Desafio_Final_Murilo.ipynb

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    "<h1>Sumário<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Joana,-Pandas-e-Computadores\" data-toc-modified-id=\"Joana,-Pandas-e-Computadores-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Joana, Pandas e Computadores</a></span><ul class=\"toc-item\"><li><span><a href=\"#Importações-de-Bibliotecas\" data-toc-modified-id=\"Importações-de-Bibliotecas-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Importações de Bibliotecas</a></span></li><li><span><a href=\"#Análise-Exploratória\" data-toc-modified-id=\"Análise-Exploratória-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Análise Exploratória</a></span><ul class=\"toc-item\"><li><span><a href=\"#Parte-I:-Os-Experimentos\" data-toc-modified-id=\"Parte-I:-Os-Experimentos-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Parte I: Os Experimentos</a></span></li><li><span><a href=\"#Parte-II:-Células-e-Genes\" data-toc-modified-id=\"Parte-II:-Células-e-Genes-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Parte II: Células e Genes</a></span></li><li><span><a href=\"#Parte-III:-Mecanismos-de-Ação\" data-toc-modified-id=\"Parte-III:-Mecanismos-de-Ação-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Parte III: Mecanismos de Ação</a></span></li></ul></li></ul></li><li><span><a href=\"#Hipóteses,-Elefantes-e-Anti-inflamatórios\" data-toc-modified-id=\"Hipóteses,-Elefantes-e-Anti-inflamatórios-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Hipóteses, Elefantes e Anti-inflamatórios</a></span><ul class=\"toc-item\"><li><span><a href=\"#Aprendizado-de-Máquina\" data-toc-modified-id=\"Aprendizado-de-Máquina-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Aprendizado de Máquina</a></span><ul class=\"toc-item\"><li><span><a href=\"#Preparando-os-Dados\" data-toc-modified-id=\"Preparando-os-Dados-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Preparando os Dados</a></span></li><li><span><a href=\"#Modelos-e-Resultados\" data-toc-modified-id=\"Modelos-e-Resultados-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Modelos e Resultados</a></span></li></ul></li></ul></li><li><span><a href=\"#Conclusões\" data-toc-modified-id=\"Conclusões-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Conclusões</a></span></li><li><span><a href=\"#Fontes\" data-toc-modified-id=\"Fontes-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Fontes</a></span><ul class=\"toc-item\"><li><span><a href=\"#Citações\" data-toc-modified-id=\"Citações-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Citações</a></span></li><li><span><a href=\"#Inspirações\" data-toc-modified-id=\"Inspirações-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Inspirações</a></span></li><li><span><a href=\"#Documentações\" data-toc-modified-id=\"Documentações-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Documentações</a></span></li></ul></li><li><span><a href=\"#Autor-e-Contato\" data-toc-modified-id=\"Autor-e-Contato-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Autor e Contato</a></span></li></ul></div>"
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    "> **APRESENTAÇÃO**\n",
    ">\n",
    "> [Desafio Final](https://github.com/alura-cursos/imersao-dados-desafio-final) de Murilo Amaral para a terceira edição da [imersão dados](https://github.com/alura-cursos/imersaodados3) da Alura, submetido em 9 de maio de 2021.\n",
    ">\n",
    "> Para mais informações, veja o [repositório](https://github.com/mumaral/desafio-final-imersao-dados-alura) deste projeto no github.\n",
    ">\n",
    "> **Resumo**\n",
    ">\n",
    "> O projeto está escrito em uma linguagem narrativa, que lembra algo como uma crônica, expondo uma conversa entre Clara (mãe) e Joana (sua filha) numa noite de véspera do dia das mãe. Clara trabalha como cientista de dados na área de descobrimento de medicamentos e acaba ensinando e explicando para Joana um pouco do seu trabalho durante a conversa. As células de código (em Python) vão sendo mostradas acompanhando a interação entre mãe e filha, como se fosse a tela do computador da própria Clara.\n",
    ">\n",
    "> **Objetivos** \n",
    ">\n",
    "> - Tentar tornar mais palatável uma apresentação de informações que mistura temas bastante complicados por si só, como farmacologia, genética, estatística e computação. Por isso a escolha da figura da criança e o desafio de explicar conceitos complexos de forma simples.\n",
    ">\n",
    "> - Explorar uma faceta mais sutil da ciência de dados: o raciocínio dentro da mente humana."
   ]
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   "source": [
    "# Joana, Pandas e Computadores"
   ]
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    "— Mamãe, mamãe, olha o que eu aprendi a fazer !!\n",
    "\n",
    "Joana entrara na sala pulando num pé só enquanto tentava, com o maior cuidado e atenção do mundo, equilibrar um livro na cabeça.\n",
    "\n",
    "— Nossa Jojô, que legal. — disse Clara sem tirar os olhos da tela.\n",
    "\n",
    "— Mamãe, olha rápido senão o livro vai cair !\n",
    "Ao perceber que Joana não estava disposta a perder a atenção da mãe para o computador, Clara desiste e cede ao pedido da filha.\n",
    "\n",
    "— Uau, como você melhorou ! Na semana passada você só conseguia equilibrar o livro parada, e agora já tá até pulando.\n",
    "\n",
    "— Pois é mamãe, to aprendendo bem rápido né?! Agora é sua vez, quero ver se você consegue fazer igual.\n",
    "\n",
    "— Ah, filhinha, a mamãe tá um pouco ocupada agora, a gente não pode brincar depois?\n",
    "\n",
    "— Ocupada com o quê? O que você tá fazendo no computador?\n",
    "\n",
    "— Preciso entregar algumas análises pra segunda feira, vamos ter uma reunião importante no trabalho.\n",
    "\n",
    "Clara trabalhava na área de descobrimento de novos fármacos *(drug discovery)* como cientista de dados e, neste final de semana, estava buscando compreender melhor uma grande base de dados envolvendo testes de determinadas drogas e seus impactos em células e genes, com o objetivo de levar alguns resultados a serem discutidos com seu time no início da semana. Além do mais, era véspera do dia das mãe, e ela queria acabar nesta noite para ter o dia seguinte livre.\n",
    "\n",
    "— Entendi mamãe... E porque tá escrito pandas aqui nessa linha? — Joana colocou o dedo na tela cheia de palavras coloridas — Você tá escrevendo uma historinha? Ontem eu vi um desenho que contava a história do panda Pupi, eu gostei muito muito muito.\n",
    "\n",
    "— Hahaha, não filha, não tô escrevendo uma história não, pandas é o nome de um pacotinho cheio de códigos que eu to usando pra facilitar minhas análises.\n",
    "\n",
    "— E você tá analisando o que?\n",
    "\n",
    "Se dando por vencida, Clara decidiu mudar de estratégia.\n",
    "\n",
    "— Vem cá que vou te mostrar. — disse a mãe enquanto colocava Joana sentada entre suas pernas — Tá vendo essas primeiras linhas aqui?"
   ]
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   "source": [
    "## Importações de Bibliotecas"
   ]
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    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_style(\"white\")"
   ]
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   "source": [
    "— Nelas eu tô dizendo quais bibliotecas eu vou usar no meu código. Mas não são iguais à biblioteca da sua escola. Essas aqui servem pra guardar um monte de palavras chaves e linhas de código, que servem pro computador saber o que ele tem que fazer quando eu digitar alguma destas palavras. Pandas é uma dessas bibliotecas, é um nome engraçadinho que escolheram, e vem da abreviação de *Panel Data* [[1]](https://www.dlr.de/sc/Portaldata/15/Resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf), que é um termo da estatística.\n",
    "\n",
    "— Entendi... e o que o seu panda ta falando pro computador?\n",
    "\n",
    "— Depende do que eu digitar, olha só:"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Análise Exploratória"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5c7be17",
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   "outputs": [
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       "      <td>0.0743</td>\n",
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       "      <td>-0.0323</td>\n",
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       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
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       "      <td>com_droga</td>\n",
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       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
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       "             id tratamento  tempo dose      droga     g-0     g-1     g-2  \\\n",
       "0  id_000644bb2  com_droga     24   D1  b68db1d53  1.0620  0.5577 -0.2479   \n",
       "1  id_000779bfc  com_droga     72   D1  df89a8e5a  0.0743  0.4087  0.2991   \n",
       "2  id_000a6266a  com_droga     48   D1  18bb41b2c  0.6280  0.5817  1.5540   \n",
       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "      g-3     g-4  ...    c-90    c-91    c-92    c-93    c-94    c-95  \\\n",
       "0 -0.6208 -0.1944  ...  0.2862  0.2584  0.8076  0.5523 -0.1912  0.6584   \n",
       "1  0.0604  1.0190  ... -0.4265  0.7543  0.4708  0.0230  0.2957  0.4899   \n",
       "2 -0.0764 -0.0323  ... -0.7250 -0.6297  0.6103  0.0223 -1.3240 -0.3174   \n",
       "3  0.5288  4.0620  ... -2.0990 -0.6441 -5.6300 -1.3780 -0.8632 -1.2880   \n",
       "4  0.6919  1.4180  ...  0.0042  0.0048  0.6670  1.0690  0.5523 -0.3031   \n",
       "\n",
       "     c-96    c-97    c-98    c-99  \n",
       "0 -0.3981  0.2139  0.3801  0.4176  \n",
       "1  0.1522  0.1241  0.6077  0.7371  \n",
       "2 -0.6417 -0.2187 -1.4080  0.6931  \n",
       "3 -1.6210 -0.8784 -0.3876 -0.8154  \n",
       "4  0.1094  0.2885 -0.3786  0.7125  \n",
       "\n",
       "[5 rows x 877 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
    "# O arquivo tem 50MB. Para executar mais de uma vez, \n",
    "# é preferível fazer o download do arquivo acima e executar localmente\n",
    "# url_dados = '../imersaodados3/dados/dados_experimentos.zip'\n",
    "\n",
    "# a abreviação df é comum ao se utilizar o pandas, e se refere a dataframe\n",
    "df = pd.read_csv(url_dados, compression = 'zip')\n",
    "\n",
    "# mostrando apenas as primeiras 5 linhas \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca51f7a",
   "metadata": {},
   "source": [
    "— Aqui eu to chamando o pandas pra me ajudar a ler esse monte de letra e número, e depois eu peço pra ele me mostrar só as primeiras cinco linhas, tá vendo?\n",
    "\n",
    "— Mas só tem cinco linhas ?\n",
    "\n",
    "— Não, tem muitas linhas, tá vendo aqui embaixo escrito *(23814, 877)*? Isso diz pra gente que tem 23814 linhas e 877 colunas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8413e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23814, 877)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68410a7",
   "metadata": {},
   "source": [
    "— Nossa mamãe, mas isso é muita coisa! Como você consegue ver tanta coisa assim? Você tem superpoderes? \n",
    "\n",
    "— Não consigo filha, é muita coisa mesmo. Por isso eu peço ajuda pro computador e pro pandas. Eles são muitos bons em fazer contas e tarefas repetitivas, mas não são tão bons assim em entender o que tá acontecendo, eu preciso explicar pra eles o que significa cada palavra, escrevendo de um jeito que eles entendam. Esses dados, por exemplo, são de experimentos feitos pra descobrir em quais situações algumas drogas podem ser úteis pra tratar nós humanos.\n",
    "\n",
    "Joana franziu a testa e olhou para Clara com cara de preocupação.\n",
    "\n",
    "— Droga?? Mas isso não é uma coisa ruim?\n",
