVYQ3UgjEEAAAQKsOM4BAqw4lDkGAggggEAqBAiwUqEYYxkEWDFic6hsFSDAytaeo94IIIBA+QKMwPJ0dhBgeYKlWAQQQACBlAsQYKWc1G+BBFh+fSk9CAECrCC6kUYggAACewgQYHk6IQiwPMFSLAIIIIBAygUIsFJO6rdAAiy/vpQehAABVhDdSCMQQAABAqw4zoFcC7DWFr9lX5VsjoM25ccotOrWsOB4q5l3cMrLpkAEEEAgGwQIsLKhlxLqSICVZR1GddMhQICVDnWOiQACCPgVYASWJ99cC7AW7njW3t/9pidNv8XWyatnHWtcYLXy6vg9EKUjgAACGSpAgJWhHVNetQiwsqzDqG46BAiw0qHOMRFAAAG/AgRYnnwJsDzBeiiWAMsDKkUigEBWCRBgZVV3mRFgZVmHUd10CBBgpUOdYyKAAAJ+BQiwPPkSYHmC9VAsAZYHVIpEAIGsEiDAyqruIsDKsu6iuukRIMBKjztHRQABBHwKEGB50iXA8gTroVgCLA+oFIkAAlklQICVVd1FgJVl3UV10yNAgJUed46KAAII+BQgwPKkS4DlCdZDsQRYHlApEgEEskqAACuruosAK8u6i+qmR4AAKz3uHBUBBBDwKUCA5UmXAMsTrIdiCbA8oFIkAghklQABVlZ1FwFWlnUX1U2PAAFWetw5KgIIIOBTgADLky4BlidYD8USYHlApUgEEMgqAQKsrOouAqws6y6qmx4BAqz0uHNUBBBAwKcAAZYnXQIsT7AeiiXA8oBKkQggkFUCBFhZ1V0EWFnWXVQ3PQIEWOlx56gIIICATwECLE+6BFieYD0US4DlAZUiEUAgqwQIsLKquwiwsqy7qG56BAiw0uMe21GLt5jt3GBmu2M7ZEoPlFfDrObRKS2SwhDIBQECLE+9TIDlCdZDsQRYHlApEgEEskqAACuruosAK8u6i+qmR4AAKz3usR1156dm6x4zK/oitkOm9ECHfcfsa2emtEgKQyAXBIIPsFauXGkzZ860TZs22amnnmodOnSwQw891PXtkiVLbO7cuVZcXGzt27e3008/3f259l2wYIE1b97cWrZsWXoePPHEE9alSxerV69ehecGAVaFRBmzAwFWxnQFFUEAgTQJEGClCb6qh63Z2OyYPmZ5hVUtgc8hELwAAVbgXawA64NHszfAqtvB7GtdA+8kmodA6gWCDrAURF155ZV27LHHWseOHe3ll1+2LVu22Lhx42zFihXu17Zt27qQasqUKXbxxRdb69atbejQoS6oUsClX/XfunXr7LHHHrNhw4Yl1QsEWEkxZcROBFgZ0Q1UAgEE0ihAgJVG/KocmgCrKmp8JscECLAC73ACrMA7mOYhsG+BoAOs1157zQVTd955p2v9qlWrbNSoUTZy5Eh76qmnLD8/3wYOHOh+9sADD9iaNWusa9eutnDhQhsyZIi9/vrrNnv2bBs+fLhNmDDBevbsaY0bN07qXCLASoopI3YiwMqIbqASCCCQRgECrDTiV+XQBFhVUeMzOSZAgBV4hxNgBd7BNA+BHAywNm7caJs3b7amTZu61iuM+tOf/mT33HOPjRgxwjp16mRnnXWW+5mmGU6fPt1uvfVWF16dffbZtmjRIjf6qlmzZu7ngwYNSvo8IsBKmirtOxJgpb0LqAACCKRZgAArzR1Q2cMTYFVWjP1zUIAAK/BOJ8AKvINpHgI5GGBFTd69e7c9//zz9uyzz9p5551nP/rRj6x///524YUXuqmF2hRW3XfffTZ58mTTullz5syxVq1auTWzxowZY/369bNq1arZ0qVL3bTD2rVru89t21G8l2x+ntkd66bbv7Z+mJXnXbODjrSBjX5gNaxaUvVXe/9ZMsPe3/1mUvtn2k4KsE4r6GXVdx+SaVWjPgikVKB6Yb4VFuSltMxsL2x3idn2nXtfx7O9XZWtf1H+Nlupe6PuAAAgAElEQVRY/Lh9XvJxZT+aEfs3zG9pp+T9xNSfyWz5ecVW86NHzLavTWb3zNunZmPbXv9i211SkHl1o0ZeBWpWLzD9u4vtvwJFxSW2s2jvN9Gt2fWx3b5muu0qyc5r/C+OPsNOrnUcXV2OQLXdG63a+t9n7RpYJXU72PZDu1hJkt9bnAjhCNSqwXf3gfRm0FMIBaMRWAqgtB5W37597bTTTnNemjqoUVbnnnuu+/28efNKR2AlgiqwWrx4sXXu3NmV06RJEzfVcPz48VanTh3btr1oL//8/LzsD7COPq9yAZY9n90BVv7PCLAO5ErCZ7NCoHq1AgKsMj3lAqwde1/Hs6JDU1hJF2DtfiK7AyxTgJXcnYALsD5+NLsDrKMuIsBK4d+BbCmqZo1CAqwyneUCrF17h1Rrdn1it7+fxQFWQwVYzbPl1Iy9ni7A+ih730LoAqw6nQmwYj9z0n/AWjV5AcuB9ELQAdbWrVvtxhtvdG8NHDBgQOmoKYFpCqGmBirU0qY3DK5fv94GDx5c6llSUmLXX3+9m1I4depUq1+/vgu87r//fmvUqJF169atXHumEB7IaRnvZ5lCGK83R0MAgcwTYAph5vXJfmvEFMIs6zCqmw4BphCmQz3GYzKFMEZsDoVA5ggEHWDprYNaxF1vDqxRo0ap+lFHHeXWw9KaV6NHj7YNGza4hd67d+9euiaWdp4/f74bbdWrVy+bNWuWLV++3Hr37m0TJ060Hj16WJs2bQiw/k9g4Y5ns3oEVscaF1itvDqZ8zeTmiCAAAIxChBgxYidikMRYKVCkTICFyDACryDCbAC72Cah8C+BYIOsCZNmuSm/5Xd9FbBY445xsaOHeveTKitRYsWNnToUPdmQm3FxcVulJb2rVWrlmk0l95UuGzZMmvXrp316dOndN990TICK3v+yjECK3v6ipoigIAfAQIsP67eSiXA8kZLweEIEGCF05f7bAkBVuAdTPMQyMEAK5lO1+irgoICq1u37h6779q1y43MatCgQTLF7LUPAVaV2NLyIQKstLBzUAQQyCABAqwM6oxkqkKAlYwS++S4AAFW4CcAAVbgHUzzECDAivUcIMCKlfuADkaAdUB8fBgBBAIQIMDKsk4kwMqyDqO66RAgwEqHeozHJMCKEZtDIZA5AkFPIUwnMwFWOvUrd2wCrMp5sTcCCIQnQICVZX1KgJVlHUZ10yFAgJUO9RiPSYAVIzaHQiBzBAiwPPUFAZYnWA/FEmB5QKVIBLJcYEvxdlu7fYMVlezOypbUyq9hzWsdlXTdCbCSpsqMHQmwMqMfqEVGC+RagPVVyWb7suRTK7GSjO6X8ipX02rbYfn1k687AVbyVuyJQEACBFieOpMAyxOsh2IJsDygUiQCWS7wyc4vbMLq50y/ZuPW5fATrXfD7yZddQKspKkyY0cCrMzoB2qR0QK5FmB9tnu9/XXHH2ynbc/ofimvcq2rnWnHF56WfN0JsJK3Yk8EAhIgwPLUmQRYnmA9FEuA5QGVIhHIcgECrOzqwIb5Le07NXomX+mSIrO1D5ltX5v8ZzJpTwKsTOoN6pKhAgRYGdox5VSLACu7+ovaIpAuAQIsT/IEWJ5gPRRLgOUBlSIRyHIBAqzs6kACrOzqL2qLQBwCBFhxKKfuGARYqbOkJARCFiDA8tS7BFieYD0US4DlAZUiEchyAQKs7OpAAqzs6i9qi0AcAgRYcSin7hgEWKmzpCQEQhYgwPLUuwRYnmA9FPv/2zsTIKuqaw0vmlFmRBEHJlFxHvJEBbFQxGjEeYhR41xGnI1oIGiiMVEURUUjcQajEY2ZjMZEHPJQVHAoFBUUUUFlEEFFZpuGV2tXnX7ddAMHvety1+pvV1kkcHqdvb//9r33fGfvfRBYBlApCQHnBBBYvgJEYPnKi95CoBgEEFjFoFy4cyCwCscyQqVVSxbL0sf+JBWzZ7kcTv1OXaTpiae57HupdxqBZZQQAssIrEFZBJYBVEpCwDkBBJavABFYvvKitxAoBgEEVjEoF+4cCKzCsYxQSQXWwht/KxXTP3I5nAY77CwtfvFrl30v9U4jsIwSQmAZgTUoi8AygEpJCDgngMDyFSACy1de9BYCxSCAwCoG5cKdA4FVOJYRKiGwIqRoMwYElg1XQWAZgTUoi8AygEpJCDgngMDyFSACy1de9BYCxSCAwCoG5cKdA4FVOJYRKiGwIqRoMwYElg1XBJYRV4uyCCwLqtSEgG8CCCxf+SGwfOVFbyFQDAIIrGJQLtw5EFiFYxmhEgIrQoo2Y0Bg2XBFYBlxtSiLwLKgSk0I+CaAwPKVHwLLV170FgLFIIDAKgblwp0DgVU4lhEqIbAipGgzBgSWDVcElhFXi7IILAuq1ISAbwIILF/5IbB85UVvIVAMAgisYlAu3DkQWIVjGaESAitCijZjQGDZcEVgGXG1KIvAsqBKTQj4JoDA8pUfAstXXvQWAsUggMAqBuXCnQOBVTiWESohsCKkaDMGBJYNVwSWEVeLsggsC6rUhIBvAggsX/khsHzlRW8hUAwCCKxiUC7cORBYhWMZoRICK0KKNmNAYNlwRWAZcbUoi8CyoEpNCPgmgMDylR8Cy1de9BYCxSCAwCoG5cKdA4FVOJYRKiGwIqRoMwYElg1XBJYRV4uyCCwLqtSEgG8CCCxf+SGwfOVFbyFQDAIIrGJQLtw5EFiFYxmhEgIrQoo2Y0Bg2XBFYBlxtSiLwLKgSk0I+CaAwPKVHwLLV170FgLFIIDAKgblwp0DgVU4lhEqIbAipGgzBgSWDVcElhFXi7IILAuq1ISAbwIILF/5IbB85UVvIVAMAgisYlAu3DkQWIVjGaESAitCijZjQGDZcEVgGXG1KIvAsqBKTQj4JoDA8pUfAstXXvQWAsUggMAqBuXCnQOBVTiWESohsCKkaDMGBJYNVwSWEVeLsggsC6rUhIBvAggsX/khsHzlRW8hUAwCCKxiUC7cORBYhWMZoRICK0KKNmNAYNlwRWAZcbUoi8CyoEpNCPgmgMDylR8Cy1de9BYCxSCAwCoG5cKdA4FVOJYRKiGwIqRoMwYElg1XBJYRV4uyCCwLqtSEgG8CCCxf+SGwfOVFbyFQDAIIrGJQLtw5EFiFYxmhEgIrQoo2Y0Bg2XBFYBlxtSiLwLKgSk0I+CaAwPKVHwLLV170FgLFIIDAKgblwp0DgVU4lhEqIbAipGgzBgSWDVcElhFXi7IILAuq1ISAbwIILF/5IbB85UVvIVAMAgisYlAu3DkQWIVjGaESAitCijZjQGDZcEVgGXG1KIvAsqBKTQj4JoDA8pUfAstXXvQWAsUggMAqBuXCnQOBVTiWESohsCKkaDMGBJYNVwSWEVeLsggsC6rUhIBvAggsX/khsHzlRW8hUAwCCKxiUC7cORBYhWMZoRICK0KKNmNAYNlwRWAZcbUoi8CyoEpNCPgmgMDylR8Cy1de9BYCxSCAwCoG5cKdA4FVOJYRKiGwIqRoMwYElg1XBJYRV4uyCCwLqtSEgG8CCCxf+SGwfOVFbyFQDAIIrGJQLtw5EFiFYxmhEgIrQoo2Y0Bg2XBFYBlxtSiLwLKgSk0I+CaAwPKVHwIrR15vjBWZNyfHgSV4SPOWInv0FmnatAQ7R5dKlQACq1STqb1fCCxfeVn3FoFlTdhvfQSWUXY3fPx3eW/xLKPqtmW7bNROLutypDQta5T7RBOWPy6frHw79/GldCACq5TSoC8QKA0CCKzSyCFvLxBYOUiNeUzkkw9yHFiCh2zcTuRHJ4ps1KwEO0eXSpUAAqtUk0FgJQJteols+kNfIRWxtwisIsJ2dioEllFgCCwjsAZlEVgGUCkJAecEEFi+AkRg5cgLgZUDEodEIoDA8pUmM7B85WXdWwSWNWG/9RFYRtkhsIzAGpRFYBlApSQEnBNAYPkKEIGVIy8EVg5IHBKJAALLV5oILF95WfcWgWVN2G99BJZRdggsI7AGZRFYBlApCQHnBBBYvgJEYOXIC4GVAxKHRCKAwPKVJgLLV17WvUVgWRP2Wx+BZZQdAssIrEFZBJYBVEpCwDkBBJavABFYOfJCYOWAxCGRCCCwfKWJwPKVl3VvEVjWhP3WR2AZZYfAMgJrUBaBZQCVkhBwTgCB5StABFaOvBBYOSBxSCQCCCxfaSKwfOVl3VsEljVhv/URWEbZIbCMwBqURWAZQKUkBJwTQGD5ChCBlSMvBFYOSBwSiQACy1eaCCxfeVn3FoFlTdhvfQSWUXYILCOwBmURWAZQKQkB5wQQWL4CRGDlyAuBlQMSh0QigMDylSYCy1de1r1FYFkT9lsfgWWUHQLLCKxBWQSWAVRKQsA5AQSWrwARWDnyQmDlgMQhkQggsHylicDylZd1bxFY1oT91kdgGWWHwDICa1AWgWUAlZIQcE4AgeUrQARWjrwQWDkgcUgkAggsX2kisHzlZd1bBJY1Yb/1EVhG2SGwjMAalEVgGUClJAScE0Bg+QoQgZUjLwRWDkgcEokAAstXmggsX3lZ9xaBZU3Yb30EllF2CCwjsAZlEVgGUCkJAecEEFi+AkRg5cgLgZUDEodEIoDA8pUmAstXXta9RWBZE/ZbH4FllB0CywisQVkElgFUSkLAOQEElq8AEVg58kJg5YDEIZEIILB8pYnA8pWXdW8RWNaE/dZHYBllh8AyAmtQFoFlAJWSEHBOAIHlK0AEVo68EFg5IHFIJAIILF9pIrB85WXdWwSWNWG/9RFYRtkhsIzAGpRFYBlApSQEnBNAYPkKEIGVIy8EVg5IHBKJAALLV5oILF95WfcWgWVN2G99BJZRdggsI7AGZRFYBlApCQHnBBBYvgJEYOXIC4GVAxKHRCKAwPKVJgLLV17WvUVgWRP2Wx+BZZQdAssIrEFZBJYBVEpCwDkBBJavABFYOfJCYOWAxCGRCCCwfKWJwPKVl3VvEVjWhP3WR2AZZYfAMgJrUBaBZQCVkhBwTgCB5StABFaOvBBYOSBxSCQCCCxfaSKwfOVl3VsEljVhv/URWEbZIbCMwBqURWAZQKUkBJwTQGD5ChCBlSMvBFYOSBwSiQACy1eaCCxfeVn3FoFlTdhvfQSWUXYILCOwBmURWAZQKQkB5wQQWL4CRGDlyAuBlQMSh0QigMDylSYCy1de1r1FYFkT9lsfgWWUHQLLCKxBWQSWAVRKQsA5AQSWrwARWDnyQmDlgMQhkQggsHylicDylZd1bxFY1oT91kdgGWWHwDICa1AWgWUAlZIQcE4AgeUrQARWjrwQWDkgcUgkAggsX2kisHzlZd1bBJY1Yb/1EVhG2SGwjMAalP1OAmvRFJFlswx6U4SSZY1FWu4s0qB1EU7GKSDgkwACy1duCKwceSGwckDikEgEEFi+0kRg+crLurcILGvCfusjsIyyQ2AZgTUo+50E1hdjRL4aZ9CbIpRs0Epkq1NEGrUrwsk4BQR8EkBg+coNgZUjLwRWDkgcEokAAstXmggsX3lZ9xaBZU3Yb30EllF2CCwjsAZlEVgGUCkJAecEEFi+AkRg5cgLgZUDEodEIoDA8pUmAstXXta9RWBZE/ZbH4FllB0CywisQVkElgFUSkLAOQEElq8AEVg58kJg5YDEIZEIILB8pYnA8pWXdW8RWNaE/dZHYBllh8AyAmtQFoFlAJWSEHBOAIHlK0AEVo68EFg5IHFIJAIILF9pIrB85WXdWwSWNWG/9RFYRtkhsIzAGpRFYBlApSQEnBNAYPkKEIGVIy8EVg5IHBKJAALLV5oILF95WfcWgWVN2G99BJZRdggsI7AGZRFYBlApCQHnBBBYvgJEYOXIC4GVAxKHRCKAwPKVJgLLV17WvUVgWRP2Wx+BZZQdAssIrEFZBJYBVEpCwDkBBJavABFYOfJCYOWAxCGRCCCwfKWJwPKVl3VvEVjWhP3WR2AZZYfAMgJrUBaBZQCVkhBwTgCB5StABFaOvBBYOSBxSCQCCCxfaSKwfOVl3VsEljVhv/XrtMCaOHGijB07VioqKqRHjx7Ss2fPlOSXX34p48ePl65du0q3bt0q03300UflwAMPlE022WSdiSOw1omoZA5AYJVMFHQEAiVDAIFVMlHk6ggCKwcmBFYOSBwSiQACy1eaCKx15PXtcpH5c0QqKnwFm/W2UWORdlvm7jsCKzeqOndgnRVYU6dOlaFDh8qee+6ZJNXo0aPl1FNPld12200uv/zyJKpUcOmf+t/MmTPloYcekoEDB+Z6kSCwcmEqiYMQWCURA52AQEkRQGCVVBzr7AwCa52IRBBYOSBxSCQCCCxfaSKw1pHXom9E/vOIyNfzfAWb9XabXUT2Pzx33xFYuVHVuQPrrMAaPny4lJWVyYUXXphCv++++2TGjBnywx/+UCZMmCADBgyQ119/XZ5++mm54oorZNiwYXL88cdLx44dc71IEFi5MJXEQQiskoiBTkCgpAggsEoqjnV2BoG1TkQIrByIOCQWAQSWrzwRWAisqgQQWL5+f4vZ2zorsAYPHiy9e/eWgw8+OPF+6qmn5J///KfceOONSV4dcsgh8uqrr6bZV126dEn/ftFFF+XOBoGVG9UGPxCBtcEjoAMQKDkCCKySi2StHUJg5ciLGVg5IHFIJAIILF9pIrAQWAgsX7+zG6q3dVZg9e/fX04++WTZb7/9EnuVVXfffbfce++9Mm3aNHn22Wdlhx12kF69esmQIUPkZz/7mTRs2FAmTZqUlh02a9Ys/dy8BctrZNewfj15auEEmbFs/obK9Xudd/PGreWQlt2lfkWDXHUa1K8nMxtPkC9WTs91fKkd1KxeK+myYj+R8qa5ula/rJ60Kn9DypZMyXV8yR1Uv7ksbdFbFle0zt21xg3LpKysXu7jS+3A8vJVsmLlytzdatSwTDRnr618xSpZUVFzvM2aNJCNGtf3OiyTfq+oWCVfL/q2Ru0lZYvl3wvekK/KF5uc17rors07So8mO0vFylW5TlWv4TL5uMGLsmjVV7mOL7WD2pZtJR2W7yuaZ57WsP5KabXkOZFv5+Y5vPSOabSZLGjaR8orynL1TT+nW015SerNnZnr+JI7qEUbWbhLL1le1iR315o0qi/1/L6Ny/LyCqntY6t180aiedL+n8DS5RWyeNmKGki+rP+V/HPeq7JiVf7P/1Li2rfNrtKlbEtZle9tTSqazJdp8qKUS83PtFIa15r60rHertJ62faS82NLmtdfIE0W/q9IxSIPw6vRx5VNd5AFDf8n9+f0RiuWSLN3xoksWuByvKvad5YF3fbK/TndqHypNPjXn6Vi9iyX463fqYuUH36ilNfyvWSTVo1djqlUOl1nBZYuHdRZVv369UtZvPjii5UzsKqGo8Lqtddekz59+iSR1alTp7TU8KabbpKWLVvWKrBKJVz6AQEIQKAqAQRWzdfDmgQWrxwIQAACpUgAgVUzlTUJrFLMjz5BAAIQQGB9v9dAnRVYv/71r9PSwDPOOCMR1CcMzp49Wy655JJKoqtWrZKrrroqLSn8y1/+Iu3bt0/C65577pGtttpKfvSjH30/+vw0BCAAAQhAAAIQgAAEIAABCEAAAhCAwDoJ1FmB9eSTT6YZV9ddd53MmzdPbr/9djniiCMq98RScq+88kqabfWTn/xE/v3vf8vkyZPlpz/9qdx2221y3HHHyR577LFOwBwAAQhAAAIQgAAEIAABCEAAAhCAAAQg8P0I1FmBtWLFCrnhhhvk448/TgS33XZbufzyy9OTCbVVVFSIztLSJxA2bdpUFi9enJ5U+M4778g+++wjp59+euWx3y8CfhoC1QksX75cPv/8c2nVqlX6jwYBCEAAAr4IfPPNN/L111/LpptuKhtttJGvztNbCEAAAnWcgK7C+eKLL0SvF9u1aycNGuTbF7iOY2P4ECgKgTorsDK6Ovuqfv360qZNm2rAy8vL08yszTffvChBrM9JdEnjwoUL5Y477kgby0dtunH+yio7mDZu3Fj23XdfOfHEE6We551Z1xCYZvqHP/xBpk6dWnmELlXV/dratm0bKma9sLvsssuqjWnjjTeWQw89VPbff/9QY80GM3HixPQ7u3rTcQ8dOjTkmBlU7QSy13/nzp3lyiuvDIvpk08+kWuuuaba+Fq0aCE//vGPpUePHiHHrWPWGd1fffX/m+F3795dzjrrrHAXQC+//LLcf//9lTnqDcAtttgizVTfZpttQub7pz/9Sf773//WGJs+EOi0004LOWYGVTuBCRMmpC1FDjvsMDnqqKPCYvrPf/6TtlGp2jbbbDM555xzpGPHjiHHrStwRo4cWXkNou9txx57bLVVOlEGrq9hfS1nTUXddtttJ2eeeaa0bp3/YU+eeAwePFjmzq35ABd9wJs+qI1W+gTqvMAq/Yiq91BnjF177bXpL88++2zZe++9vQ0hd39VYOnyzb322ktUKOqX5b///e/y85//XHbaaafcdTwcqHd69EJW/1RhpeL0s88+kxEjRqQP0Ouvv97DMHL3US/udMajjlUfjDBnzhx57rnnRCXPCSecIAcddFDuWl4OzATWb3/722oCViV0NEHpJZMN1c9sCbv+bg8bNizsTMtMYKnE0oeeLFmyRB5++OE0k1llrt6UiNQWLFiQ3td0e4FTTz1VmjRpIu+//77ceuut6YnHp5xySqThps9kFVj6UBtt7733XnqC8/Tp09MM9ogXtyqw3n777Wr7perY9cnUKmdpdYeAruL48MMPU+76Ph616RYqKrGyaw99n8seZPWb3/wm3LDfeuutdBNCv4secMAB6fpj3Lhxaa/k8847T37wgx+EGvNdd92VVn1cdNFFabbZ66+/Ls8884x8++23cvPNN4ecKKECq2vXrpUPcssC1cks0b6XhHqxVhkMAstZsqNGjUpTWvWiV99U9cty1KYCS5dq9uzZMw0xkx4RP0D0C/Hw4cPl6quvTg8IyJruwaZfHPTOrl4MRWlZlvohsvXWW1cO68EHH5Tx48fXOlPJ+9gzgaV3uyLOIPSeTzH7r+/bOtPwiSeekCOPPDLsA0EygVVV0mXSQ6VO8+bNi4nd/FyjR49OFzr6Xl51uYnezf/000/TzLNILcvy3nvvrTasQYMGpZlYekEUranA0lnSES/co2VlOR5dInzppZemWSoqcaMKW2WoAmvMmDFyyy23VCJV6aE31KPdXNUB6s1knRmv+VZtf/vb39JSwl69elm+tIpeW7OcP3++6PfxrGWv75NOOkn69OlT9D5Zn1DHuvvuu4f7TLbmVkr1EVillMY6+qL7cp1//vnpyYl6AXz33XenL8p65y9iU4GlbzC6P9myZcvktddek+zOT7Slk0899VR6qMCdd94ZMcoaY1qTwMrufEV8XWcCS78QZHvtKRi9m6ezU2h1g0BVqaNfiD/44IP0MJGILRurLrHRzyl9/x47dmxanqCzL6M1XQrcqFGjGrNzoo0zG8+aBJbOsnvzzTdDLo1WgfXqq6/K0UcfXRmrfh/r3bt31JgZVy0E9MaizlJROZ/NutTP9ohNBdbjjz+eltBl+0LpMtqTTz45zVCK1vTaQ2829O3bN9rQah1PbQJLD1TJo5/VOpEgWtOx6R7DVVcxqbTcddddow017HgQWI6i1Qtg3SNJl17o0hOVWVE/QDQW/RDRqdn6JqPyTqe46n5lene3Q4cOjpJbd1f//Oc/y0svvZSEZF1oaxJYOh1/yJAhaXp6tLX3mcBaXThfcskl0qVLl7oQO2MUEZ1lqDMr9S6vLrnS17ouzdA9RaK1TGDpbBydkaQPqNAZxO3bt08PSIk2Vf+qq65KM490b5i60NYksHRm4fPPP19txkYUHtkeWFXfx/WGRNXZKVHGyjjWTEC/h+oDnXTvK90fSoXO73//+5Czq1Vg/fWvf61cEqz7tep3uIj7vum1hr5/68y6bPVH9N+DNQksvbGmDyHR7WqiNRVYus911QesqKzT62qaDwIILB85pV7qWuTJkydX/sItXbo0faDo1OWIbfUlhPrBohd6uj9UtDdUnZWgF7aacdXZOPr0y6effjrdCYo0S2dNAkuX2jzwwAMhZ6KxhDDiu9T6jUlvPOgSaP1T5Y3+qVJHH15wzDHHrF8xB0fXtoRQZ2ENHDgw7REV7QJBby599NFHNfbDUQ6TJk1Kmz1HamsSWLpMWi9yV1+CE2HsLCGMkOL3G8PMmTNFZbVKeV0NoO/h+l6uM7G6dev2/YqX4E/XtoTw3XffTdL2xhtvrPEQrBIcwnp1SWcH6z6GKrGqNt0bSq9Dou09vCaBpfsN63eTiHvSsoRwvX4lSvJgBFZJxlKzU7r5re4nofulZBuj6pdindYb8QNECawusPTv9Kkguu5+9SdbOYlxjd3Uix6927H6XR/d2Pwf//iH3HbbbaHu7K1JYOmmqLqJpM7OiNYQWNESXf/xqMTQ32WVWDqbVJvKa31PiziDozaBpWPWCz190o9ukhup6cyj2j6Tdb8vvdCNdne3NoGlF/MXX3xx2tdNv69EawisaImu/3h0iax+nuvTNrP20EMPpeVW0W6u6vhqE1iLFi1KS6X1v5133nn9IZbwT+h3cZ0pXPUzWZdOXnDBBXL88ceHe1J2bQIre2CYzjSM+ERZBFYJ/wLm7BoCKyeoDX2YTsd/5JFH0lPpss1h9UJfL4T0S2K/fv02dBcLfn4VWIcffni6E6J3PfQxr/qEIx1rxC/GKm/0yYP6IalLynSjWL3YjTjeTGCpsFMhq7MJdZNQ/VIY9QMTgVXwtwh3BfX3WW9G6Gs8a9OmTUsb4eqSws6dO7sb09o6nAksnYmjM0h1Rql+lr3xxhshf891r8YBAwakJZJ6IavLoFVQPvbYYyHHmwksffiINp1dp4JHZ1/pUvBom/TrGBFYod6i1nswmchQQVt1RqWK63/9619pm4/s5sR6F9HqJYcAAA7QSURBVC/RH1CBpfu06sxZbXPnzk17ts6aNavaNUmJdn+9u5XdUNb9vbKMM2mpy0R1n8NITQWWzirUay6dSai56gPD9AFLerMp4kOHEFj+X8EILCcZ6hdE/VLcv3//aj3WPZN0b6iImwBnb6bZgHXJTffu3dNdr6pPeHIS4Tq7qVJHl6DoI8izpuPVDRSj7RXz9ddfy2WXXVaNSadOneSQQw5JGUdsCKyIqeYfk85M0Rk4+vtc9SlG2QWRLqfTPQ0jtUxgVR2T7muoFwUHHnhgpKFWjkVvPOj7uMq6rOld+4MPPjjceDOBlQ1M94Lafvvt0wbIVZ+mG2ngCKxIaa7/WKZMmZKWCOsNx7Zt21YWmD17tvzqV79K7/F60zVS0w3rdZ+vqk2fxqefZTrrLGLLJg2o0NGmeyWde+65suOOO4Ybri751gkCWdOx6mbmp5xySqinn1cNDoHl/2WMwPKfISMIRkAfX6syS4VlNHEVLCqGAwEIQKAGAZWS+lhylZb6Ph5tRgaRQwACEIhOQFd+zJkzJ30PV1kZcSZS9AwZX1wCCKy42TIyCEAAAhCAAAQgAAEIQAACEIAABCAQggACK0SMDAICEIAABCAAAQhAAAIQgAAEIAABCMQlgMCKmy0jgwAEIAABCEAAAhCAAAQgAAEIQAACIQggsELEyCAgAAEIQAACEIAABCAAAQhAAAIQgEBcAgisuNkyMghAAAIQgAAEIAABCEAAAhCAAAQgEIIAAitEjAwCAhCAAAQgAAEIQAACEIAABCAAAQjEJYDAipstI4MABCAAAQhAAAIQgAAEIAABCEAAAiEIILBCxMggIAABCEAAAhCAAAQgAAEIQAACEIBAXAIIrLjZMjIIQAACEIAABCAAAQhAAAIQgAAEIBCCAAIrRIwMAgIQgAAEIAABCEAAAhCAAAQgAAEIxCWAwIqbLSODAAQgAAEIQAACEIAABCAAAQhAAAIhCCCwQsTIICAAAQhAAAIQgAAEIAABCEAAAhCAQFwCCKy42TIyCEAAAhCAAAQgAAEIQAACEIAABCAQggACK0SMDAICEIAABCAAAQhAAAIQgAAEIAABCMQlgMCKmy0jgwAEIAABCEAAAhCAAAQgAAEIQAACIQggsELEyCAgAAEIQAACEIAABCAAAQhAAAIQgEBcAgisuNkyMghAAAIQgAAEIAABCEAAAhCAAAQgEIIAAitEjAwCAhCAAAQgAAEIQAACEIAABCAAAQjEJYDAipstI4MABCAAAQhAAAIQgAAEIAABCEAAAiEIILBCxMggIAABCEAAAhCAAAQgAAEIQAACEIBAXAIIrLjZMjIIQAACEIBAnSawYsUK+fTTT6W8vFzatWsnrVu3rtM8GDwEIAABCEAAAhDwTACB5Tk9+g4BCEAAAhCAQA0CixcvlhEjRsj7779f7d+22GILOfvss6VDhw4lR23QoEFSVlYm1113naxatUqmTZsmbdq0kU022aTk+kqHIAABCEAAAhCAwIYggMDaENQ5JwQgAAEIQAACJgQWLFggV155pSxdulS23XZb2WeffeTbb7+VCRMmyPTp06VBgwZyww03SKtWrUzO/12LDhs2TOrVqyeXXnqp6Myx/v37y7777itnnHHGdy3Jz0EAAhCAAAQgAIFQBBBYoeJkMBCAAAQgAIG6TWDUqFEybtw46dOnj5x00knVYDz44IMyduxY6dWrl5x++uklCwqBVbLR0DEIQAACEIAABDYgAQTWBoTPqSEAAQhAAAIQKBwBnXV14YUXpllWt99+uzRs2LBacV1aOHToUGnfvr2ce+656d8++eQTeeCBB9JeWbqEb/vtt5dTTz1VNt544/TvY8aMkddeey3JsIcffjgdp8v6jjrqKNlzzz0r67/wwgvy5JNPypdffimdO3eWvffeW/r27ZtmVWn76KOP5JFHHpGZM2em2V/6s1pDz6lt5MiR6djDDz9chg8fLrNmzUrj6Nq1q1x++eXpmJdffjmdY+7cudKsWTPZa6+95Pjjj5dGjRqlf1+0aJE8//zzMn78eJk3b55svvnmcvLJJ8t2221XOMhUggAEIAABCEAAAhuIAAJrA4HntBCAAAQgAAEIFJbAxx9/LNdee22SR7rX1bqayqtrrrkmiaKePXuKCq433ngj/X9d0qeSSOXWiy++mErpHloqv958801ZuXJlkmEqup555hl59NFHpUWLFrLjjjvKpEmT0hLGI488MgmpyZMny80335xq7LzzzrJw4UKZMWNGEku/+MUv0t/rnyqzfvnLX8pDDz0kEydOTPX22GOPJNSee+45GT16dDrf7rvvnsSb7pPVpUsXueKKK1KNu+66K8k2lWMVFRXy1ltvpX7qWEptyeS6suHfIQABCEAAAhCAwOoEEFi8JiAAAQhAAAIQCEHglVdekfvuu08OO+ywNLtpXe36669PEkgllsopbTrL6f7775cDDzxQTjzxxEqBVXXZYSaszjvvPNlpp53k4osvTrO9dG+tpk2bpj2sLrjgAmnZsmWSXCqndGaW/nvbtm2rySbdr0tnbGUCS/u0+hLC5cuXp3M0btw41dM/td1zzz1pby/9t2222SbNPqsqtFRy6abwKsBU0NEgAAEIQAACEICAZwIILM/p0XcIQAACEIAABCoJ6OwjnYV0zDHHyKGHHrpWMvqkP52lpTOTdIZS1jJ5pFJJ5VI2A0vFUvZEwGym17HHHptmUQ0ZMkT2228/Oe200yrr6JJB3VBe6+gSwK222iptzJ61qVOnyh//+Ec5+uijpV+/fmsVWB9++GE6R48ePeSss86qrPHuu+/KLbfckoTdEUcckerrjKsDDjhAunfvnqRWtkSRlwkEIAABCEAAAhDwTgCB5T1B+g8BCEAAAhCAQCKg+0tdddVVNWRSVTzPPvusLFmyRHr37i0DBgxIouecc86pRlBnMum+Uiq2MoF1xx13VM58+uyzz+Tqq69OoqxNmzZp1pfukaUbx6/e3nvvPbnpppvWmNBBBx0kJ5xwwloFVjYrTDee15lgWdN9rgYNGpRmV5155pmiQuvuu+9OSyG1qbzS8ekMrGzWFi8VCEAAAhCAAAQg4JUAAstrcvQbAhCAAAQgAIFqBLLZU7p3lc5MWn320fz582XgwIFpueDgwYPTMj+dpaQSKGvLli1Lf7/6DKw777wz7Y2lrarA0j2xRowYUWPZom60rqJM2+9+9zvZYYcd5LjjjquRWOvWrdMssLUtIdT9sFSg6bJInW2Vtezvqy6Z1JllunRQ9+HSvbt06WKpP3WRlzEEIAABCEAAAhDIQwCBlYcSx0AAAhCAAAQg4IKAiqbXX3+9cgP1rNMqdnR5of5btsTw/PPPl/LyctGfyWTXO++8I7feemtahqdP8MtmYK1JYOmG6SrDMuGl51ORpvtS6X5YKq/0PLohu0q1rOlyR92sXWdO7bbbbmsVWHPmzEnLGbfeeut0rqzpxvG6H5fW1721dMaYztLSjd+16d5Z+m9V98VyESKdhAAEIAABCEAAArUQQGDxsoAABCAAAQhAIAwBXT6nSwNVIukTAfWJfdrGjx8vui+V7mOlTyqsX7++PPHEE/L444/LrrvummY26c/ee++96c9sw/V1CSzda0s3Vtc9rXQvK53RNWbMGJkyZUrlrKyRI0fKSy+9JN26dZO+ffvKBx98ILqUUTd+V+nUpEmTagIr259Llyfq8r9ddtklbcau/denGqqgmj59etpDS2dvZUsUL7nkkiTidCx6Lt3U/umnn057c+keXTQIQAACEIAABCDgmQACy3N69B0CEIAABCAAgRoEZs2alUSULqWr2vbee+9q+0Gp5FJBpaInayqAdAmhSi1tKoleeOGFNEsrW0KY7bWVzeRauHBhmrU1Y8aM9DO635TuS6VPMdR6uixRZ3+9/fbblefRZY6615YKL21VlxDq/x81apSMGzcunVPP/dVXX6Vz6LmzpoJLN4hv165d+it9IqH2V2deZa1jx45p1lbWd14uEIAABCAAAQhAwCsBBJbX5Og3BCAAAQhAAAJrJfDNN9/I7Nmzk1Dacsst04yn2po+LVD3tWrZsmXaH0tnZ32XpnX0nB06dKj1x1VCqVxTedWpUyepV6/eWk+jM8F0Nlbz5s3Tcfq/v/jiC/n8889ls802k0033bRGjYqKCtGN41XO6b/reGgQgAAEIAABCEAgAgEEVoQUGQMEIAABCEAAAhCAAAQgAAEIQAACEAhMAIEVOFyGBgEIQAACEIAABCAAAQhAAAIQgAAEIhBAYEVIkTFAAAIQgAAEIAABCEAAAhCAAAQgAIHABBBYgcNlaBCAAAQgAAEIQAACEIAABCAAAQhAIAIBBFaEFBkDBCAAAQhAAAIQgAAEIAABCEAAAhAITACBFThchgYBCEAAAhCAAAQgAAEIQAACEIAABCIQQGBFSJExQAACEIAABCAAAQhAAAIQgAAEIACBwAQQWIHDZWgQgAAEIAABCEAAAhCAAAQgAAEIQCACAQRWhBQZAwQgAAEIQAACEIAABCAAAQhAAAIQCEwAgRU4XIYGAQhAAAIQgAAEIAABCEAAAhCAAAQiEEBgRUiRMUAAAhCAAAQgAAEIQAACEIAABCAAgcAEEFiBw2VoEIAABCAAAQhAAAIQgAAEIAABCEAgAgEEVoQUGQMEIAABCEAAAhCAAAQgAAEIQAACEAhMAIEVOFyGBgEIQAACEIAABCAAAQhAAAIQgAAEIhBAYEVIkTFAAAIQgAAEIAABCEAAAhCAAAQgAIHABBBYgcNlaBCAAAQgAAEIQAACEIAABCAAAQhAIAIBBFaEFBkDBCAAAQhAAAIQgAAEIAABCEAAAhAITACBFThchgYBCEAAAhCAAAQgAAEIQAACEIAABCIQQGBFSJExQAACEIAABCAAAQhAAAIQgAAEIACBwAQQWIHDZWgQgAAEIAABCEAAAhCAAAQgAAEIQCACAQRWhBQZAwQgAAEIQAACEIAABCAAAQhAAAIQCEwAgRU4XIYGAQhAAAIQgAAEIAABCEAAAhCAAAQiEPg/7pbG863J9pkAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_ufabc_grades_prop_comp_bar = make_subplots(rows=1, cols=3, shared_yaxes=True, x_title='Conceitos', \n",