    "\n",
    "— Hahah, não é esse significado de droga, filha. Você pode entender droga aqui como remédio. Na verdade a palavra droga tem origem lá na idade média e significava a substância que a gente pode usar pra criar um remédio, um medicamento [[2]](http://www.jmrezende.com.br/droga.htm). \n",
    "\n",
    "— A tá... Então nessa tabela o pessoal testou bastante remédio?\n",
    "\n",
    "— Muitos, filha. Dá até pra gente perguntar quantos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5aa0226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de drogas diferentes testadas: 3289\n"
     ]
    }
   ],
   "source": [
    "print(\"Quantidade de drogas diferentes testadas: %d\" % (len(df.droga.value_counts())))\n",
    "#print(\"Destas %d drogas, a que aparece mais vezes é: %s\" % (len(df.droga.value_counts()), df.droga.value_counts().index[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845324e3",
   "metadata": {},
   "source": [
    "— Viu, então os 23814 experimentos testaram 3289 drogas. O objetivo é, por exemplo, tentar descobrir quais delas podem ser úteis pra quando voce tem dor de garganta, e quais podem ser úteis pra quando você cai de bicicleta.\n",
    "\n",
    "— Ai... É muito número pra mim. Não sei se eu tô entendendo.\n",
    "\n",
    "— Não é tão confuso não, vou te explicar. Dá pra dividir a tabela em alguns pedaços, vamos começar com as primeiras 5 colunas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4698e81",
   "metadata": {},
   "source": [
    "### Parte I: Os Experimentos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e28763b1",
   "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>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>b68db1d53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_000779bfc</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>df89a8e5a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_000a6266a</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>18bb41b2c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>8c7f86626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D2</td>\n",
       "      <td>7cbed3131</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>23809</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>df1d0a5a1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23810</th>\n",
       "      <td>id_fffb70c0c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>ecf3b6b74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23813</th>\n",
       "      <td>id_ffffdd77b</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>972f41291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23814 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose      droga\n",
       "0      id_000644bb2     com_droga     24   D1  b68db1d53\n",
       "1      id_000779bfc     com_droga     72   D1  df89a8e5a\n",
       "2      id_000a6266a     com_droga     48   D1  18bb41b2c\n",
       "3      id_0015fd391     com_droga     48   D1  8c7f86626\n",
       "4      id_001626bd3     com_droga     72   D2  7cbed3131\n",
       "...             ...           ...    ...  ...        ...\n",
       "23809  id_fffb1ceed     com_droga     24   D2  df1d0a5a1\n",
       "23810  id_fffb70c0c     com_droga     24   D2  ecf3b6b74\n",
       "23811  id_fffc1c3f4  com_controle     48   D2  cacb2b860\n",
       "23812  id_fffcb9e7c     com_droga     24   D1  8b87a7a83\n",
       "23813  id_ffffdd77b     com_droga     72   D1  972f41291\n",
       "\n",
       "[23814 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtrando para exibir apenas as 5 primeiras colunas\n",
    "\n",
    "df.loc[:, 'id':'droga']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1882b866",
   "metadata": {},
   "source": [
    "— A primeira coluna, tem um identificador (id) que funciona um como uma digital de cada linha, igual a digital do seu dedão da mão direita. Isso existe pra não confundir as linhas, ou seja, cada experimento é único. E porque é único? Porque cada um tem um combinação dos valores das colunas tratamento, tempo, dose e droga que é só dele. Olha aqui na primeira linha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7059db40",
   "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>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>b68db1d53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id tratamento  tempo dose      droga\n",
       "0  id_000644bb2  com_droga     24   D1  b68db1d53"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostrando apenas a primeira linha e as 5 primeiras colunas\n",
    "\n",
    "df.loc[:0, 'id':'droga']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "015a227a",
   "metadata": {},
   "source": [
    "— Isso aqui tá dizendo pra gente que o experimento chamado \"id_000644bb2\" foi feito com uma quantidade 'D1' da droga \"b68db1d53\" e medindo os efeitos após 24 horas. Voce pode entender o nome na primeira coluna como um apelido que só aquele conjunto de informações tem. Se você mudar qualquer coisa no experimento, ele precisa de outro nome.\n",
    "\n",
    "— Aaaa, agora eu entendi, mamãe. Então se eu procurar por esses mesmos valores, mas só medir o efeito depois de 6 horas, ao invés de 24 horas, vai ter uma outra linha, com um outro apelido pra chamar esse experimento?\n",
    "\n",
    "— Isso Jojô, seu raciocínio tá certinho! Só que agora a gente precisa ver quais são os tempos que foram medidos. Vamos descobrir se tem algum teste medido após 6 horas?\n",
    "\n",
    "— O panda sabe responder isso pra gente?\n",
    "\n",
    "— Sabe sim, é só a gente perguntar certinho:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc3bdc0f",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Olá Joana, os valores da coluna 'tempo' presentes na tabela são:\n",
      "\n",
      "- 24 horas, que aparece 7772 vezes\n",
      "- 48 horas, que aparece 8250 vezes\n",
      "- 72 horas, que aparece 7792 vezes\n",
      "\n",
      "No gráfico abaixo fica mais fácil de visualizar:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### PREPARANDO DADOS DA COLUNA TEMPO PARA IMPRIMIR GRÁFICO #####\n",
    "\n",
    "# Criando dicionário a partir dos valores de tempo presentes nos dados\n",
    "dicionario_tempo = dict(df.tempo.value_counts())\n",
    "\n",
    "print (\"Olá Joana, os valores da coluna 'tempo' presentes na tabela são:\\n\")\n",
    "\n",
    "# ordenando os numeros em ordem crescente e imprimindo-os\n",
    "\n",
    "for item in sorted(dicionario_tempo): \n",
    "    print(\"- %d horas, que aparece %d vezes\" % (item, dicionario_tempo[item]))\n",
    "    \n",
    "    \n",
    "print(\"\\nNo gráfico abaixo fica mais fácil de visualizar:\")\n",
    "    \n",
    "    \n",
    "\n",
    "    \n",
    "######### GRÁFICO DE BARRAS #########\n",
    "\n",
    "# Definindo função pra posicionar valores acima das barras no gráfico\n",
    "# esta função foi modificada a partir da original, disponível em: https://stackoverflow.com/a/56780852\n",
    "\n",
    "def show_values_on_bars(axs, h_v=\"v\", space=0.4, size = 12.0, color='black'):\n",
    "    def _show_on_single_plot(ax):\n",
    "        if h_v == \"v\":\n",
    "            for p in ax.patches:\n",
    "                _x = p.get_x() + p.get_width() / 2\n",
    "                _y = p.get_y() + p.get_height() + float(space)\n",
    "                value = int(p.get_height())\n",
    "                ax.text(_x, _y, value, ha=\"center\", size=size, color=color) \n",
    "        elif h_v == \"h\":\n",
    "            for p in ax.patches:\n",
    "                _x = p.get_x() + p.get_width() + float(space)\n",
    "                _y = p.get_y() + p.get_height()\n",
    "                value = int(p.get_width())\n",
    "                ax.text(_x, _y, value, ha=\"left\", size=size, color=color)\n",
    "\n",
    "    if isinstance(axs, np.ndarray):\n",
    "        for idx, ax in np.ndenumerate(axs):\n",
    "            _show_on_single_plot(ax)\n",
    "    else:\n",
    "        _show_on_single_plot(axs)\n",
    "        \n",
    "# convertendo o dicionario com os dados de tempo para um dataframe do pandas\n",
    "data_tempo = pd.DataFrame.from_dict(dicionario_tempo.items())\n",
    "\n",
    "# criando e imprimindo o grafico de barras no seaborn\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x=0, y=1, data=data_tempo , palette = \"Purples\", order = [24, 48, 72])\n",
    "ax.set_title(\"Janelas de tempo\".upper(), pad=4.0, fontsize=16, fontweight = 'bold', color = 'dimgrey', y = 1.05)\n",
    "ax.set_xlabel('Tempo (horas)', labelpad = 5.0, fontsize=14,  color = 'dimgrey')\n",
    "ax.set_ylabel('Quantidade', labelpad = 10.0, fontsize=14, color = 'dimgrey')\n",
    "ax.set_ylim(ymin=7000)\n",
    "show_values_on_bars(ax, size = 15, space = 30, color = 'dimgrey')\n",
    "plt.xticks(rotation = 0, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed22b3d3",
   "metadata": {},
   "source": [
    "— Tá vendo, Jojô, então não tem nenhum experimento medido após 6 horas. Nestes dados os experimentos só foram medidos após 24, 48 ou 72 horas.\n",
    "\n",
    "— Ai, que legal esse gráfico mamãe, eu adoro roxo!! — Joana parou pra pensar um pouco — Se eu somar esse números em cima das barrinhas, quanto dá? Seus pandas podem me ajudar nisso?\n",
    "\n",
    "— Claro, vamos pedir pra eles fazerem a soma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0342cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23814"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# somando os valores do dicionario criado anteriormente, \n",
    "# que equivalem à contagem feita pelo df.tempo.value_counts()\n",
    "\n",
    "sum(dicionario_tempo.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a250a21d",
   "metadata": {},
   "source": [
    "— Que rápido! Eu ia demorar muito tempo pra fazer essa conta com números grandes.\n",
    "\n",
    "— Esse número não te lembra nada Jojô?\n",
    "\n",
    "— Acho que sim, deixa eu rodar a tela pra cima um pouquinho... — diz Joana pegando o mouse da mão direita de Clara — ...deixa eu contar aqui 2...3...8...1...4... Achei! É o mesmo número da quantidade de linhas na sua tabela, não é?\n",
    "\n",
    "— Isso mesmo ! A soma do número de valores na coluna \"tempo\" tem que ser igual ao número de linhas. Senão, provavelmente teria alguma linha vazia, ou algum outro erro. E a gente pode pedir pro computador fazer essa comparação direto, olha só:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "585f6bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numero_de_linhas = df.shape[0]\n",
    "numero_de_linhas == sum(dicionario_tempo.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e3045c",
   "metadata": {},
   "source": [
    "— Ele tá dizendo pra gente que é *True*, que significa \"verdadeiro\" em inglês. Se os números não fossem iguais, ele diria *False*, que significa \"falso\", em inglês também. Isso é um tipo de valor que chamamos de *booleano*, ele só pode ser falso ou verdadeiro, 0 ou 1. O nome vem de um matemático inglês, chamado George Boole, que escreveu sobre isso lá na métade do século XIX.\n",
    "\n",
    "— Nossa, faz tempo né? E a gente usa isso até hoje? \n",
    "\n",
    "— Muito! Os computadores, os celulares e todos os dispositivos eletrônicos funcionam assim, eles \"pensam\" em zeros e uns. Quando a gente digita alguma coisa aqui pro pandas, ele traduz isso em outros idiomas, até chegar nos zeros e uns, que é o que ele consegue entender. É como um telefone sem fio, vários pedacinhos do computador vão sussurando um para o outro até chegar em quem realmente processa a informação. Você já brincou de telefone sem fio?\n",
    "\n",
    "— Já sim! Eu acho engraçado porque nunca o que eu disse chega igual no final — e deu uma risadinha.\n",
    "\n",