    "                                         subplot_titles=SUBPLOT_TITLES)\n",
    "\n",
    "for col, (prop, name) in enumerate(zip([ufabc_grade_prop, next_prob_grade_prop, next_mode_grade_prop], SUBPLOT_TITLES)):\n",
    "    x_grades_prop_bar = prop.index\n",
    "    y_grades_prop_bar = prop.values\n",
    "    \n",
    "    grades_prop_bar_colors = [GRADES_COLOR_MAP[grade] for grade in x_grades_prop_bar]\n",
    "    fig_ufabc_grades_prop_comp_bar.add_trace(go.Bar(x=x_grades_prop_bar, y=y_grades_prop_bar, showlegend=False, name=name, hovertemplate='Conceito %{x}<br>%{y:.2%}',\n",
    "                                              yhoverformat='.2%', marker_color=grades_prop_bar_colors), row=1, col=col + 1)\n",
    "\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Comparativo da proporção de notas na graduação'\n",
    "fig_ufabc_grades_prop_comp_bar.update_layout({'yaxis_title': 'Proporção', 'yaxis_tickformat': '.0%', \n",
    "                                              'yaxis_range': [0, 1], 'height': 500, 'margin_t': 70, 'margin_b': 70})\n",
    "fig_ufabc_grades_prop_comp_bar.write_html('../assets/graphs/ufabc_grades_prop_comp_bar.html')\n",
    "fig_ufabc_grades_prop_comp_bar.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_ufabc_grades_prop_comp_pie = make_subplots(rows=1, cols=3, shared_yaxes=True, subplot_titles=SUBPLOT_TITLES, specs=[[{'type': 'pie'}, {'type': 'pie'}, {'type': 'pie'}]])\n",
    "\n",
    "for col, (prop, name) in enumerate(zip([ufabc_grade_prop, next_prob_grade_prop, next_mode_grade_prop], SUBPLOT_TITLES)):\n",
    "    x_grades_prop_bar = prop.index\n",
    "    y_grades_prop_bar = prop.values\n",
    "    \n",
    "    grades_prob_bar_colors = [GRADES_COLOR_MAP[grade] for grade in x_grades_prop_bar]\n",
    "    fig_ufabc_grades_prop_comp_pie.add_trace(go.Pie(labels=x_grades_prop_bar, values=y_grades_prop_bar, name=name, hovertemplate='Conceito %{label}<br>%{percent:.2%}', \n",
    "                                              texttemplate='%{percent:.1%}', marker_colors=grades_prob_bar_colors,hoverinfo='label+percent'), row=1, col=col + 1)\n",
    "\n",
    "\n",
    "\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Comparativo da proporção de notas na graduação'\n",
    "fig_ufabc_grades_prop_comp_pie.update_layout({'height': 500, 'margin_t': 80, 'margin_b': 40})\n",
    "fig_ufabc_grades_prop_comp_pie.write_html('../assets/graphs/ufabc_grades_prop_comp_pie.html')\n",
    "fig_ufabc_grades_prop_comp_pie.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise da evolução da proporção de conceitos quadrimestre a quadrimestre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em seguida, vamos fazer uma comparação da evolução da proporção da minhas notas quadrimestre a quadrimestre. Para fazermos isso, uma das possibilidades é traçar um mapa de calor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>num_A</th>\n",
       "      <th>num_B</th>\n",
       "      <th>num_C</th>\n",
       "      <th>num_D</th>\n",
       "      <th>num_F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ano</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017.2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.2</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        num_A  num_B  num_C  num_D  num_F\n",
       "Ano                                      \n",
       "2017.2      4      0      2      0      0\n",
       "2017.3      4      0      0      0      0\n",
       "2018.1      2      2      0      0      0\n",
       "2018.2      2      2      0      0      0\n",
       "2018.3      3      1      0      0      0\n",
       "2019.1      2      3      0      0      0\n",
       "2019.2      3      3      0      0      0\n",
       "2019.3      2      2      1      0      0\n",
       "2020.3      2      1      0      0      0\n",
       "2021.1      3      1      0      0      0\n",
       "2021.2      3      1      0      0      0\n",
       "2021.3      3      1      0      0      0\n",
       "2022.1      4      0      0      0      0\n",
       "2022.2      6      0      0      0      0\n",
       "2022.3      3      1      0      0      0\n",
       "2023.1      4      0      0      0      0\n",
       "2023.2      3      1      0      0      0\n",
       "2023.3      3      0      0      0      0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_grades_prop_quad = df_ufabc.groupby('Ano', as_index=False).agg(\n",
    "    num_A=('Resultado', lambda s: s.value_counts().get('A', default=0)),\n",
    "    num_B=('Resultado', lambda s: s.value_counts().get('B', default=0)),\n",
    "    num_C=('Resultado', lambda s: s.value_counts().get('C', default=0)),\n",
    "    num_D=('Resultado', lambda s: s.value_counts().get('D', default=0)),\n",
    "    num_F=('Resultado', lambda s: s.value_counts().get('F', default=0))\n",
    ")\n",
    "\n",
    "df_ufabc_grades_prop_quad = df_ufabc_grades_prop_quad.sort_values(by='Ano', ascending=True).set_index('Ano', drop=True)\n",
    "df_ufabc_grades_prop_quad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se quisermos ver a proporção:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>num_A</th>\n",
       "      <th>num_B</th>\n",
       "      <th>num_C</th>\n",
       "      <th>num_D</th>\n",
       "      <th>num_F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ano</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017.2</th>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.1</th>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.3</th>\n",
       "      <td>66.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.1</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        num_A  num_B  num_C  num_D  num_F\n",
       "Ano                                      \n",
       "2017.2   66.7    0.0   33.3    0.0    0.0\n",
       "2017.3  100.0    0.0    0.0    0.0    0.0\n",
       "2018.1   50.0   50.0    0.0    0.0    0.0\n",
       "2018.2   50.0   50.0    0.0    0.0    0.0\n",
       "2018.3   75.0   25.0    0.0    0.0    0.0\n",
       "2019.1   40.0   60.0    0.0    0.0    0.0\n",
       "2019.2   50.0   50.0    0.0    0.0    0.0\n",
       "2019.3   40.0   40.0   20.0    0.0    0.0\n",
       "2020.3   66.7   33.3    0.0    0.0    0.0\n",
       "2021.1   75.0   25.0    0.0    0.0    0.0\n",
       "2021.2   75.0   25.0    0.0    0.0    0.0\n",
       "2021.3   75.0   25.0    0.0    0.0    0.0\n",
       "2022.1  100.0    0.0    0.0    0.0    0.0\n",
       "2022.2  100.0    0.0    0.0    0.0    0.0\n",
       "2022.3   75.0   25.0    0.0    0.0    0.0\n",
       "2023.1  100.0    0.0    0.0    0.0    0.0\n",
       "2023.2   75.0   25.0    0.0    0.0    0.0\n",
       "2023.3  100.0    0.0    0.0    0.0    0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_grades_prop_quad.apply(lambda s: np.round(s/s.sum()*100,1), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>num_A</th>\n",
       "      <th>num_B</th>\n",
       "      <th>num_C</th>\n",
       "      <th>num_D</th>\n",
       "      <th>num_F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>72.966667</td>\n",
       "      <td>24.072222</td>\n",
       "      <td>2.961111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.022901</td>\n",
       "      <td>20.477241</td>\n",
       "      <td>8.914835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54.175000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93.750000</td>\n",
       "      <td>38.325000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            num_A      num_B      num_C  num_D  num_F\n",
       "count   18.000000  18.000000  18.000000   18.0   18.0\n",
       "mean    72.966667  24.072222   2.961111    0.0    0.0\n",
       "std     21.022901  20.477241   8.914835    0.0    0.0\n",
       "min     40.000000   0.000000   0.000000    0.0    0.0\n",
       "25%     54.175000   0.000000   0.000000    0.0    0.0\n",
       "50%     75.000000  25.000000   0.000000    0.0    0.0\n",
       "75%     93.750000  38.325000   0.000000    0.0    0.0\n",
       "max    100.000000  60.000000  33.300000    0.0    0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_grades_prop_quad.apply(lambda s: np.round(s/s.sum()*100,1), axis=1).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Além disso, podemos trazer uma visão acumulada, vendo como a proporção acumulada e quantidade acumuladas de conceitos alteraram-se ano após ano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cum_A</th>\n",
       "      <th>cum_B</th>\n",
       "      <th>cum_C</th>\n",
       "      <th>cum_D</th>\n",
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       "      <th>Ano</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017.2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.3</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.1</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.2</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.3</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.1</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.2</th>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.3</th>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.3</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.1</th>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.2</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.3</th>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.1</th>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.2</th>\n",
       "      <td>43</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.3</th>\n",
       "      <td>46</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.1</th>\n",
       "      <td>50</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.2</th>\n",
       "      <td>53</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.3</th>\n",
       "      <td>56</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cum_A  cum_B  cum_C  cum_D  cum_F\n",
       "Ano                                      \n",
       "2017.2      4      0      2      0      0\n",
       "2017.3      8      0      2      0      0\n",
       "2018.1     10      2      2      0      0\n",
       "2018.2     12      4      2      0      0\n",
       "2018.3     15      5      2      0      0\n",
       "2019.1     17      8      2      0      0\n",
       "2019.2     20     11      2      0      0\n",
       "2019.3     22     13      3      0      0\n",
       "2020.3     24     14      3      0      0\n",
       "2021.1     27     15      3      0      0\n",
       "2021.2     30     16      3      0      0\n",
       "2021.3     33     17      3      0      0\n",
       "2022.1     37     17      3      0      0\n",
       "2022.2     43     17      3      0      0\n",
       "2022.3     46     18      3      0      0\n",
       "2023.1     50     18      3      0      0\n",
       "2023.2     53     19      3      0      0\n",
       "2023.3     56     19      3      0      0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_grades_cum_quad = df_ufabc.groupby('Ano', as_index=False).agg(\n",
    "    cum_A=('Resultado', lambda s: s.value_counts().get('A', default=0)),\n",
    "    cum_B=('Resultado', lambda s: s.value_counts().get('B', default=0)),\n",
    "    cum_C=('Resultado', lambda s: s.value_counts().get('C', default=0)),\n",
    "    cum_D=('Resultado', lambda s: s.value_counts().get('D', default=0)),\n",
    "    cum_F=('Resultado', lambda s: s.value_counts().get('F', default=0)),\n",
    ")\n",
    "\n",
    "df_ufabc_grades_cum_quad = df_ufabc_grades_cum_quad.sort_values(by='Ano', ascending=True).set_index('Ano', drop=True)\n",
    "df_ufabc_grades_cum_quad = df_ufabc_grades_cum_quad.cumsum()\n",
    "df_ufabc_grades_cum_quad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou vendo a proporção:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>cum_A</th>\n",
       "      <th>cum_B</th>\n",
       "      <th>cum_C</th>\n",
       "      <th>cum_D</th>\n",
       "      <th>cum_F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ano</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017.2</th>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.3</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.1</th>\n",
       "      <td>71.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.2</th>\n",
       "      <td>66.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.3</th>\n",
       "      <td>68.2</td>\n",
       "      <td>22.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.1</th>\n",
       "      <td>63.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.2</th>\n",
       "      <td>60.6</td>\n",
       "      <td>33.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.3</th>\n",
       "      <td>57.9</td>\n",
       "      <td>34.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.3</th>\n",
       "      <td>58.5</td>\n",
       "      <td>34.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.1</th>\n",
       "      <td>60.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.2</th>\n",
       "      <td>61.2</td>\n",
       "      <td>32.7</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.3</th>\n",
       "      <td>62.3</td>\n",
       "      <td>32.1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.1</th>\n",
       "      <td>64.9</td>\n",
       "      <td>29.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.2</th>\n",
       "      <td>68.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.3</th>\n",
       "      <td>68.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.1</th>\n",
       "      <td>70.4</td>\n",
       "      <td>25.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.2</th>\n",
       "      <td>70.7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.3</th>\n",
       "      <td>71.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cum_A  cum_B  cum_C  cum_D  cum_F\n",
       "Ano                                      \n",
       "2017.2   66.7    0.0   33.3    0.0    0.0\n",
       "2017.3   80.0    0.0   20.0    0.0    0.0\n",
       "2018.1   71.4   14.3   14.3    0.0    0.0\n",
       "2018.2   66.7   22.2   11.1    0.0    0.0\n",
       "2018.3   68.2   22.7    9.1    0.0    0.0\n",
       "2019.1   63.0   29.6    7.4    0.0    0.0\n",
       "2019.2   60.6   33.3    6.1    0.0    0.0\n",
       "2019.3   57.9   34.2    7.9    0.0    0.0\n",
       "2020.3   58.5   34.1    7.3    0.0    0.0\n",
       "2021.1   60.0   33.3    6.7    0.0    0.0\n",
       "2021.2   61.2   32.7    6.1    0.0    0.0\n",
       "2021.3   62.3   32.1    5.7    0.0    0.0\n",
       "2022.1   64.9   29.8    5.3    0.0    0.0\n",
       "2022.2   68.3   27.0    4.8    0.0    0.0\n",
       "2022.3   68.7   26.9    4.5    0.0    0.0\n",
       "2023.1   70.4   25.4    4.2    0.0    0.0\n",
       "2023.2   70.7   25.3    4.0    0.0    0.0\n",
       "2023.3   71.8   24.4    3.8    0.0    0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufabc_grades_cum_quad.apply(lambda s: np.round(s/s.sum()*100, 1), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, podemos combinar os resultados em um subplot com dois heatmaps e comparar com os demais alunos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Só com a proporção\n",
    "fig_ufabc_grades_prop_cum_quad = make_subplots(rows=1, cols=3, shared_yaxes=True, shared_xaxes=True, horizontal_spacing=0.025, vertical_spacing=0.025,\n",
    "                                               x_title='Proporção e quantidade de conceitos', subplot_titles=SUBPLOT_TITLES)\n",
    "\n",
    "# Com a proporção e proporção acumulada\n",
    "# fig_ufabc_grades_prop_cum_quad = make_subplots(rows=2, cols=3, shared_yaxes=True, shared_xaxes=True, horizontal_spacing=0.025, vertical_spacing=0.025,\n",
    "#                                                x_title='Proporção e quantidade de conceitos', subplot_titles=SUBPLOT_TITLES)\n",
    "\n",
    "for col, df_tmp in enumerate([df_ufabc, df_next.rename(columns={'Conceito provável': 'Resultado'}), df_next.rename(columns={'Conceito moda': 'Resultado'})]):\n",
    "    df_ufabc_grades_prop_quad = df_tmp.groupby('Ano', as_index=False).agg(\n",