    "— Pois é filha, os computadores tem esse mesmo problema. Eles precisam garantir que depois de tanta sussurro de ouvido em ouvido, a informação chegue certinha lá no fim. Mas agora vamos... — Clara fez uma pausa enquanto escrevia novas linhas de código — ...vamos voltar naquela tabela do começo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db7e158",
   "metadata": {},
   "source": [
    "### Parte II: Células e Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02996147",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</th>\n",
       "      <th>g-0</th>\n",
       "      <th>g-1</th>\n",
       "      <th>g-2</th>\n",
       "      <th>g-3</th>\n",
       "      <th>g-4</th>\n",
       "      <th>...</th>\n",
       "      <th>c-90</th>\n",
       "      <th>c-91</th>\n",
       "      <th>c-92</th>\n",
       "      <th>c-93</th>\n",
       "      <th>c-94</th>\n",
       "      <th>c-95</th>\n",
       "      <th>c-96</th>\n",
       "      <th>c-97</th>\n",
       "      <th>c-98</th>\n",
       "      <th>c-99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>b68db1d53</td>\n",
       "      <td>1.0620</td>\n",
       "      <td>0.5577</td>\n",
       "      <td>-0.2479</td>\n",
       "      <td>-0.6208</td>\n",
       "      <td>-0.1944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2862</td>\n",
       "      <td>0.2584</td>\n",
       "      <td>0.8076</td>\n",
       "      <td>0.5523</td>\n",
       "      <td>-0.1912</td>\n",
       "      <td>0.6584</td>\n",
       "      <td>-0.3981</td>\n",
       "      <td>0.2139</td>\n",
       "      <td>0.3801</td>\n",
       "      <td>0.4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_000779bfc</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>df89a8e5a</td>\n",
       "      <td>0.0743</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.2991</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4265</td>\n",
       "      <td>0.7543</td>\n",
       "      <td>0.4708</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>0.2957</td>\n",
       "      <td>0.4899</td>\n",
       "      <td>0.1522</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.7371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_000a6266a</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>18bb41b2c</td>\n",
       "      <td>0.6280</td>\n",
       "      <td>0.5817</td>\n",
       "      <td>1.5540</td>\n",
       "      <td>-0.0764</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7250</td>\n",
       "      <td>-0.6297</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>-1.3240</td>\n",
       "      <td>-0.3174</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
       "      <td>0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>8c7f86626</td>\n",
       "      <td>-0.5138</td>\n",
       "      <td>-0.2491</td>\n",
       "      <td>-0.2656</td>\n",
       "      <td>0.5288</td>\n",
       "      <td>4.0620</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0990</td>\n",
       "      <td>-0.6441</td>\n",
       "      <td>-5.6300</td>\n",
       "      <td>-1.3780</td>\n",
       "      <td>-0.8632</td>\n",
       "      <td>-1.2880</td>\n",
       "      <td>-1.6210</td>\n",
       "      <td>-0.8784</td>\n",
       "      <td>-0.3876</td>\n",
       "      <td>-0.8154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D2</td>\n",
       "      <td>7cbed3131</td>\n",
       "      <td>-0.3254</td>\n",
       "      <td>-0.4009</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>0.6919</td>\n",
       "      <td>1.4180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>1.0690</td>\n",
       "      <td>0.5523</td>\n",
       "      <td>-0.3031</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>-0.3786</td>\n",
       "      <td>0.7125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id tratamento  tempo dose      droga     g-0     g-1     g-2  \\\n",
       "0  id_000644bb2  com_droga     24   D1  b68db1d53  1.0620  0.5577 -0.2479   \n",
       "1  id_000779bfc  com_droga     72   D1  df89a8e5a  0.0743  0.4087  0.2991   \n",
       "2  id_000a6266a  com_droga     48   D1  18bb41b2c  0.6280  0.5817  1.5540   \n",
       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "      g-3     g-4  ...    c-90    c-91    c-92    c-93    c-94    c-95  \\\n",
       "0 -0.6208 -0.1944  ...  0.2862  0.2584  0.8076  0.5523 -0.1912  0.6584   \n",
       "1  0.0604  1.0190  ... -0.4265  0.7543  0.4708  0.0230  0.2957  0.4899   \n",
       "2 -0.0764 -0.0323  ... -0.7250 -0.6297  0.6103  0.0223 -1.3240 -0.3174   \n",
       "3  0.5288  4.0620  ... -2.0990 -0.6441 -5.6300 -1.3780 -0.8632 -1.2880   \n",
       "4  0.6919  1.4180  ...  0.0042  0.0048  0.6670  1.0690  0.5523 -0.3031   \n",
       "\n",
       "     c-96    c-97    c-98    c-99  \n",
       "0 -0.3981  0.2139  0.3801  0.4176  \n",
       "1  0.1522  0.1241  0.6077  0.7371  \n",
       "2 -0.6417 -0.2187 -1.4080  0.6931  \n",
       "3 -1.6210 -0.8784 -0.3876 -0.8154  \n",
       "4  0.1094  0.2885 -0.3786  0.7125  \n",
       "\n",
       "[5 rows x 877 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde8ac4d",
   "metadata": {},
   "source": [
    "— Até agora você só viu as primeiras cinco colunas. Agora vamos entender o que o resto das colunas tem pra contar pra gente, afinal, temos 877 colunas no total. Se você prestar atenção na tabela, vai perceber que depois da coluna \"droga\" as colunas parecem que só tem letra e número, como \"g-0\", \"g-1\", e mais pro fim isso muda pra letra \"c\", por exemplo \"c-94\", \"c-95\"...\n",
    "\n",
    "— É, eu to vendo, mas não entendi nada.\n",
    "\n",
    "— Pra você entender, antes eu preciso te explicar um pouquinho de biologia.\n",
    "\n",
    "— A gente vai falar de plantas e de cachorrinhos?\n",
    "\n",
    "— Também. Você sabe o que as plantas, os cachorrinhos e você têm em comum? \n",
    "\n",
    "— Eu tenho alguma coisa igual a uma planta? Mas eu não sou uma plantinha, mamãe. Eu nem tenho folhas.\n",
    "\n",
    "— Sim, eu sei, mas você é um ser vivo, igual a ela e igual a todos os animais. E todo ser vivo é feito de células, do mesmo jeito que um prédio é feito de tijolos.\n",
    "\n",
    "— Então você tá me falando que tem um monte de tijolinhos aqui no meu dedo? — disse ela olhando de perto sua mão — Eu não to vendo nenhum não, mãe.\n",
    "\n",
    "— É que as células são muito pequenas. Pra ver, você precisa de um microscópio. Eu tenho um livro naquela estante do escritório com algumas fotos tiradas em microscópios, depois eu pego pra você ver... Agora olha aqui na tabela, tá vendo a coluna \"c-0\" ?\n",
    "\n",
    "— Tô sim\n",
    "\n",
    "— Essa letra \"c\" é de célula. Imagina um montinho de células vivendo juntas num potinho dentro de um laboratório, aí chega alguém e pinga umas gotinhas de um remédio nesse pote. No dia seguinte, a pessoa volta pra medir quantas células tem ali. Se a pessoa ver que algumas células morreram, isso quer dizer que o remédio fez mal pras células. Mas se elas tiverem aumentado, a gente pode dizer que o remédio fez bem pra elas. É isso que os números dessas colunas dizem pra gente, se o montinho de células aumentou, diminuiu ou ficou igual. \n",
    "\n",
    "— Então se o remédio ajudar as células a terem filhotinhos, é um remédio bom pra elas, e se matar algumas células, é um remédio ruim pra elas?\n",
    "\n",
    "— Exatamente. A gente chama isso de \"viabilidade celular\".\n",
    "\n",
    "— Celular? Igual aquele que a gente usa pra mandar mensagem no \"atizápi\"?\n",
    "\n",
    "— Não, hahah. Aqui a palavra celular tem um significado diferente. Viabilidade celular aqui tem a mais ver com quantas células ficaram vivas do que com o celular de fazer ligação e mandar mensagem. Podemos perguntar pro computador quantos grupos de células temos aqui nestes dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7aaae4f8",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A tabela contém dados de: \n",
      "- 772 genes (indo de g-0 a g-771), e \n",
      "- 100 linhagens celulares (indo de c-0 a c-99)\n"
     ]
    }
   ],
   "source": [
    "##### Explorando as colunas de linhagens celulares e expressão gênica ######\n",
    "\n",
    "# selecionando apenas as colunas que começam com a letra g, indicando os genes\n",
    "colunas_g = [col for col in df if col.startswith('g')]\n",
    "\n",
    "# fazendo o mesmo, agora com as células\n",
    "colunas_c = [col for col in df if col.startswith('c')]\n",
    "\n",
    "print(\"A tabela contém dados de: \\n- %d genes (indo de %s a %s), e \\n- %d linhagens celulares (indo de %s a %s)\"\n",
    "       % (len(colunas_g), colunas_g[0], colunas_g[-1], \n",
    "          len(colunas_c), colunas_c[0], colunas_c[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75e1218",
   "metadata": {},
   "source": [
    "— Tá vendo, temos 100 grupos de células na tabela.\n",
    "\n",
    "— Mãe, eu to vendo aqui que tá escrito \"genes\"... O que é um \"genes\".\n",
    "\n",
    "— Ah, desculpa filha, esqueci de te explicar essa parte. Deixa eu pensar numa forma mais fácil... Já sei ! Você já comeu macarrão parafuso, não comeu?\n",
    "\n",
    "— Já sim, é aquele todo enroladinho, né?\n",
    "\n",
    "— Esse mesmo. Dentro de cada célula sua tem uma molécula chamada DNA. Ela é toda enroladinha também, igual um macarrão parafuso. O DNA é como se fosse a receita sobre como fazer tudo que tem no seu corpo. Então quando seu cabelo tá crescendo, é porque seu DNA tá mandando as informações certinhas de como encaixar as coisinhas lá dentro até aparecer mais cabelo na sua cabeça. Mas são só alguns pedaços do DNA que guardam essas informações.\n",
    "\n",
    "— Esse tal de DNA tem um caderninho pra guardar as receitas? Igual a vovó?\n",
    "\n",
    "— Na verdade ele é o caderninho de receitas, mas imagina um caderno com a maioria das páginas em branco. Poucas páginas tem receitas escritas, e essas páginas são os genes. Gene é o pedacinho do DNA onde tem as receitas escritas.\n",
    "\n",
    "— E porque ele deixa tanta página em branco? \n",
    "\n",
    "— É uma ótima pergunta filha. Apesar de ter muita gente no mundo estudando os genes e o DNA humano, nós não sabemos ainda. Você pode estudar isso e ajudar a descobrir mais sobre as outras páginas, sobre esses outros pedaços do DNA que a gente não sabe muito bem pra que serve... Agora voltando pra tabela, como você viu ali em cima, temos dados de 772 genes. Cada coluna que começa com \"g\" tá mostrando pra gente como aquele gene foi afetado pelos experimentos, chamamos isso de \"expressão gênica\".\n",
    "\n",
    "— Mas só tem um monte de número aqui, como você entende isso?\n",
    "\n",
    "— Pensa assim: se os genes que fazem seu cabelo crescer de repente ficarem cansados e pararem de fazer cabelo, seu cabelo vai parar de crescer, e isso pode indicar pra gente que a expressão gênica dele foi reduzida. A próxima pergunta é: porque? No caso da tabela, o motivo é a droga que estamos testando.\n",
    "\n",
    "— Então o remédio que você ta testando pode me deixar careca?\n",
    "\n",
    "— Se nestes dados tiver algum gene que fabrica cabelo, nós poderíamos ver se tem alguma droga que reduz a expressão gênica dele. Depois de mais algumas análises, se tivermos certeza desta relação, poderíamos fazer um shampoo que deixa as pessoas carecas. Mas se tiver alguma droga que aumente a expressão gênica, daria pra fazer um shampoo que faz seu cabelo crescer.\n",
    "\n",
    "— Nossa, que legal! É quase igual mágica.\n",
    "\n",
    "— É meio maluco mesmo. — disse Clara sorrindo — Agora que já sabemos um pouco mais sobre os 772 genes e 100 linhagens de células que temos nessa tabela, podemos conhecer mais sobre eles, olha aqui nestes gráficos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9df95e45",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "###### EXPLICAÇÃO E FUNÇÃO PARA OS GRÁFICOS A SEGUIR ######\n",