    "        num_A=('Resultado', lambda s: s.value_counts().get('A', default=0)),\n",
    "        num_B=('Resultado', lambda s: s.value_counts().get('B', default=0)),\n",
    "        num_C=('Resultado', lambda s: s.value_counts().get('C', default=0)),\n",
    "        num_D=('Resultado', lambda s: s.value_counts().get('D', default=0)),\n",
    "        num_F=('Resultado', lambda s: s.value_counts().get('F', default=0))\n",
    "    )\n",
    "\n",
    "    df_ufabc_grades_prop_quad = df_ufabc_grades_prop_quad.sort_values(by='Ano', ascending=True).set_index('Ano', drop=True)\n",
    "\n",
    "    x_grades_prop_quad = [f'Prop. {grade[-1]}' for grade in df_ufabc_grades_prop_quad.columns]\n",
    "    y_grades_prop_quad = df_ufabc_grades_prop_quad.index\n",
    "    z_grades_prop_quad = df_ufabc_grades_prop_quad.apply(lambda s: s/s.sum(), axis=1).values\n",
    "    text_1 = df_ufabc_grades_prop_quad.apply(lambda s: np.round(s/s.sum()*100,1), axis=1).astype(str) + '%'\n",
    "    # text_1 = df_ufabc_grades_prop_quad.apply(lambda s: np.round(s/s.sum()*100,1), axis=1).astype(str) + '% (' + df_ufabc_grades_prop_quad.astype(str) + ')'\n",
    "    # text_1 = df_ufabc_grades_prop_quad.apply(lambda s: np.round(s/s.sum()*100,1), axis=1).astype(str) + '%<br>(' + df_ufabc_grades_prop_quad.astype(str) + ' de ' + pd.concat([df_ufabc_grades_prop_quad.sum(axis=1)]*5, axis=1).astype(str).rename(columns={c:cc for c, cc in zip(range(0, 5), df_ufabc_grades_prop_quad)}) + ')'\n",
    "\n",
    "    fig_ufabc_grades_prop_cum_quad.add_trace(go.Heatmap(x=x_grades_prop_quad, y=y_grades_prop_quad, z=z_grades_prop_quad, text=text_1, name=SUBPLOT_TITLES[col],\n",
    "                                                        texttemplate='%{text}', zhoverformat='.1%', hovertemplate='%{x} em %{y}<br>%{z:.2%}', # textfont_size=8,\n",
    "                                                        colorscale=HEATMAP_PALETTE, hoverinfo='x+y+z', colorbar_tickformat='.0%', zmin=0, zmax=1), row=1, col=col + 1)\n",
    "\n",
    "    \n",
    "    # df_ufabc_grades_cum_quad = df_tmp.groupby('Ano', as_index=False).agg(\n",
    "    #     cum_A=('Resultado', lambda s: s.value_counts().get('A', default=0)),\n",
    "    #     cum_B=('Resultado', lambda s: s.value_counts().get('B', default=0)),\n",
    "    #     cum_C=('Resultado', lambda s: s.value_counts().get('C', default=0)),\n",
    "    #     cum_D=('Resultado', lambda s: s.value_counts().get('D', default=0)),\n",
    "    #     cum_F=('Resultado', lambda s: s.value_counts().get('F', default=0)),\n",
    "    # )\n",
    "\n",
    "    # df_ufabc_grades_cum_quad = df_ufabc_grades_cum_quad.sort_values(by='Ano', ascending=True).set_index('Ano', drop=True)\n",
    "    # df_ufabc_grades_cum_quad = df_ufabc_grades_cum_quad.cumsum()\n",
    "\n",
    "    # x_grades_prop_cum_quad = [f'Prop. {grade[-1]}' for grade in df_ufabc_grades_cum_quad.columns]\n",
    "    # y_grades_prop_cum_quad = df_ufabc_grades_cum_quad.index\n",
    "    # z_grades_prop_cum_quad = df_ufabc_grades_cum_quad.apply(lambda s: s/s.sum(), axis=1).values\n",
    "    # text_2 = df_ufabc_grades_cum_quad.apply(lambda s: np.round(s/s.sum()*100, 1), axis=1).astype(str) + '% (' + df_ufabc_grades_cum_quad.astype(str) + ')'\n",
    "\n",
    "    # fig_ufabc_grades_prop_cum_quad.add_trace(go.Heatmap(x=x_grades_prop_cum_quad, y=y_grades_prop_cum_quad, z=z_grades_prop_cum_quad, name=SUBPLOT_TITLES[col],\n",
    "    #                                                     text=text_2, texttemplate='%{text}', hovertemplate='%{x} em %{y}<br>%{z:.2%}', zhoverformat='.1%', # textfont_size=8,\n",
    "    #                                                     colorscale=HEATMAP_PALETTE, hoverinfo='x+y+z', zmin=0, zmax=1,colorbar_tickformat='.0%'), row=2, col=col + 1)\n",
    "\n",
    "\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Proporção e quantidade de conceitos quadrimestre a quadrimestre na graduação'\n",
    "fig_ufabc_grades_prop_cum_quad.update_layout({'yaxis_title': 'Quadrimestre', 'height': 600, 'margin_t': 70, 'margin_b': 70})\n",
    "fig_ufabc_grades_prop_cum_quad.write_html('../assets/graphs/ufabc_grades_prop_cum_quad.html')\n",
    "fig_ufabc_grades_prop_cum_quad.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise comparativa da evolução temporal do CR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feito isso, agora podemos traçar a evolução temporal do CR (Coeficiente de Rendimento) ao longo dos quadrimestres. Este valor é calculado como uma média ponderada da equivalência numérica da nota pela quantidade de créditos:\n",
    "\n",
    "$$\n",
    "CR = \\frac{\\sum_i \\text{nota}_i \\cdot \\text{créditos}_i}{\\sum_i \\text{créditos}_i}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Quadrimestre</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>3.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>3.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>3.553846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>3.602410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>3.565657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>3.551724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>3.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>3.503497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>3.531250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>3.548023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>3.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>3.598086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>3.652893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>3.657588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>3.675277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>3.674912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>3.687075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quadrimestre        CR\n",
       "0    2017-05-01  3.176471\n",
       "1    2017-09-01  3.562500\n",
       "2    2018-01-01  3.540000\n",
       "3    2018-05-01  3.553846\n",
       "4    2018-09-01  3.602410\n",
       "5    2019-01-01  3.565657\n",
       "6    2019-05-01  3.551724\n",
       "7    2019-09-01  3.496241\n",
       "8    2020-09-01  3.503497\n",
       "9    2021-01-01  3.531250\n",
       "10   2021-05-01  3.548023\n",
       "11   2021-09-01  3.562500\n",
       "12   2022-01-01  3.598086\n",
       "13   2022-05-01  3.652893\n",
       "14   2022-09-01  3.657588\n",
       "15   2023-01-01  3.675277\n",
       "16   2023-05-01  3.674912\n",
       "17   2023-09-01  3.687075"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades_map = {  # Mapeamento numérico da nota para número\n",
    "    'A': 4,\n",
    "    'B': 3,\n",
    "    'C': 2,\n",
    "    'D': 1,\n",
    "    'F': 0\n",
    "}\n",
    "\n",
    "quad_map = {\n",
    "    '1': 1,  # Q1 -> 01 -> 04 (Janeiro até Abril)\n",
    "    '2': 5,  # Q2 -> 05 -> 08 (Maio -> Agosto)\n",
    "    '3': 9   # Q3 -> 09 -> 12 (Setembro -> Dezembro)\n",
    "}\n",
    "\n",
    "cr_series = df_ufabc.copy().dropna()\n",
    "cr_series['Nota ponderada'] = cr_series.apply(lambda s: s['Créditos'] * grades_map[s['Resultado']], axis=1)\n",
    "cr_series = cr_series.groupby('Ano', as_index=False)[['Créditos', 'Nota ponderada']].sum()\n",
    "cr_series[['Créditos acum', 'Nota ponderada acum']] = cr_series[['Créditos', 'Nota ponderada']].cumsum()\n",
    "cr_series['CR'] = cr_series.apply(lambda s: s['Nota ponderada acum']/s['Créditos acum'], axis=1)\n",
    "cr_series['Quadrimestre'] = cr_series['Ano'].apply(lambda s: pd.to_datetime(f'{quad_map[s.split('.')[1]]}/{s.split('.')[0]}')) \n",
    "cr_series = cr_series[['Quadrimestre', 'CR']]\n",
    "cr_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Quadrimestre</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>2.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>2.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>2.096386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2.101010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2.215517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2.270677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>2.251748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2.362500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>2.401130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2.432292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2.435407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>2.541322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>2.509728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2.549815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2.561837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>2.568027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quadrimestre        CR\n",