    "\n",
    "# criando uma variavel para armazenar o cálculo das estatísticas para todas as colunas de genes\n",
    "df_estatisticas_g = df.loc[:, colunas_g].describe()\n",
    "\n",
    "# fazendo o mesmo para colunas de viabilidade celular\n",
    "df_estatisticas_c = df.loc[:,colunas_c].describe()\n",
    "\n",
    "\n",
    "## basicamente, o que e está acontecendo nas próximas duas células é a visualização dos seguinte dados,\n",
    "# que dizem respeito as extatísticas das expressões gênicas e viabilidade celular:\n",
    "'''\n",
    "df_estatisticas_g.T['min']\n",
    "df_estatisticas_g.T['max']\n",
    "df_estatisticas_g.T['mean']\n",
    "df_estatisticas_g.T['std']\n",
    "\n",
    "df_estatisticas_c.T['min']\n",
    "df_estatisticas_c.T['max']\n",
    "df_estatisticas_c.T['mean']\n",
    "df_estatisticas_c.T['std']\n",
    "'''\n",
    "\n",
    "###### CRIANDO FUNÇÃO PARA PREPARAR E ESTILIZAR OS HISTOGRAMAS ######\n",
    "\n",
    "\n",
    "def hist(df, stat, color, xlabel, title):\n",
    "    sns.histplot(data=df.T[stat],bins=30, element=\"step\", color = color)\n",
    "    ax2.set_title(title, pad=4.0, fontsize=16, fontweight = 'bold', color = 'grey')\n",
    "    ax2.set_xlabel(xlabel, labelpad = 5.0, fontsize=14,  color = 'dimgrey')\n",
    "    ax2.set_ylabel('Quantidade', labelpad = 10.0, fontsize=14, color = 'dimgrey')\n",
    "    plt.xticks(rotation = 0, fontsize=14)\n",
    "    plt.yticks(fontsize=14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3a212e9",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### GRAFICOS EXPRESSÃO GÊNICA #####\n",
    "\n",
    "#def four_charts(df, color, suptitle, xlabel):\n",
    "data=df_estatisticas_g\n",
    "color=\"lightskyblue\"\n",
    "suptitle=\"Frequência de aparição das estatísticas de expressão gênica\".upper()\n",
    "xlabel = \"Valor\"\n",
    "    \n",
    "sns.set_style(\"white\")\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "ax2 = plt.subplot(2, 2, 1)\n",
    "hist(data, \"min\", color, xlabel, \"Valores Mínimos\" )\n",
    "\n",
    "ax2 = plt.subplot(2 ,2, 2)\n",
    "hist(data, \"max\", color, xlabel, \"Valores Máximos\" )\n",
    "\n",
    "ax2 = plt.subplot(2 ,2, 3)\n",
    "hist(data, \"mean\", color, xlabel, \"Médias\" )\n",
    "\n",
    "ax2 = plt.subplot(2 ,2, 4)\n",
    "hist(data, \"std\", color, xlabel, \"Desvios-Padrão\" )\n",
    "\n",
    "\n",
    "plt.suptitle(suptitle, fontsize = 20,  y= 0.95, fontweight = 'bold', color = 'dimgray')\n",
    "\n",
    "sns.despine(offset=5, trim=True)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c9c6b57",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rl2bOnClJOnHihNatW6eYmBiNHDkyxJUFJiUlRZ07d1ZsbKxiY2M1cOBA7du3LyKC4vLlyzV9+nS5XC717t1bPXv21IEDB/RP//RPoS7tOzU0NKioqEh5eXnKyck573Gnvndcal1SZL13RNZHSmFm+PDhLac/rFixQiNGjDhvn/T0dNXU1LR8F2rz5s265pprgllmm/mzvurqap09e1aSdPz4cW3bts0x65P8W+Mvf/lLffzxx/rwww81e/ZsFRQUhFVIvBR/1lhRUaG6ujpJ3z6n27Ztc1QD9meNZ8+e1WOPPab8/PywCsD+8Gd9TpSenq5Dhw7p8OHDOnv2rNasWaPhw4eb9hk+fLjeffddSdL//d//6aabbuKIahCde+0ZhqEdO3YoISGh5TRoJ/v666/1ox/9SK+88oqj3usu5cMPP2z53+jRo/Xcc885PiRK337IXlZWpsbGRp05c0Y7d+5Unz59Ql2WJbp3766NGzdKkr755hsdPHhQPXv2DHFV380wDM2ZM0dpaWmaMmXKBfdx4nuHP+uKtPcOl8G3/m1z4sQJPf744yovL1ePHj307//+7+rcubP+/Oc/6+2339YLL7wgSdqwYYNefvllSVL//v01b968lguHhDN/1rdt2zY999xzcrlcMgxD9913X9icVusPf5/Dc5YvX65du3bp2WefDVHFrefPGs+9Rs89j/fee6/uvvvuUJfuN3/WuHLlSj3zzDOmDzJefvll9evXL4SV+8ff1+k///M/68CBAzp9+rQ6d+6sF154QUOHDg1x9d9t3bp1evHFF9XU1KTx48frkUce0auvvqoBAwZoxIgRqq+v15NPPqm9e/eqU6dO+rd/+7eWMzQQuJkzZ2rLli06ceKEkpKS9KMf/UiNjY2SpHvuuUeGYWjevHn65JNPdPnll+vFF19Uenp6iKu+tEuta86cOfrggw9aLo4SHR2t5cuXh7Jkv1xqXX/rqaee0rBhwxzxwZg/61q0aJGWL1+uqKgoTZgwQffff38IK/bfpdbm9Xr19NNP69ixYzIMQ9OmTVN+fn6Iq/5uW7du1Q9+8AP17du35TTnmTNn6uuvv5bk3PcOf9bl1PeOiyEoAgAAAABMOPUUAAAAAGBCUAQAAAAAmBAUAQAAAAAmBEUAAAAAgAlBEQAAAABgQlAEAAAAAJgQFIF2ZPXq1frZz34W6jIAAAgr9EfgfARFwCEWLFigV1999YKPlZeX69FHH9WePXuCXBUAAKFFfwTsQVAEHOLmm2/WZ599pqqqqvMeKy0tVWJiov7xH//R9jqamppsnwMAAH/RHwF7xIS6AAD+GTBggBISErRx40bdcccdLdubmpq0ZcsWZWdn6w9/+IM+++wz1dTUqHPnzhoyZIhGjhypqKgLfybU3Nys999/X+vXr1dtba2Sk5OVl5enjIwMSVJVVZXmzp2rKVOmaMOGDTp48KDGjRunYcOGBWPJAABcEv0RsAdBEXCI6Oho3XTTTdq0aZNuv/32lua2c+dO1dbWavDgwdqwYYOmTp2qhIQEHTp0SEuWLFFcXJyGDBlywTE/+ugjrV27Vvfcc49SU1O1ZcsWLVy4UE899ZR69erVst/KlStVWFioe++9V9HR0UFZLwAA/qA/Avbg1FPAQW6++WYdP35c+/bta9lWWlqqfv36KTExUXl5ebrqqquUlJSkrKwsDR06VFu3br3oeGvXrtXIkSM1aNAgeTwe5eXl6ZprrtHatWtN+w0bNkw33HCDunbtqi5duti2PgAA2oL+CFiPI4qAgyQnJ+vaa6/Vxo0bdd111+nkyZPau3evHnjgAUlSSUmJSktLdfz4cTU0NKipqUmJiYkXHOvMmTOqrq5WWlqaaXufPn20e/du07bU1FR7FgQAgAXoj4D1CIqAw9x88836wx/+IJ/Pp02bNik2NlYZGRnaunWrli1bpsLCQqWlpaljx44qKSnRjh07Ap6zQ4cOgRcOAICN6I+AtTj1FHCY66+/Xpdddpm2bNmijRs36sYbb1R0dLS++OILXXXVVRo2bJhSU1OVnJysY8eOXXScyy+/XJ06ddKBAwdM27/44gt1797d7mUAAGAp+iNgLYIi4DBut1sDBw7UmjVrdOzYMd18882Svj3t5vDhw9q9e7cqKyv13nvv6fPPP//OsUaNGqW1a9fqT3/6k7xer1atWqX9+/dr5MiRwVgKAACWoT8C1uLUU8CBhgwZok8++URpaWktn24OHTpUR44c0euvvy5JyszM1MiRI1VaWnrRcYYNG6a6ujq9++67OnXqlDwej6ZNm6aePXsGZR0AAFiJ/ghYx2UYhhHqIgAAAAAA4YNTTwEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGBCUAQAAAAAmBAUAQAAAAAm/w+u5rAcf3xKFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### GRAFICOS VIABILIDADE CELULAR #####\n",
    "\n",
    "#def four_charts(df, color, suptitle, xlabel):\n",
    "data=df_estatisticas_c\n",
    "color=\"limegreen\"\n",
    "suptitle=\"Frequência de aparição das estatísticas de viabilidade celular\".upper()\n",
    "xlabel = \"Valor\"\n",
    "    \n",
    "sns.set_style(\"white\")\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "ax2 = plt.subplot(2, 2, 1)\n",
    "hist(data, \"min\", color, xlabel, \"Valores Mínimos\" )\n",
    "\n",
    "ax2 = plt.subplot(2 ,2, 2)\n",
    "hist(data, \"max\", color, xlabel, \"Valores Máximos\" )\n",
    "\n",
    "ax2 = plt.subplot(2 ,2, 3)\n",
    "hist(data, \"mean\", color, xlabel, \"Médias\" )\n",
    "\n",
    "ax2 = plt.subplot(2 ,2, 4)\n",
    "hist(data, \"std\", color, xlabel, \"Desvios-Padrão\" )\n",
    "\n",
    "\n",
    "plt.suptitle(suptitle, fontsize = 20,  y= 0.95, fontweight = 'bold', color = 'dimgray')\n",
    "\n",
    "sns.despine(offset=5, trim=True)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5bad738-ce61-4497-bdc3-d3d3d1b45ea1",
   "metadata": {},
   "source": [
    "— Como nós seres humanos não conseguimos \"entender\" essa grande quantidade de números que existe na tabela, uma das formas que podemos \"ver\" estas coisas é utilizando gráficos, como esses. Aqui temos dois gráficos que mostram quantas vezes aparecem certos valores mínimos e máximos, médias e desvio-padrão. Em azul, vemos estas informações para os genes, e em verde, para as células.\n",
    "\n",
    "— Desvio o quê?\n",
    "\n",
    "— Desvio-padrão, filha. É uma medida estatística que mostra quanto os valores estão \"fugindo\" da média. Por exemplo: na sua escola, a idade de todas as alunas e alunos é parecida, certo?\n",
    "\n",
    "— É, todo mundo tem mais ou menos a mesma idade.\n",
    "\n",
    "— Então, se eu fizesse a mesma medição no domingo, quando estávamos no parque, você concorda que existiriam muito mais diferenças entre as idades das pessoas?\n",
    "\n",
    "— Sim, tinha bebê, criança, adolescente, adulto e até uma vovózinha andando no parque.\n",
    "\n",
    "— Podemos dizer que a idade medida no parque tem uma maior \"variabilidade\", e é pra isso que usamos os desvios-padrão. Mas agora eu quero te explicar outra coisa, pra podermos falar melhor sobre como os remédios funcionam no nosso corpo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26284084",
   "metadata": {},
   "source": [
    "### Parte III: Mecanismos de Ação"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20d9437",
   "metadata": {},
   "source": [
    "— Jojô, um mecanismo de ação é o jeito como a droga testada tá afetando suas células. Por exemplo, seu corpo produz uma enzima chamada \"lactase\", que é importante na digestão do leite. Então se você tomar algum remédio que tem um mecanismo de ação inibidor da lactase, e logo depois tomar leite, seu corpo vai ter dificuldade pra digerir o leite, e isso pode te dar uma baita dor de barriga ! Vamos ver alguns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "528d8644",
   "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>id</th>\n",
       "      <th>5-alpha_reductase_inhibitor</th>\n",
       "      <th>11-beta-hsd1_inhibitor</th>\n",
       "      <th>acat_inhibitor</th>\n",
       "      <th>acetylcholine_receptor_agonist</th>\n",
       "      <th>acetylcholine_receptor_antagonist</th>\n",
       "      <th>acetylcholinesterase_inhibitor</th>\n",
       "      <th>adenosine_receptor_agonist</th>\n",
       "      <th>adenosine_receptor_antagonist</th>\n",
       "      <th>adenylyl_cyclase_activator</th>\n",
       "      <th>...</th>\n",
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       "      <th>trpv_agonist</th>\n",
       "      <th>trpv_antagonist</th>\n",
       "      <th>tubulin_inhibitor</th>\n",
       "      <th>tyrosine_kinase_inhibitor</th>\n",
       "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
       "      <th>vegfr_inhibitor</th>\n",
       "      <th>vitamin_b</th>\n",
       "      <th>vitamin_d_receptor_agonist</th>\n",
       "      <th>wnt_inhibitor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>23809</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23810</th>\n",
       "      <td>id_fffb70c0c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23813</th>\n",
       "      <td>id_ffffdd77b</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23814 rows × 207 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  5-alpha_reductase_inhibitor  11-beta-hsd1_inhibitor  \\\n",
       "0      id_000644bb2                            0                       0   \n",
       "1      id_000779bfc                            0                       0   \n",
       "2      id_000a6266a                            0                       0   \n",
       "3      id_0015fd391                            0                       0   \n",
       "4      id_001626bd3                            0                       0   \n",
       "...             ...                          ...                     ...   \n",
       "23809  id_fffb1ceed                            0                       0   \n",
       "23810  id_fffb70c0c                            0                       0   \n",
       "23811  id_fffc1c3f4                            0                       0   \n",
       "23812  id_fffcb9e7c                            0                       0   \n",
       "23813  id_ffffdd77b                            0                       0   \n",
       "\n",
       "       acat_inhibitor  acetylcholine_receptor_agonist  \\\n",
       "0                   0                               0   \n",
       "1                   0                               0   \n",
       "2                   0                               0   \n",
       "3                   0                               0   \n",
       "4                   0                               0   \n",
       "...               ...                             ...   \n",
       "23809               0                               0   \n",
       "23810               0                               0   \n",
       "23811               0                               0   \n",
       "23812               0                               0   \n",
       "23813               0                               0   \n",
       "\n",
       "       acetylcholine_receptor_antagonist  acetylcholinesterase_inhibitor  \\\n",
       "0                                      0                               0   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               0   \n",