       "0    2017-05-01  2.529412\n",
       "1    2017-09-01  2.218750\n",
       "2    2018-01-01  2.160000\n",
       "3    2018-05-01  2.092308\n",
       "4    2018-09-01  2.096386\n",
       "5    2019-01-01  2.101010\n",
       "6    2019-05-01  2.215517\n",
       "7    2019-09-01  2.270677\n",
       "8    2020-09-01  2.251748\n",
       "9    2021-01-01  2.362500\n",
       "10   2021-05-01  2.401130\n",
       "11   2021-09-01  2.432292\n",
       "12   2022-01-01  2.435407\n",
       "13   2022-05-01  2.541322\n",
       "14   2022-09-01  2.509728\n",
       "15   2023-01-01  2.549815\n",
       "16   2023-05-01  2.561837\n",
       "17   2023-09-01  2.568027"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_prob_series = df_next.copy().dropna()\n",
    "cr_prob_series['Nota ponderada'] = cr_prob_series.apply(lambda s: s['Créditos'] * grades_map[s['Conceito provável']], axis=1)\n",
    "cr_prob_series = cr_prob_series.groupby('Ano', as_index=False)[['Créditos', 'Nota ponderada']].sum()\n",
    "cr_prob_series[['Créditos acum', 'Nota ponderada acum']] = cr_prob_series[['Créditos', 'Nota ponderada']].cumsum()\n",
    "cr_prob_series['CR'] = cr_prob_series.apply(lambda s: s['Nota ponderada acum']/s['Créditos acum'], axis=1)\n",
    "cr_prob_series['Quadrimestre'] = cr_prob_series['Ano'].apply(lambda s: pd.to_datetime(f'{quad_map[s.split('.')[1]]}/{s.split('.')[0]}')) \n",
    "cr_prob_series = cr_prob_series[['Quadrimestre', 'CR']]\n",
    "cr_prob_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Quadrimestre</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>1.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>1.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1.815385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>1.771084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1.898990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2.129310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2.172932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>2.104895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2.206250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>2.322034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2.359375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2.387560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>2.541322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>2.486381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2.553506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2.586572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>2.591837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quadrimestre        CR\n",
       "0    2017-05-01  2.411765\n",
       "1    2017-09-01  1.656250\n",
       "2    2018-01-01  1.820000\n",
       "3    2018-05-01  1.815385\n",
       "4    2018-09-01  1.771084\n",
       "5    2019-01-01  1.898990\n",
       "6    2019-05-01  2.129310\n",
       "7    2019-09-01  2.172932\n",
       "8    2020-09-01  2.104895\n",
       "9    2021-01-01  2.206250\n",
       "10   2021-05-01  2.322034\n",
       "11   2021-09-01  2.359375\n",
       "12   2022-01-01  2.387560\n",
       "13   2022-05-01  2.541322\n",
       "14   2022-09-01  2.486381\n",
       "15   2023-01-01  2.553506\n",
       "16   2023-05-01  2.586572\n",
       "17   2023-09-01  2.591837"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_mode_series = df_next.copy().dropna()\n",
    "cr_mode_series['Nota ponderada'] = cr_mode_series.apply(lambda s: s['Créditos'] * grades_map[s['Conceito moda']], axis=1)\n",
    "cr_mode_series = cr_mode_series.groupby('Ano', as_index=False)[['Créditos', 'Nota ponderada']].sum()\n",
    "cr_mode_series[['Créditos acum', 'Nota ponderada acum']] = cr_mode_series[['Créditos', 'Nota ponderada']].cumsum()\n",
    "cr_mode_series['CR'] = cr_mode_series.apply(lambda s: s['Nota ponderada acum']/s['Créditos acum'], axis=1)\n",
    "cr_mode_series['Quadrimestre'] = cr_mode_series['Ano'].apply(lambda s: pd.to_datetime(f'{quad_map[s.split('.')[1]]}/{s.split('.')[0]}')) \n",
    "cr_mode_series = cr_mode_series[['Quadrimestre', 'CR']]\n",
    "cr_mode_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotando as curvas sobrepostas, podemos fazer uma comparação da evolução temporal do meu CR com relação as situações de conceitos mais prováveis e conceitos moda:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_ufabc_cr_comp_series = go.Figure()\n",
    "\n",
    "fig_ufabc_cr_comp_series.add_trace(go.Scatter(x=cr_series['Quadrimestre'], y=cr_series['CR'], name=SUBPLOT_TITLES[0].replace('<br>&nbsp;', ''), \n",
    "                                              line_shape='spline', marker_color=BLUE, hovertemplate='%{y:.2f}', legendgroup=0))\n",
    "fig_ufabc_cr_comp_series.add_trace(go.Scatter(x=cr_prob_series['Quadrimestre'], y=cr_prob_series['CR'], name=SUBPLOT_TITLES[1], \n",
    "                                              line_shape='spline', marker_color=GREEN, hovertemplate='%{y:.2f}', legendgroup=1))\n",
    "fig_ufabc_cr_comp_series.add_trace(go.Scatter(x=cr_mode_series['Quadrimestre'], y=cr_mode_series['CR'], name=SUBPLOT_TITLES[2], \n",
    "                                              line_shape='spline', marker_color=BROWN, hovertemplate='%{y:.2f}', legendgroup=2))\n",
    "fig_ufabc_cr_comp_series.add_hline(y=4, line_color='green', line_dash='dash', annotation_text='Valor máximo')\n",
    "fig_ufabc_cr_comp_series.add_hline(y=0, line_color='red', line_dash='dash', annotation_text='Valor mínimo')\n",
    "\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Evolução temporal do CR'\n",
    "fig_ufabc_cr_comp_series.update_layout({'yaxis_title': 'CR', 'xaxis_title': 'Data', 'yaxis_range': [-0.25, 4.25], 'xaxis_range': ['2017-04-01', '2024-01-01'],\n",
    "                                        'hovermode': 'x unified',  'height': 500, 'legend_tracegroupgap': 10, 'legend_orientation': 'v', # 'legend_title': 'Curvas',\n",
    "                                        'margin_t': 30, 'margin_b': 60})\n",
    "fig_ufabc_cr_comp_series.write_html('../assets/graphs/ufabc_cr_comp_series.html')\n",
    "fig_ufabc_cr_comp_series.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise dos dados do CIn-UFPE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leitura dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogamente ao que fizemos antes, como primeiro passo vamos ler os dados das notas do CIn-UFPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Ano</th>\n",
       "      <th>Código</th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Situação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0068</td>\n",
       "      <td>APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA</td>\n",
       "      <td>9.9</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0066</td>\n",
       "      <td>APRENDIZAGEM DE MÁQUINA I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0067</td>\n",
       "      <td>APRENDIZAGEM DE MÁQUINA II</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0069</td>\n",
       "      <td>ARQUITETURA BIG DATA E ANALYTICS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0061</td>\n",
       "      <td>ARQUITETURA DE SOFTWARE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0065</td>\n",
       "      <td>ESTATÍSTICA AVANÇADA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0071</td>\n",
       "      <td>FAMILIARIZAÇÃO AERONÁUTICA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0064</td>\n",
       "      <td>INTRODUÇÃO A CIÊNCIA DE DADOS</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0070</td>\n",
       "      <td>PROJETO DE CIÊNCIA DE DADOS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0062</td>\n",
       "      <td>QUALIDADE DE SOFTWARE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0060</td>\n",
       "      <td>REQUISITOS DE SOFTWARE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0063</td>\n",
       "      <td>TÓPICOS AVANÇADOS</td>\n",
       "      <td>9.8</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>LAT0001</td>\n",
       "      <td>TRABALHO FINAL DE CURSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ano   Código                             Disciplina  Resultado  \\\n",
       "0   2024.1  CIN0068  APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA        9.9   \n",
       "1   2024.1  CIN0066              APRENDIZAGEM DE MÁQUINA I       10.0   \n",
       "2   2024.1  CIN0067             APRENDIZAGEM DE MÁQUINA II       10.0   \n",
       "3   2024.1  CIN0069       ARQUITETURA BIG DATA E ANALYTICS       10.0   \n",
       "4   2024.1  CIN0061                ARQUITETURA DE SOFTWARE       10.0   \n",
       "5   2024.1  CIN0065                   ESTATÍSTICA AVANÇADA        9.5   \n",
       "6   2024.1  CIN0071             FAMILIARIZAÇÃO AERONÁUTICA       10.0   \n",
       "7   2024.1  CIN0064          INTRODUÇÃO A CIÊNCIA DE DADOS        9.5   \n",
       "8   2024.1  CIN0070            PROJETO DE CIÊNCIA DE DADOS       10.0   \n",
       "9   2024.1  CIN0062                  QUALIDADE DE SOFTWARE       10.0   \n",
       "10  2024.1  CIN0060                 REQUISITOS DE SOFTWARE        9.5   \n",
       "11  2024.1  CIN0063                      TÓPICOS AVANÇADOS        9.8   \n",
       "12  2024.1  LAT0001                TRABALHO FINAL DE CURSO       10.0   \n",
       "\n",
       "    Situação  \n",
       "0   APROVADO  \n",
       "1   APROVADO  \n",
       "2   APROVADO  \n",
       "3   APROVADO  \n",
       "4   APROVADO  \n",
       "5   APROVADO  \n",
       "6   APROVADO  \n",
       "7   APROVADO  \n",
       "8   APROVADO  \n",
       "9   APROVADO  \n",
       "10  APROVADO  \n",
       "11  APROVADO  \n",
       "12  APROVADO  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufpe = pd.read_csv('./data/notas-ufpe.csv', dtype={'Ano': str}, sep=';', decimal=',')\n",
    "df_ufpe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com base nestes dados, verificamos que há redundância quanto as disciplinas \"Projeto de Ciência de Dados\" e \"Trabalho Final de Curso\", visto que estes referem-se a mesma coisa. Isto posto, vamos remover o registro com \"Projeto de Ciência de Dados\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Ano</th>\n",
       "      <th>Código</th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Situação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0068</td>\n",
       "      <td>APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA</td>\n",
       "      <td>9.9</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0066</td>\n",
       "      <td>APRENDIZAGEM DE MÁQUINA I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0067</td>\n",
       "      <td>APRENDIZAGEM DE MÁQUINA II</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0069</td>\n",
       "      <td>ARQUITETURA BIG DATA E ANALYTICS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0061</td>\n",
       "      <td>ARQUITETURA DE SOFTWARE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0065</td>\n",
       "      <td>ESTATÍSTICA AVANÇADA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0071</td>\n",
       "      <td>FAMILIARIZAÇÃO AERONÁUTICA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0064</td>\n",
       "      <td>INTRODUÇÃO A CIÊNCIA DE DADOS</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0062</td>\n",
       "      <td>QUALIDADE DE SOFTWARE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0060</td>\n",
       "      <td>REQUISITOS DE SOFTWARE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0063</td>\n",
       "      <td>TÓPICOS AVANÇADOS</td>\n",
       "      <td>9.8</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>LAT0001</td>\n",
       "      <td>TRABALHO FINAL DE CURSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ano   Código                             Disciplina  Resultado  \\\n",
       "0   2024.1  CIN0068  APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA        9.9   \n",
       "1   2024.1  CIN0066              APRENDIZAGEM DE MÁQUINA I       10.0   \n",
       "2   2024.1  CIN0067             APRENDIZAGEM DE MÁQUINA II       10.0   \n",
       "3   2024.1  CIN0069       ARQUITETURA BIG DATA E ANALYTICS       10.0   \n",
       "4   2024.1  CIN0061                ARQUITETURA DE SOFTWARE       10.0   \n",
       "5   2024.1  CIN0065                   ESTATÍSTICA AVANÇADA        9.5   \n",
       "6   2024.1  CIN0071             FAMILIARIZAÇÃO AERONÁUTICA       10.0   \n",
       "7   2024.1  CIN0064          INTRODUÇÃO A CIÊNCIA DE DADOS        9.5   \n",
       "9   2024.1  CIN0062                  QUALIDADE DE SOFTWARE       10.0   \n",
       "10  2024.1  CIN0060                 REQUISITOS DE SOFTWARE        9.5   \n",
       "11  2024.1  CIN0063                      TÓPICOS AVANÇADOS        9.8   \n",
       "12  2024.1  LAT0001                TRABALHO FINAL DE CURSO       10.0   \n",
       "\n",
       "    Situação  \n",
       "0   APROVADO  \n",
       "1   APROVADO  \n",
       "2   APROVADO  \n",
       "3   APROVADO  \n",
       "4   APROVADO  \n",
       "5   APROVADO  \n",
       "6   APROVADO  \n",
       "7   APROVADO  \n",
       "9   APROVADO  \n",
       "10  APROVADO  \n",
       "11  APROVADO  \n",
       "12  APROVADO  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufpe = df_ufpe[df_ufpe['Disciplina'] != 'PROJETO DE CIÊNCIA DE DADOS']\n",
    "df_ufpe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesta especialização, as matérias foram cursadas uma por vez. Todavia, como não temos a informação da ordem no Sigaa, vamos adicionar isto manualmente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Ano</th>\n",
       "      <th>Código</th>\n",
       "      <th>Disciplina</th>\n",
       "      <th>Resultado</th>\n",
       "      <th>Situação</th>\n",
       "      <th>Ordem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0060</td>\n",
       "      <td>REQUISITOS DE SOFTWARE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0071</td>\n",
       "      <td>FAMILIARIZAÇÃO AERONÁUTICA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0061</td>\n",
       "      <td>ARQUITETURA DE SOFTWARE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0062</td>\n",
       "      <td>QUALIDADE DE SOFTWARE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0063</td>\n",
       "      <td>TÓPICOS AVANÇADOS</td>\n",
       "      <td>9.8</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0064</td>\n",
       "      <td>INTRODUÇÃO A CIÊNCIA DE DADOS</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0065</td>\n",
       "      <td>ESTATÍSTICA AVANÇADA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0066</td>\n",
       "      <td>APRENDIZAGEM DE MÁQUINA I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0067</td>\n",
       "      <td>APRENDIZAGEM DE MÁQUINA II</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0069</td>\n",
       "      <td>ARQUITETURA BIG DATA E ANALYTICS</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>CIN0068</td>\n",
       "      <td>APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA</td>\n",
       "      <td>9.9</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024.1</td>\n",
       "      <td>LAT0001</td>\n",
       "      <td>TRABALHO FINAL DE CURSO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>APROVADO</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ano   Código                             Disciplina  Resultado  \\\n",
       "0   2024.1  CIN0060                 REQUISITOS DE SOFTWARE        9.5   \n",
       "1   2024.1  CIN0071             FAMILIARIZAÇÃO AERONÁUTICA       10.0   \n",
       "2   2024.1  CIN0061                ARQUITETURA DE SOFTWARE       10.0   \n",
       "3   2024.1  CIN0062                  QUALIDADE DE SOFTWARE       10.0   \n",
       "4   2024.1  CIN0063                      TÓPICOS AVANÇADOS        9.8   \n",
       "5   2024.1  CIN0064          INTRODUÇÃO A CIÊNCIA DE DADOS        9.5   \n",
       "6   2024.1  CIN0065                   ESTATÍSTICA AVANÇADA        9.5   \n",
       "7   2024.1  CIN0066              APRENDIZAGEM DE MÁQUINA I       10.0   \n",
       "8   2024.1  CIN0067             APRENDIZAGEM DE MÁQUINA II       10.0   \n",
       "9   2024.1  CIN0069       ARQUITETURA BIG DATA E ANALYTICS       10.0   \n",
       "10  2024.1  CIN0068  APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA        9.9   \n",
       "11  2024.1  LAT0001                TRABALHO FINAL DE CURSO       10.0   \n",
       "\n",
       "    Situação  Ordem  \n",
       "0   APROVADO      1  \n",
       "1   APROVADO      2  \n",
       "2   APROVADO      3  \n",
       "3   APROVADO      4  \n",
       "4   APROVADO      5  \n",
       "5   APROVADO      6  \n",
       "6   APROVADO      7  \n",
       "7   APROVADO      8  \n",