       "...                                  ...                             ...   \n",
       "23809                                  0                               0   \n",
       "23810                                  0                               0   \n",
       "23811                                  0                               0   \n",
       "23812                                  0                               0   \n",
       "23813                                  0                               0   \n",
       "\n",
       "       adenosine_receptor_agonist  adenosine_receptor_antagonist  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "2                               0                              0   \n",
       "3                               0                              0   \n",
       "4                               0                              0   \n",
       "...                           ...                            ...   \n",
       "23809                           0                              0   \n",
       "23810                           0                              0   \n",
       "23811                           0                              0   \n",
       "23812                           0                              0   \n",
       "23813                           0                              0   \n",
       "\n",
       "       adenylyl_cyclase_activator  ...  tropomyosin_receptor_kinase_inhibitor  \\\n",
       "0                               0  ...                                      0   \n",
       "1                               0  ...                                      0   \n",
       "2                               0  ...                                      0   \n",
       "3                               0  ...                                      0   \n",
       "4                               0  ...                                      0   \n",
       "...                           ...  ...                                    ...   \n",
       "23809                           0  ...                                      0   \n",
       "23810                           0  ...                                      0   \n",
       "23811                           0  ...                                      0   \n",
       "23812                           0  ...                                      0   \n",
       "23813                           0  ...                                      0   \n",
       "\n",
       "       trpv_agonist  trpv_antagonist  tubulin_inhibitor  \\\n",
       "0                 0                0                  0   \n",
       "1                 0                0                  0   \n",
       "2                 0                0                  0   \n",
       "3                 0                0                  0   \n",
       "4                 0                0                  0   \n",
       "...             ...              ...                ...   \n",
       "23809             0                0                  0   \n",
       "23810             0                0                  0   \n",
       "23811             0                0                  0   \n",
       "23812             0                0                  0   \n",
       "23813             0                0                  0   \n",
       "\n",
       "       tyrosine_kinase_inhibitor  ubiquitin_specific_protease_inhibitor  \\\n",
       "0                              0                                      0   \n",
       "1                              0                                      0   \n",
       "2                              0                                      0   \n",
       "3                              0                                      0   \n",
       "4                              0                                      0   \n",
       "...                          ...                                    ...   \n",
       "23809                          0                                      0   \n",
       "23810                          0                                      0   \n",
       "23811                          0                                      0   \n",
       "23812                          0                                      0   \n",
       "23813                          0                                      0   \n",
       "\n",
       "       vegfr_inhibitor  vitamin_b  vitamin_d_receptor_agonist  wnt_inhibitor  \n",
       "0                    0          0                           0              0  \n",
       "1                    0          0                           0              0  \n",
       "2                    0          0                           0              0  \n",
       "3                    0          0                           0              0  \n",
       "4                    0          0                           0              0  \n",
       "...                ...        ...                         ...            ...  \n",
       "23809                0          0                           0              0  \n",
       "23810                0          0                           0              0  \n",
       "23811                0          0                           0              0  \n",
       "23812                0          0                           0              0  \n",
       "23813                0          0                           0              0  \n",
       "\n",
       "[23814 rows x 207 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_moa = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')\n",
    "\n",
    "# O arquivo tem 10MB. Para executar mais de uma vez, \n",
    "# é preferível fazer o download do arquivo acima e executar localmente\n",
    "# df_moa = pd.read_csv('../imersaodados3/dados/dados_resultados.csv')\n",
    "\n",
    "df_moa  # moa é a abreviação de mechanisms of action"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11300af0",
   "metadata": {},
   "source": [
    "— Agora temos essa nova tabela que conversa com aquela outra. Pra cada experimento descrito nas linhas da outra tabela, essa aqui mostra quais mecanismos de ação foram ativados. Podemos ver quais valores tem nessa tabela:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e344ec9",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conferindo quais são os valores presentes na tabela\n",
    "\n",
    "# um set (conjunto) no python é uma estrutura de dados que não aceita duplicatas nem alterações,\n",
    "# sendo, portanto, uma ótima escolha para a verificação que queremos fazer\n",
    "\n",
    "conjunto_valores_unicos_moa = set()\n",
    "for i in range(1, 207):\n",
    "    valores_unicos = df_moa.iloc[:,i].unique()\n",
    "    for valor in valores_unicos:\n",
    "        conjunto_valores_unicos_moa.add(valor)\n",
    "\n",
    "conjunto_valores_unicos_moa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e5b2cf",
   "metadata": {},
   "source": [
    "— Lembra quando eu te falei de valores booleanos? Aqui encontramos eles de novo. Nesta tabela, 0 significa que o mecanismo de ação não foi ativado, e 1 significa que foi ativado.\n",
    "\n",
    "— Ligado ou desligado? É igual ligar a lâmpada?\n",
    "\n",
    "— Boa comparação, é por aí. Deixa eu checar se tem um valor aqui na tabela, pra eu te dar um exemplo..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bab6ca83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conferindo se há uma coluna 'anti-inflammatory', e retornando sua posição\n",
    "df_moa.columns.get_loc('anti-inflammatory') \n",
    "# coluna 22"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7f50a1",
   "metadata": {},
   "source": [
    "— Ótimo! Temos nessa tabela a medição de quais experimentos ativaram um mecanismo de ação anti-inflamatório. Lembra da semana retrasada quando você tava com o braço inchado e ardendo? O remédio que você tomou era um anti-inflamatório.\n",
    "\n",
    "— Aquele remédio vermelho que eu tomava na tampinha? Ai, tinha um gosto horrível.\n",
    "\n",
    "— Alguns até tem um gosto mais docinho, mas o que importa é que ele te ajuda a melhorar. Agora a gente vai mostrar esses dados pro computador e ver se ele consegue adivinhar quais experimentos vão despertar um mecanismo de ação anti-inflamatório e quais não vão.\n",
    "\n",
    "— O computador consegue adivinhar as coisas? Eu achei que você que tinha que perguntar tudo certinho.\n",
    "\n",
    "— Mais ou menos. Vou te explicar melhor logo logo, mas antes preciso perguntar se você sabe o que é uma hipótese."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80495cd",
   "metadata": {},
   "source": [
    "# Hipóteses, Elefantes e Anti-inflamatórios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d0b30a",
   "metadata": {},
   "source": [
    "— Uma hipótese... — Joana pausou a fala pra pensar por alguns segundos — É quando você tem um palpite?\n",
    "\n",
    "— Por aí. Vou te dar um exemplo: Eu tenho uma hipótese de que amanhã não vai chover.\n",
    "\n",
    "— Ah, eu acho que vai sim.\n",
    "\n",
    "— Porque?\n",
    "\n",
    "— Porque já é de noite e tá cheio de nuvens no céu, olha lá — falou Joana apontando para o céu, estampado atrás das janelas da sala.\n",
    "\n",
    "— Então nós temos hipóteses opostas, certo? Se amanhã não chover, a minha hipótese foi confirmada e a sua rejeitada. Mas se chover, acontece o inverso. Ou chove, ou não chove. Não tem meio termo. O próximo passo é juntar informações testar aquela hipótese. No nosso caso isso é fácil. É só esperar até amanhã e ver se vai chover. Aí teremos certeza se as nossas hipóteses foram confirmadas ou invalidadas.\n",
    "\n",
    "— Mamãe, eu tenho uma hipótese !!\n",
    "\n",
    "— Qual?\n",
    "\n",
    "— Todos os elefantes do mundo inteeeeiro tem uma tromba\n",
    "\n",
    "— E agora, como você faz pra testar sua hipótese?\n",
    "\n",
    "— Não sei... \n",
    "\n",
    "— Então vamos fazer o teste dos anti-inflamatórios pra ver se você tem alguma idéia, e depois eu quero que você tente responder sozinha como faz pra testar sua hipótese de que todos os elefantes tem tromba, tá bem?\n",
    "\n",
    "— Tuuudo bem !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d80fba",
   "metadata": {},
   "source": [
    "## Aprendizado de Máquina"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d02f6edc",
   "metadata": {},
   "source": [
    "— Respondendo sua pergunta anterior, sim, precisamos dizer certinho pro computador o que ele tem que fazer. Só que, conforme os computadores foram ficando mais potentes e a quantidade de dados disponíveis aumentou muito, foi aparecendo cada vez mais uma área chamada Aprendizado de Máquina. As idéias por trás dela não são novas, mas só nos últimos anos conseguimos colocar em prática.\n",
    "\n",
    "— Que legal! Então dá pra ensinar um computador a pular corda?\n",
    "\n",
    "— Na teoria sim, mas não quer dizer que seja fácil, nem rápido. Pensa no computador como uma esponja. Precisamos deixá-lo inundado de informação até ele absorver bastante. Por isso é muito importante organizar os dados que vamos mostrar pra ele, porque se tiver algum erro ali, o computador vai aprender errado. Vamos começar fazendo isso."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ba40c3",
   "metadata": {},
   "source": [
    "### Preparando os Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd37154c",
   "metadata": {},
   "source": [
    "— Primeiro pegamos aquela tabela que usamos no começo, depois a tabela que tem os dados das ativações de mecanismos de ação anti-inflamatórios, e então juntamos as duas numa nova tabela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d158f02",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.1912</td>\n",
       "      <td>0.6584</td>\n",
       "      <td>-0.3981</td>\n",
       "      <td>0.2139</td>\n",
       "      <td>0.3801</td>\n",
       "      <td>0.4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_000779bfc</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>df89a8e5a</td>\n",
       "      <td>0.0743</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.2991</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4265</td>\n",
       "      <td>0.7543</td>\n",
       "      <td>0.4708</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>0.2957</td>\n",
       "      <td>0.4899</td>\n",
       "      <td>0.1522</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.7371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_000a6266a</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>18bb41b2c</td>\n",
       "      <td>0.6280</td>\n",
       "      <td>0.5817</td>\n",
       "      <td>1.5540</td>\n",
       "      <td>-0.0764</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7250</td>\n",
       "      <td>-0.6297</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>-1.3240</td>\n",
       "      <td>-0.3174</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
       "      <td>0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>8c7f86626</td>\n",
       "      <td>-0.5138</td>\n",
       "      <td>-0.2491</td>\n",
       "      <td>-0.2656</td>\n",
       "      <td>0.5288</td>\n",
       "      <td>4.0620</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0990</td>\n",
       "      <td>-0.6441</td>\n",
       "      <td>-5.6300</td>\n",
       "      <td>-1.3780</td>\n",
       "      <td>-0.8632</td>\n",
       "      <td>-1.2880</td>\n",
       "      <td>-1.6210</td>\n",
       "      <td>-0.8784</td>\n",
       "      <td>-0.3876</td>\n",
       "      <td>-0.8154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D2</td>\n",
       "      <td>7cbed3131</td>\n",
       "      <td>-0.3254</td>\n",
       "      <td>-0.4009</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>0.6919</td>\n",
       "      <td>1.4180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>1.0690</td>\n",
       "      <td>0.5523</td>\n",
       "      <td>-0.3031</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>-0.3786</td>\n",
       "      <td>0.7125</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",
       "      <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>23809</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>df1d0a5a1</td>\n",
       "      <td>0.1394</td>\n",
       "      <td>-0.0636</td>\n",
       "      <td>-0.1112</td>\n",
       "      <td>-0.5080</td>\n",
       "      <td>-0.4713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1969</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>-0.8121</td>\n",
       "      <td>0.3434</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>-0.3246</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>0.5258</td>\n",
       "      <td>0.4680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23810</th>\n",
       "      <td>id_fffb70c0c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>ecf3b6b74</td>\n",
       "      <td>-1.3260</td>\n",
       "      <td>0.3478</td>\n",
       "      <td>-0.3743</td>\n",
       "      <td>0.9905</td>\n",
       "      <td>-0.7178</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4286</td>\n",
       "      <td>0.4426</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>-0.3195</td>\n",
       "      <td>-0.8086</td>\n",
       "      <td>-0.9798</td>\n",
       "      <td>-0.2084</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>-0.2715</td>\n",
       "      <td>0.3689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>0.3942</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>-0.7389</td>\n",
       "      <td>0.5505</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5409</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.7343</td>\n",
       "      <td>0.2807</td>\n",
       "      <td>0.4116</td>\n",
       "      <td>0.6422</td>\n",
       "      <td>0.2256</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.3808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>0.2324</td>\n",
       "      <td>0.4392</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.8531</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1105</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>-0.2012</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>1.5230</td>\n",
       "      <td>0.7101</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23813</th>\n",
       "      <td>id_ffffdd77b</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>972f41291</td>\n",
       "      <td>-0.8598</td>\n",
       "      <td>1.0240</td>\n",
       "      <td>-0.1361</td>\n",
       "      <td>0.7952</td>\n",
       "      <td>-0.3611</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.3890</td>\n",
       "      <td>-1.7450</td>\n",
       "      <td>-6.6300</td>\n",
       "      <td>-4.0950</td>\n",
       "      <td>-7.3860</td>\n",
       "      <td>-1.4160</td>\n",
       "      <td>-3.5770</td>\n",
       "      <td>-0.4775</td>\n",
       "      <td>-2.1500</td>\n",
       "      <td>-4.2520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23814 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose      droga     g-0     g-1  \\\n",
       "0      id_000644bb2     com_droga     24   D1  b68db1d53  1.0620  0.5577   \n",
       "1      id_000779bfc     com_droga     72   D1  df89a8e5a  0.0743  0.4087   \n",
       "2      id_000a6266a     com_droga     48   D1  18bb41b2c  0.6280  0.5817   \n",
       "3      id_0015fd391     com_droga     48   D1  8c7f86626 -0.5138 -0.2491   \n",
       "4      id_001626bd3     com_droga     72   D2  7cbed3131 -0.3254 -0.4009   \n",
       "...             ...           ...    ...  ...        ...     ...     ...   \n",
       "23809  id_fffb1ceed     com_droga     24   D2  df1d0a5a1  0.1394 -0.0636   \n",
       "23810  id_fffb70c0c     com_droga     24   D2  ecf3b6b74 -1.3260  0.3478   \n",
       "23811  id_fffc1c3f4  com_controle     48   D2  cacb2b860  0.3942  0.3756   \n",
       "23812  id_fffcb9e7c     com_droga     24   D1  8b87a7a83  0.6660  0.2324   \n",
       "23813  id_ffffdd77b     com_droga     72   D1  972f41291 -0.8598  1.0240   \n",
       "\n",
       "          g-2     g-3     g-4  ...    c-90    c-91    c-92    c-93    c-94  \\\n",
       "0     -0.2479 -0.6208 -0.1944  ...  0.2862  0.2584  0.8076  0.5523 -0.1912   \n",
       "1      0.2991  0.0604  1.0190  ... -0.4265  0.7543  0.4708  0.0230  0.2957   \n",
       "2      1.5540 -0.0764 -0.0323  ... -0.7250 -0.6297  0.6103  0.0223 -1.3240   \n",
       "3     -0.2656  0.5288  4.0620  ... -2.0990 -0.6441 -5.6300 -1.3780 -0.8632   \n",
       "4      0.9700  0.6919  1.4180  ...  0.0042  0.0048  0.6670  1.0690  0.5523   \n",
       "...       ...     ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "23809 -0.1112 -0.5080 -0.4713  ...  0.1969  0.0262 -0.8121  0.3434  0.5372   \n",
       "23810 -0.3743  0.9905 -0.7178  ...  0.4286  0.4426  0.0423 -0.3195 -0.8086   \n",
       "23811  0.3109 -0.7389  0.5505  ...  0.5409  0.3755  0.7343  0.2807  0.4116   \n",
       "23812  0.4392  0.2044  0.8531  ... -0.1105  0.4258 -0.2012  0.1506  1.5230   \n",
       "23813 -0.1361  0.7952 -0.3611  ... -3.3890 -1.7450 -6.6300 -4.0950 -7.3860   \n",
       "\n",
       "         c-95    c-96    c-97    c-98    c-99  \n",
       "0      0.6584 -0.3981  0.2139  0.3801  0.4176  \n",
       "1      0.4899  0.1522  0.1241  0.6077  0.7371  \n",
       "2     -0.3174 -0.6417 -0.2187 -1.4080  0.6931  \n",
       "3     -1.2880 -1.6210 -0.8784 -0.3876 -0.8154  \n",
       "4     -0.3031  0.1094  0.2885 -0.3786  0.7125  \n",
       "...       ...     ...     ...     ...     ...  \n",
       "23809 -0.3246  0.0631  0.9171  0.5258  0.4680  \n",
       "23810 -0.9798 -0.2084 -0.1224 -0.2715  0.3689  \n",
       "23811  0.6422  0.2256  0.7592  0.6656  0.3808  \n",
       "23812  0.7101  0.1732  0.7015 -0.6290  0.0740  \n",
       "23813 -1.4160 -3.5770 -0.4775 -2.1500 -4.2520  \n",
       "\n",
       "[23814 rows x 877 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TABELA INICIAL\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79916676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>anti-inflammatory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>23809</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23810</th>\n",
       "      <td>id_fffb70c0c</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23813</th>\n",
       "      <td>id_ffffdd77b</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23814 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  anti-inflammatory\n",
       "0      id_000644bb2                  0\n",
       "1      id_000779bfc                  0\n",
       "2      id_000a6266a                  0\n",
       "3      id_0015fd391                  0\n",
       "4      id_001626bd3                  0\n",
       "...             ...                ...\n",
       "23809  id_fffb1ceed                  0\n",
       "23810  id_fffb70c0c                  0\n",
       "23811  id_fffc1c3f4                  0\n",
       "23812  id_fffcb9e7c                  0\n",
       "23813  id_ffffdd77b                  0\n",
       "\n",
       "[23814 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TABELA COM RESULTADOS DE ATIVAÇÃO DE MECANISMOS DE AÇÃO ANTI-INFLAMATÓRIOS\n",
    "df_moa[['id', 'anti-inflammatory']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ba1a89f",
   "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>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</th>\n",
       "      <th>g-0</th>\n",
       "      <th>g-1</th>\n",
       "      <th>g-2</th>\n",
       "      <th>g-3</th>\n",
       "      <th>g-4</th>\n",
       "      <th>...</th>\n",
       "      <th>c-91</th>\n",
       "      <th>c-92</th>\n",
       "      <th>c-93</th>\n",
       "      <th>c-94</th>\n",
       "      <th>c-95</th>\n",
       "      <th>c-96</th>\n",
       "      <th>c-97</th>\n",
       "      <th>c-98</th>\n",
       "      <th>c-99</th>\n",
       "      <th>anti-inflammatory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>b68db1d53</td>\n",
       "      <td>1.0620</td>\n",
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       "      <td>-0.2479</td>\n",
       "      <td>-0.6208</td>\n",
       "      <td>-0.1944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2584</td>\n",
       "      <td>0.8076</td>\n",
       "      <td>0.5523</td>\n",
       "      <td>-0.1912</td>\n",
       "      <td>0.6584</td>\n",
       "      <td>-0.3981</td>\n",
       "      <td>0.2139</td>\n",
       "      <td>0.3801</td>\n",
       "      <td>0.4176</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_000779bfc</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>df89a8e5a</td>\n",
       "      <td>0.0743</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.2991</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7543</td>\n",
       "      <td>0.4708</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>0.2957</td>\n",
       "      <td>0.4899</td>\n",
       "      <td>0.1522</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.7371</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_000a6266a</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>18bb41b2c</td>\n",
       "      <td>0.6280</td>\n",
       "      <td>0.5817</td>\n",
       "      <td>1.5540</td>\n",
       "      <td>-0.0764</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6297</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>-1.3240</td>\n",
       "      <td>-0.3174</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
       "      <td>0.6931</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>8c7f86626</td>\n",