       "8   APROVADO      9  \n",
       "9   APROVADO     10  \n",
       "10  APROVADO     11  \n",
       "11  APROVADO     12  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjects_order = {\n",
    "    'REQUISITOS DE SOFTWARE': 1,\n",
    "    'FAMILIARIZAÇÃO AERONÁUTICA': 2,\n",
    "    'ARQUITETURA DE SOFTWARE': 3,\n",
    "    'QUALIDADE DE SOFTWARE': 4, \n",
    "    'TÓPICOS AVANÇADOS': 5, \n",
    "    'INTRODUÇÃO A CIÊNCIA DE DADOS': 6, \n",
    "    'ESTATÍSTICA AVANÇADA': 7, \n",
    "    'APRENDIZAGEM DE MÁQUINA I': 8, \n",
    "    'APRENDIZAGEM DE MÁQUINA II': 9,\n",
    "    'ARQUITETURA BIG DATA E ANALYTICS': 10, \n",
    "    'APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA': 11,\n",
    "    'TRABALHO FINAL DE CURSO': 12\n",
    "}\n",
    "\n",
    "df_ufpe['Ordem'] = df_ufpe['Disciplina'].apply(lambda s: subjects_order[s])\n",
    "df_ufpe = df_ufpe.sort_values('Ordem').reset_index(drop=True)\n",
    "df_ufpe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feito isto, como primeiro passo vamos ver a distribuição das notas de forma visual. Como, neste caso, não temos nenhuma outra base para compara, vamos trazer as representações apenas para as minhas notas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise da distribuição das notas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como temos poucos dados, o histograma não mostra-se uma representação tão valiosa e informativa. Isto posto, para verificar a distribuiçõ das notas usaremos apenas um boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_ufpe_grades_dist = go.Figure()\n",
    "fig_ufpe_grades_dist.add_trace(go.Box(y=df_ufpe['Resultado'], x0='Distribuição das minhas notas', name='Distribuição das minhas notas', boxmean=True,\n",
    "                                      boxpoints='all', jitter=1, meta=df_ufpe['Disciplina'], hovertemplate='%{meta}: %{y:.2f}', marker_color=BLUE))\n",
    "\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Distribuição das notas na pós-graduação'\n",
    "fig_ufpe_grades_dist.update_layout({'yaxis_title': 'Nota', 'height': 450,'margin_t': 50, 'margin_b': 30})\n",
    "fig_ufpe_grades_dist.write_html('../assets/graphs/ufpe_grades_dist.html')\n",
    "fig_ufpe_grades_dist.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nota matéria a matéria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como, neste caso, são poucas matérias (12), conseguimos sumarizar os resultados um gráfico de barras que mostra cada nota individualmente. Para facilitar na legibilidade, vamos criar um mapeamento com o novo nome das matérias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Disciplina\n",
       "REQUISITOS DE SOFTWARE                   1\n",
       "FAMILIARIZAÇÃO AERONÁUTICA               1\n",
       "ARQUITETURA DE SOFTWARE                  1\n",
       "QUALIDADE DE SOFTWARE                    1\n",
       "TÓPICOS AVANÇADOS                        1\n",
       "INTRODUÇÃO A CIÊNCIA DE DADOS            1\n",
       "ESTATÍSTICA AVANÇADA                     1\n",
       "APRENDIZAGEM DE MÁQUINA I                1\n",
       "APRENDIZAGEM DE MÁQUINA II               1\n",
       "ARQUITETURA BIG DATA E ANALYTICS         1\n",
       "APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA    1\n",
       "TRABALHO FINAL DE CURSO                  1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ufpe['Disciplina'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subjects_split_map = {\n",
    "    'REQUISITOS DE SOFTWARE': 'REQUISITOS DE<br>SOFTWARE',\n",
    "    'FAMILIARIZAÇÃO AERONÁUTICA': 'FAMILIARIZAÇÃO<br>AERONÁUTICA',\n",
    "    'ARQUITETURA DE SOFTWARE': 'ARQUITETURA DE<br>SOFTWARE',\n",
    "    'QUALIDADE DE SOFTWARE': 'QUALIDADE DE<br>SOFTWARE', \n",
    "    'TÓPICOS AVANÇADOS': 'TÓPICOS<br>AVANÇADOS',\n",
    "    'INTRODUÇÃO A CIÊNCIA DE DADOS': 'INTRODUÇÃO<br>A CIÊNCIA DE<br>DADOS',  \n",
    "    'ESTATÍSTICA AVANÇADA': 'ESTATÍSTICA<br>AVANÇADA',  \n",
    "    'APRENDIZAGEM DE MÁQUINA I': 'APRENDIZAGEM<br>DE MÁQUINA I',\n",
    "    'APRENDIZAGEM DE MÁQUINA II': 'APRENDIZAGEM<br>DE MÁQUINA II',\n",
    "    'ARQUITETURA BIG DATA E ANALYTICS': 'ARQUITETURA BIG<br>DATA E ANALYTICS',\n",
    "    'APLICAÇÕES DE APRENDIZAGEM DE MÁQUINA': 'APLICAÇÕES DE<br>APRENDIZAGEM DE<br>MÁQUINA',\n",
    "    'TRABALHO FINAL DE CURSO': 'TRABALHO FINAL<br>DE CURSO'\n",
    "}\n",
    "\n",
    "fig_ufpe_grades_values = go.Figure()\n",
    "fig_ufpe_grades_values.add_trace(go.Bar(x=df_ufpe['Disciplina'].map(subjects_split_map), y=df_ufpe['Resultado'], name='Notas por matéria',\n",
    "                                        marker_color=BLUE, hovertemplate='%{x}: %{y:.1f}'))\n",
    "fig_ufpe_grades_values.add_hline(y=df_ufpe['Resultado'].mean(), annotation_text=f'Média: {df_ufpe['Resultado'].mean():.2f}',\n",
    "                                 annotation_position='top right', annotation_y=df_ufpe['Resultado'].mean() + 0.2, line_dash='dash')\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Notas por matéria da pós-graduação'\n",
    "fig_ufpe_grades_values.update_layout({'yaxis_title': 'Nota', 'xaxis_title': 'Matéria', 'bargap': 0.3, 'yaxis_dtick': 1, 'height': 500,\n",
    "                                      'xaxis_tickangle': 45, 'xaxis_tickfont_size': 11, 'yaxis_range': [0, 10.5], 'margin_t': 40, 'margin_b': 60})\n",
    "fig_ufpe_grades_values.write_html('../assets/graphs/ufpe_grades_values.html')\n",
    "fig_ufpe_grades_values.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evolução temporal da nota média"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para verificarmos a evolução temporal da nota média, podemos aplicar o método expanding com a média:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Ordem</th>\n",
       "      <th>Resultado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>9.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>9.787500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9.811111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>9.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>9.836364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ordem  Resultado\n",
       "0       1   9.500000\n",
       "1       2   9.750000\n",
       "2       3   9.833333\n",
       "3       4   9.875000\n",
       "4       5   9.860000\n",
       "5       6   9.800000\n",
       "6       7   9.757143\n",
       "7       8   9.787500\n",
       "8       9   9.811111\n",
       "9      10   9.830000\n",
       "10     11   9.836364\n",
       "11     12   9.850000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_grade_evo = df_ufpe[['Ordem', 'Resultado']]\n",
    "mean_grade_evo['Resultado'] = mean_grade_evo['Resultado'].expanding().mean()\n",
    "mean_grade_evo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para este caso, podemos usar uma representação semelhante aquela utilizada para a evolução temporal do CR da graduação:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_ufpe_mean_grade_evo = go.Figure()\n",
    "\n",
    "fig_ufpe_mean_grade_evo.add_trace(go.Scatter(x=mean_grade_evo['Ordem'], y=mean_grade_evo['Resultado'], name='Meus conceitos', \n",
    "                                             line_shape='spline', hovertemplate='Nota média: %{y:.2f}', marker_color=BLUE))\n",
    "fig_ufpe_mean_grade_evo.add_hline(y=10, line_color='green', line_dash='dash', annotation_text='Valor máximo')\n",
    "fig_ufpe_mean_grade_evo.add_hline(y=0, line_color='red', line_dash='dash', annotation_text='Valor mínimo')\n",
    "\n",
    "# Tirando título para ficar mais clean no HTML. Caso seja necessário add o título de novo: 'Evolução temporal da nota média da pós-graduação'\n",
    "fig_ufpe_mean_grade_evo.update_layout({'height': 500, 'xaxis_range': [0.5, 12.5], 'xaxis_dtick': 1, 'yaxis_title': 'Nota média', \n",
    "                                       'xaxis_title': 'Índice da matéria', 'yaxis_range': [9.4, 10.05], 'margin_t': 30, \n",
    "                                       'margin_b': 60})\n",
    "fig_ufpe_mean_grade_evo.write_html('../assets/graphs/ufpe_mean_grade_evos.html')\n",
    "fig_ufpe_mean_grade_evo.update_layout({'width': 1200} if RENDERER is not None else {}).show(RENDERER)  # Ajusta largura se não estiver como interativo"
   ]
  }
 ],
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   "display_name": "githubpages-env",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
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 },
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}