       "      <td>-0.5138</td>\n",
       "      <td>-0.2491</td>\n",
       "      <td>-0.2656</td>\n",
       "      <td>0.5288</td>\n",
       "      <td>4.0620</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6441</td>\n",
       "      <td>-5.6300</td>\n",
       "      <td>-1.3780</td>\n",
       "      <td>-0.8632</td>\n",
       "      <td>-1.2880</td>\n",
       "      <td>-1.6210</td>\n",
       "      <td>-0.8784</td>\n",
       "      <td>-0.3876</td>\n",
       "      <td>-0.8154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D2</td>\n",
       "      <td>7cbed3131</td>\n",
       "      <td>-0.3254</td>\n",
       "      <td>-0.4009</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>0.6919</td>\n",
       "      <td>1.4180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>1.0690</td>\n",
       "      <td>0.5523</td>\n",
       "      <td>-0.3031</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>-0.3786</td>\n",
       "      <td>0.7125</td>\n",
       "      <td>0</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",
       "      <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>23809</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>df1d0a5a1</td>\n",
       "      <td>0.1394</td>\n",
       "      <td>-0.0636</td>\n",
       "      <td>-0.1112</td>\n",
       "      <td>-0.5080</td>\n",
       "      <td>-0.4713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>-0.8121</td>\n",
       "      <td>0.3434</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>-0.3246</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>0.5258</td>\n",
       "      <td>0.4680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23810</th>\n",
       "      <td>id_fffb70c0c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>ecf3b6b74</td>\n",
       "      <td>-1.3260</td>\n",
       "      <td>0.3478</td>\n",
       "      <td>-0.3743</td>\n",
       "      <td>0.9905</td>\n",
       "      <td>-0.7178</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4426</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>-0.3195</td>\n",
       "      <td>-0.8086</td>\n",
       "      <td>-0.9798</td>\n",
       "      <td>-0.2084</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>-0.2715</td>\n",
       "      <td>0.3689</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>0.3942</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>-0.7389</td>\n",
       "      <td>0.5505</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.7343</td>\n",
       "      <td>0.2807</td>\n",
       "      <td>0.4116</td>\n",
       "      <td>0.6422</td>\n",
       "      <td>0.2256</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.3808</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>0.2324</td>\n",
       "      <td>0.4392</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.8531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>-0.2012</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>1.5230</td>\n",
       "      <td>0.7101</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23813</th>\n",
       "      <td>id_ffffdd77b</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>972f41291</td>\n",
       "      <td>-0.8598</td>\n",
       "      <td>1.0240</td>\n",
       "      <td>-0.1361</td>\n",
       "      <td>0.7952</td>\n",
       "      <td>-0.3611</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.7450</td>\n",
       "      <td>-6.6300</td>\n",
       "      <td>-4.0950</td>\n",
       "      <td>-7.3860</td>\n",
       "      <td>-1.4160</td>\n",
       "      <td>-3.5770</td>\n",
       "      <td>-0.4775</td>\n",
       "      <td>-2.1500</td>\n",
       "      <td>-4.2520</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23814 rows × 878 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose      droga     g-0     g-1  \\\n",
       "0      id_000644bb2     com_droga     24   D1  b68db1d53  1.0620  0.5577   \n",
       "1      id_000779bfc     com_droga     72   D1  df89a8e5a  0.0743  0.4087   \n",
       "2      id_000a6266a     com_droga     48   D1  18bb41b2c  0.6280  0.5817   \n",
       "3      id_0015fd391     com_droga     48   D1  8c7f86626 -0.5138 -0.2491   \n",
       "4      id_001626bd3     com_droga     72   D2  7cbed3131 -0.3254 -0.4009   \n",
       "...             ...           ...    ...  ...        ...     ...     ...   \n",
       "23809  id_fffb1ceed     com_droga     24   D2  df1d0a5a1  0.1394 -0.0636   \n",
       "23810  id_fffb70c0c     com_droga     24   D2  ecf3b6b74 -1.3260  0.3478   \n",
       "23811  id_fffc1c3f4  com_controle     48   D2  cacb2b860  0.3942  0.3756   \n",
       "23812  id_fffcb9e7c     com_droga     24   D1  8b87a7a83  0.6660  0.2324   \n",
       "23813  id_ffffdd77b     com_droga     72   D1  972f41291 -0.8598  1.0240   \n",
       "\n",
       "          g-2     g-3     g-4  ...    c-91    c-92    c-93    c-94    c-95  \\\n",
       "0     -0.2479 -0.6208 -0.1944  ...  0.2584  0.8076  0.5523 -0.1912  0.6584   \n",
       "1      0.2991  0.0604  1.0190  ...  0.7543  0.4708  0.0230  0.2957  0.4899   \n",
       "2      1.5540 -0.0764 -0.0323  ... -0.6297  0.6103  0.0223 -1.3240 -0.3174   \n",
       "3     -0.2656  0.5288  4.0620  ... -0.6441 -5.6300 -1.3780 -0.8632 -1.2880   \n",
       "4      0.9700  0.6919  1.4180  ...  0.0048  0.6670  1.0690  0.5523 -0.3031   \n",
       "...       ...     ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "23809 -0.1112 -0.5080 -0.4713  ...  0.0262 -0.8121  0.3434  0.5372 -0.3246   \n",
       "23810 -0.3743  0.9905 -0.7178  ...  0.4426  0.0423 -0.3195 -0.8086 -0.9798   \n",
       "23811  0.3109 -0.7389  0.5505  ...  0.3755  0.7343  0.2807  0.4116  0.6422   \n",
       "23812  0.4392  0.2044  0.8531  ...  0.4258 -0.2012  0.1506  1.5230  0.7101   \n",
       "23813 -0.1361  0.7952 -0.3611  ... -1.7450 -6.6300 -4.0950 -7.3860 -1.4160   \n",
       "\n",
       "         c-96    c-97    c-98    c-99  anti-inflammatory  \n",
       "0     -0.3981  0.2139  0.3801  0.4176                  0  \n",
       "1      0.1522  0.1241  0.6077  0.7371                  0  \n",
       "2     -0.6417 -0.2187 -1.4080  0.6931                  0  \n",
       "3     -1.6210 -0.8784 -0.3876 -0.8154                  0  \n",
       "4      0.1094  0.2885 -0.3786  0.7125                  0  \n",
       "...       ...     ...     ...     ...                ...  \n",
       "23809  0.0631  0.9171  0.5258  0.4680                  0  \n",
       "23810 -0.2084 -0.1224 -0.2715  0.3689                  0  \n",
       "23811  0.2256  0.7592  0.6656  0.3808                  0  \n",
       "23812  0.1732  0.7015 -0.6290  0.0740                  0  \n",
       "23813 -3.5770 -0.4775 -2.1500 -4.2520                  0  \n",
       "\n",
       "[23814 rows x 878 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## UNIFICANDO AS DUAS TABELAS EXPOSTAS ACIMA\n",
    "\n",
    "df_antiinflamatorio = pd.merge(df, df_moa[['id', 'anti-inflammatory']], on = 'id')\n",
    "df_antiinflamatorio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d375a2",
   "metadata": {},
   "source": [
    "### Modelos e Resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4f2cdd",
   "metadata": {},
   "source": [
    "— Agora vamos rodar 4 modelos diferentes de aprendizado de máquina, todos bastante simples, pra ver se ele consegue adivinhar os experimentos que vão despertar ou não uma propriedade anti-inflamatória. Cada modelo vai \"ensinar\" o computador de um jeito diferente. É como se cada um fosse um óculos diferente, então pra cada óculos que ele usar, vai \"enxergar\" essa tabela de um jeito um pouquinho diferente. E isso fará ele nós dar respostas diferentes.\n",
    "\n",
    "— Nossa, mãe, isso é meio complicado né? Minha cabeça ja tá doidinha sem entender nada.\n",
    "\n",
    "— Quando você faz um desenho e quer saber se está bom, você pergunta pra uma pessoa só ou pra mais de uma?\n",
    "\n",
    "— Pra várias pessoas, pra ter mais de uma opinião.\n",
    "\n",
    "— É isso que estamos fazendo aqui. Pedindo mais de uma opinião pro computador.\n",
    "\n",
    "— Aaaa, acho que agora eu entendi.\n",
    "\n",
    "— Então vamos começar a rodar os 4, e eu vou pedir pro computador criar um gráfico com os resultados pra gente analisar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6fdfa54f",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# separando conjunto de dados entre teste e treino\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = df_antiinflamatorio.drop(['id', 'droga', 'anti-inflammatory'], axis=1)\n",
    "x = pd.get_dummies(x, columns=['tratamento', 'tempo', 'dose'])\n",
    "y = df_antiinflamatorio['anti-inflammatory']\n",
    "\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.3, stratify=y, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "742765a7",
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "## criando dicionario pra guardar os resultados dos testes\n",
    "resultados = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65ed2e5b",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9962211336599021"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Rodando modelo 1: LogisticRegression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "modelo_rlogistica = LogisticRegression(max_iter=1000)\n",
    "modelo_rlogistica.fit(x_treino, y_treino)\n",
    "resultado_logistic_reg = modelo_rlogistica.score(x_teste, y_teste)\n",
    "\n",
    "resultados[\"LogisticRegression\"] = resultado_logistic_reg\n",
    "resultado_logistic_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "153d31fe",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9969209237228831"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Rodando modelo 2: RandomForestClassifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "modelo_randomforest = RandomForestClassifier()\n",
    "modelo_randomforest.fit(x_treino, y_treino)\n",
    "resultado_randomforest = modelo_randomforest.score(x_teste, y_teste)\n",
    "\n",
    "resultados[\"RandomForest\"] = resultado_randomforest\n",
    "resultado_randomforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1152c0e4",
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9930020993701889"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Rodando modelo 3: LinearSVC\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "#X, y = make_classification(random_state=0)\n",
    "\n",
    "modelo_linearSVC = make_pipeline(StandardScaler(),\n",
    "                    LinearSVC(random_state=0, max_iter=5000))\n",
    "modelo_linearSVC.fit(x_treino, y_treino)\n",
    "resultado_linearSVC = modelo_linearSVC.score(x_teste, y_teste)\n",
    "\n",
    "resultados[\"LinearSVC\"] = resultado_linearSVC\n",
    "resultado_linearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f21114c5",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9969209237228831"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Rodando modelo 4: Dummy\n",
    "## strategy = most_frequent\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "modelo_dummy = DummyClassifier(strategy=\"most_frequent\") # generates predictions by respecting the training set’s class distribution.\n",
    "modelo_dummy.fit(x_treino, y_treino)\n",
    "previsao_dummy = modelo_dummy.predict(x_teste)\n",
    "resultado_dummy = accuracy_score(y_teste, previsao_dummy)\n",
    "\n",
    "resultados[\"Dummy\"] = resultado_dummy\n",
    "resultado_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f19d2666",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# Preparando dados a serem usados nos gráficos\n",
    "\n",
    "# convertendo o dicionario com os dados de tempo para um dataframe do pandas\n",
    "resultados_ML = pd.DataFrame.from_dict(resultados.items())\n",
    "resultados_ML = resultados_ML.rename({0: 'modelo', 1: 'taxa_acerto'}, axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0261502",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### GRÁFICOS DE BARRAS: ACURÁCIA DOS MODELOS #########\n",
    "\n",
    "# Definindo função pra posicionar valores acima das barras no gráfico\n",
    "# esta função foi modificada a partir da original, disponível em: https://stackoverflow.com/a/56780852\n",
    "\n",
    "def show_values_on_bars_float(axs, h_v=\"v\", space=0.4, size = 12.0, color=color):\n",
    "    def _show_on_single_plot(ax):\n",
    "        if h_v == \"v\":\n",
    "            for p in ax.patches:\n",
    "                _x = p.get_x() + p.get_width() / 2\n",
    "                _y = p.get_y() + p.get_height() + float(space)\n",
    "                value = (p.get_height())\n",
    "                value = \"{:.2%}\".format(value)\n",
    "                ax.text(_x, _y, value, ha=\"center\", size=size, color=color) \n",
    "        elif h_v == \"h\":\n",
    "            for p in ax.patches:\n",
    "                _x = p.get_x() + p.get_width() + float(space)\n",
    "                _y = p.get_y() + p.get_height()\n",
    "                value = int(p.get_width())\n",
    "                ax.text(_x, _y, value, ha=\"left\", size=size, color=color)\n",
    "\n",
    "    if isinstance(axs, np.ndarray):\n",
    "        for idx, ax in np.ndenumerate(axs):\n",
    "            _show_on_single_plot(ax)\n",
    "    else:\n",
    "        _show_on_single_plot(axs)\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "# criando e imprimindo os graficos de barras no seaborn\n",
    "\n",
    "#### GRAFICO 1 - ESCALA 0 ~1 ####\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x='modelo', y='taxa_acerto', data=resultados_ML , palette = \"Purples\")#, order = [24, 48, 72])\n",
    "ax.set_title(\"Acurácia dos modelos\".upper(), pad=4.0, fontsize=16, fontweight = 'bold', color = 'dimgrey', y = 1.05)\n",
    "ax.set_xlabel('Nome do Modelo', labelpad = 10.0, fontsize=14,  color = 'dimgrey')\n",
    "ax.set_ylabel('Taxa de Acertos (de 0 a 1)', labelpad = 10.0, fontsize=14, color = 'dimgrey')\n",
    "ax.set_ylim(ymin=0, ymax= 1)\n",
    "#show_values_on_bars_float(ax, size = 15, space = 30, color = 'dimgrey')\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4372e3e9",
   "metadata": {},
   "source": [
    "— Ô mãe, acho que deu alguma coisa errada, esse gráficos tão todos da mesma altura.\n",
    "\n",
    "— Pois é filha, parece que todos os nossos modelos foram muito bons em acertar quais experimentos ativaram o mecanismo de ação anti-inflamatório e quais não. Mas vamos dar um *zoom* nesse gráfico pra ver melhor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "acc817a8",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### GRAFICO 2 - ESCALA 0.99 ~ 1 ####\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x='modelo', y='taxa_acerto', data=resultados_ML , palette = \"Purples\")#, order = [24, 48, 72])\n",
    "ax.set_title(\"Acurácia dos modelos\".upper() + \"\\n(Escala Ampliada)\", pad=4.0, fontsize=16, fontweight = 'bold', color = 'dimgrey', y = 1.05)\n",
    "ax.set_xlabel('Nome do Modelo', labelpad = 10.0, fontsize=14,  color = 'dimgrey')\n",
    "ax.set_ylabel('Taxa de Acertos (de 0.99 a 1)', labelpad = 10.0, fontsize=14, color = 'dimgrey')\n",
    "ax.set_ylim(ymin=0.99, ymax= 1)\n",
    "show_values_on_bars_float(ax, size = 14,space = 0, color = 'dimgrey')\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6647c71",
   "metadata": {},
   "source": [
    "— Bom, então nossos 4 modelos tiveram mais de 99% de acertos.\n",
    "\n",
    "— Nossa, então foi muito bom! O computador é muito esperto ! Será que a gente falar pra ele adivinhar se vai chover amanhã? Aí a gente já pode testar nossas hipóteses sobre a chuva.\n",
    "\n",
    "— Calma filha, antes vamos entender o que aconteceu. — Clara falava enquanto escrevia algumas linhas de código."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc46749",
   "metadata": {},
   "source": [
    "# Conclusões"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5998ab86",
   "metadata": {},
   "source": [
    "— Olha aqui onde está o problema:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7dbe7794",
   "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>anti-inflammatory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.9969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anti-inflammatory\n",
       "0             0.9969\n",
       "1             0.0031"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>anti-inflammatory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anti-inflammatory\n",
       "0              23741\n",
       "1                 73"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_moa['anti-inflammatory'].value_counts(normalize=True).to_frame().round(4))\n",
    "display(df_moa['anti-inflammatory'].value_counts(normalize=False).to_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120f836a",
   "metadata": {},
   "source": [
    "— Temos 23741 casos nos quais não é ativado nenhum mecanismo anti-inflamatório, e só 73 casos com ativação. Se colocarmos isso em porcentagem, temos 99,69% contra 0,03%. A diferença é muito grande.\n",
    "\n",
    "— Porque isso é um problema, mamãe?\n",
    "\n",
    "— Porque ensinamos pro computador que praticamente sempre a resposta é 0. Ele viu pouquíssimos exemplos de quando a resposta é 1, então não consegue adivinhar muito bem quando isso acontece. Por exemplo, um dos modelos que nós usamos, o modelo Dummy, não faz nada além de chutar que nenhum experimento ativaria o mecanismo de ação anti-inflamatório, e como nós temos 99,69% de casos onde isso é verdade, ele acertou 99,69%.\n",
    "\n",
    "— Nossa, não entendi nada... Você falou complicado demais.\n",
    "\n",
    "— Se eu te falar que vi um elefante ontem, e pedir pra você adivinhar se ele tinha tromba, qual seria sua resposta?\n",
    "\n",
    "— Eu diria que sim, que ele tem tinha tromba sim.\n",
    "\n",
    "— Porque?\n",
    "\n",
    "— Porque eu nunca vi um elefante sem tromba.\n",
    "\n",
    "— Exatamente. O computador tá fazendo a mesma coisa. Seria como mostrar pra ele 23741 elefantes com tromba e 73 elefantes sem tromba. Então, quando a gente falar que viu um elefante e pedir pra ele adivinhar se tem tromba, ele vai dizer que sim porque ele quase nunca viu um elefante sem tromba... Falando nisso, você percebeu que acabou de testar sua hipótese?\n",
    "\n",
    "— Não, eu fiz isso é?\n",
    "\n",
    "— Sua hipótese era que...\n",
    "\n",
    "— Todos os elefantes tem tromba. Mas como eu não existe elefante sem tromba, não tem como a hipótese estar errada. Eu achei que seria bom ela estar certa.\n",
    "\n",
    "— Isso mesmo, você entendeu. E eu escolhi o caso do anti-inflamatório pra te mostrar que nem sempre a resposta certa está realmente certa. O importante é você sempre estar se perguntando, sempre estar investigando as coisas. É igual quando você chegou aqui na sala pulando num pé só e equilibrando o livro na cabeça. Você tem que estar sempre atenta ao livro, senão ele cai.\n",
    "\n",
    "...\n",
    "\n",
    "Passou-se uma hora e Clara conseguiu finalizar suas análises. Desligou o computador e, indo para o quarto, notou que Joana tinha dormido no sofá. Ao pegar ela no colo pra levar pra cama, Joana se mexeu e falou pausadamente:\n",
    "\n",
    "— Mamãe... eu tenho... uma outra hipótese. — dando um longo bocejo em seguida.\n",
    "\n",
    "— Que você está com sono? — disse Clara, rindo.\n",
    "\n",
    "— Tô mesmo, mas não é essa... A hipótese é que já passou da meia noite.\n",
    "\n",
    "— Passou mesmo, sua hipótese tá confirmada.\n",
    "\n",
    "— Então eu já posso te desejar um feliz dia das mães! — disse ela dando um abraço e um beijo na bochecha de Clara."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754cca3b",
   "metadata": {},
   "source": [
    "# Fontes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d2f3f5",
   "metadata": {},
   "source": [
    "## Citações"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1df872",
   "metadata": {},
   "source": [
    "- [1] Wes McKinney (2011). \"pandas: a Foundational Python Library for Data Analysis and Statistics\". Disponível em: https://www.dlr.de/sc/Portaldata/15/Resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf\n",
    "\n",
    "\n",
    "- [2] Joffre Rezende (2004). \"Droga, Fármaco, Medicamento, Remédio\". Disponível em: http://www.jmrezende.com.br/droga.htm\n",
    "\n",
    "\n",
    "- Apesar de não citados explicitamente, os materiais disponibilizados pelo time da Alura ao longo da Imersão Dados estão por trás de muito do que foi escrito, tanto em programação, quanto nas explicações biológicas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ac512d",
   "metadata": {},
   "source": [
    "## Inspirações"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb42f6a",
   "metadata": {},
   "source": [
    "- O livro *Perto do Coração Selvagem*, de Clarice Lispector, foi de onde tirei o estilo de narração centrado na Joana.\n",
    "\n",
    "\n",
    "- A idéia de uma explicação mesclada com um diálogo veio do dicionário etimológico [Origem da Palavra](https://origemdapalavra.com.br/artigo/burocracia/).\n",
    "\n",
    "\n",
    "- [Artigo](https://towardsdatascience.com/how-to-create-award-winning-data-visualizations-518127fc30ef) de André Sionek sobre Visualização de Dados e seu [Notebook premiado no Kaggle](https://www.kaggle.com/andresionek/is-there-any-job-out-there-kaggle-vs-glassdoor?scriptVersionId=24426116) \n",
    "\n",
    "\n",
    "- Aprendi muito lendo o caderno de [Daiane Klein](https://github.com/daianeklein/imersao-dados-Alura), que recebeu destaque na última imersão dados da Alura, então deixo aqui meu agradecimento."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbbf58f",
   "metadata": {},
   "source": [
    "## Documentações"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1235778",
   "metadata": {},
   "source": [
    "**Documentação dos modelos de Machine Learning utilizados:**\n",
    "\n",
    "- Scikit-learn\n",
    "\n",
    "[DummyClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)\n",
    "\n",
    "[LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC.fit) \n",
    "\n",
    "[RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit) \n",
    "\n",
    "[LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0a9738",
   "metadata": {},
   "source": [
    "**Documentações muito utilizadas na elaboração dos gráficos:**\n",
    "\n",
    "- Matplotlib\n",
    "\n",
    "[matplotlib.pyplot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html) \n",
    "\n",
    "[matplotlib.text](https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text.set_fontweight)\n",
    "\n",
    "[Matplolib Cheatsheet](https://github.com/matplotlib/cheatsheets/blob/master/cheatsheets.pdf)\n",
    "\n",
    "[Matplotlib Colors](https://matplotlib.org/stable/tutorials/colors/colormaps.html)\n",
    "\n",
    "- Seaborn\n",
    "\n",
    "[Controlling Figure Aesthetics](https://seaborn.pydata.org/tutorial/aesthetics.html?highlight=aesthetic)\n",
    "\n",
    "[Seaborn Histplot](https://seaborn.pydata.org/generated/seaborn.histplot.html?highlight=histplot#seaborn.histplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dac7a18",
   "metadata": {},
   "source": [
    "# Autor e Contato"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5b87b2",
   "metadata": {},
   "source": [
    "Nome: Murilo Amaral\n",
    "\n",
    "Github: https://github.com/mumaral\n",
    "\n",
    "LinkedIn: https://www.linkedin.com/in/mumaral/\n",
    "\n",
    "Email: [mumaral@protonmail.com](mailto:mumaral@protonmail.com)"
   ]
  }
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