20210506_Aula04_Victor.ipynb

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      "name": "20210506_Aula04_Victor.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNCD2xdUAGujbPKv05Y6q2K",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/victorijsn/imersaodados3/blob/main/20210506_Aula04_Victor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCdJQJEcQonx"
      },
      "source": [
        "# Respostas aos desafios da aula 04 - ImersaoDados - Alura\n",
        "Victor Nunes Toscano\n",
        "\n",
        "Linkedin: https://www.linkedin.com/in/vntoscano/\n",
        "\n",
        "Data: 06/05/2021\n",
        "\n",
        "## Contexto\n",
        "\n",
        "- Visualizando os dados\n",
        "\n",
        "## Desafios:\n",
        "1. Desafio 01: Encontrar o top 10, das ações do MOA (inibidor, agonista...)\n",
        "2. Desafio 02: Criar a coluna eh_controle para quando na linha tratamento == com_controle\n",
        "3. Desafio 03: Criar três colunas para indicar o tempo 24, 48, 72\n",
        "4. Desafio 04: Estudar sobre combinações de DF https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html\n",
        "5. Desafio 05: Fazer análise mais detalhada considerando tempo e dose, para comparar as distribuições (Escolher uma droga e comparar com controle)\n",
        "6. Desafio 06: Descobrir se tem algum composto que dependendo da configuração do experimento, ativa ou não ativa algum MOA\n",
        "7. Desafio 07: Descobrir se tem algum composto que dependendo da configuração do experimento, ativa MOAs diferentes\n",
        "8. Desafio 08: Resumo do que você aprendeu com os dados\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vC0PwhyRh2b"
      },
      "source": [
        "Importar a biblioteca [pandas](https://pandas.pydata.org/docs/index.html), a base de dados, as bliblitecas de gráficos e algumas adequações."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oI6FJlFGQlF2"
      },
      "source": [
        "# Bibliotecas\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Config dos plots\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkYSi2TBkWhb"
      },
      "source": [
        "# Importar os dados\n",
        "dados_experimentos = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true', compression = 'zip')\n",
        "dados_resultados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFse2h0Vcrp4"
      },
      "source": [
        "# Ajustes nos nomes das variáveis\n",
        "mapa = {'droga': 'composto'}\n",
        "dados_experimentos.rename(columns=mapa, inplace=True)\n",
        "dados_experimentos.columns = dados_experimentos.columns.str.replace('-','_')\n",
        "dados_experimentos['media_genes'] = dados_expr_result.loc[:,'g_0':'g_771'].mean(axis=1)\n",
        "dados_experimentos['media_celulas'] = dados_expr_result.loc[:,'c_0':'c_99'].mean(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "smoCIiHC7-XH",
        "outputId": "1e529a37-6c22-4b9f-8fb2-57ed2b6297ae"
      },
      "source": [
        "# Visualizar as 5 primeiras linhas da base de experimento\n",
        "dados_experimentos.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
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              "      <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",
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              "      <td>0.1778</td>\n",
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              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
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              "      <td>-0.0916</td>\n",
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              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
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              "      <td>0.2912</td>\n",
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              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</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>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
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              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
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              "      <td>-0.0604</td>\n",
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              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</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",
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              "      <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>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</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>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</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  ...    c_96    c_97    c_98    c_99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "RRYgknEj9-sb",
        "outputId": "9f0e5e2e-e846-41b6-b4e1-a39b75ac7abf"
      },
      "source": [
        "# Visualizar as 5 primeiras linhas da base de resultados\n",
        "dados_resultados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <th>acetylcholine_receptor_agonist</th>\n",
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              "      <th>aldehyde_dehydrogenase_inhibitor</th>\n",
              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
              "      <th>angiogenesis_inhibitor</th>\n",
              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>protein_synthesis_inhibitor</th>\n",
              "      <th>protein_tyrosine_kinase_inhibitor</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
              "      <th>ribonucleoside_reductase_inhibitor</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
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              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <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",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 207 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ...  wnt_inhibitor\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",
              "[5 rows x 207 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BK5zyufK_ACx"
      },
      "source": [
        "A base de dados de resultados está dividida por colunas e em cada coluna temos os componentes investigados e no sufixo do nome da coluna temos a ação do composto. A partir da segunda coluna temos dummies (valores binários de 0 e 1) onde 0 significa que o composto **não** teve ação na molécula ou proteína testada e 1 que houve ação na molécula ou proteína testada.\n",
        "\n",
        "Ações possíveis são:\n",
        "1. **Agonista:** que potencializa a ação da molécula (ou outro componente);\n",
        "2. **Antagonista:** que diminui a ação da molécula (ou outro componente);\n",
        "3. **Inibidor:** que inibe a ação da molécula (ou outro componente);\n",
        "4. **Ativador:** que ativa a ação da molécula (ou outro componente);\n",
        "5. entre outras.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwZLZN9aR6bQ"
      },
      "source": [
        "### Resposta 01\n",
        "#### Encontrar o top 10, das ações do MOA (inibidor, agonista...)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMy2bGVhS2GV",
        "outputId": "b6f0cfef-0800-4f5c-9944-dfe52ff5a12d"
      },
      "source": [
        "# Contar os resultados alcançados nos experimentos\n",
        "contagem_moa = dados_resultados.drop(['id'], axis=1).sum().sort_values(ascending=False)\n",
        "contagem_moa[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nfkb_inhibitor                   832\n",
              "proteasome_inhibitor             726\n",
              "cyclooxygenase_inhibitor         435\n",
              "dopamine_receptor_antagonist     424\n",
              "serotonin_receptor_antagonist    404\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvKn8ITgkQwm",
        "outputId": "0a7d85e6-dbeb-4445-9787-3580950288aa"
      },
      "source": [
        "# Arruma a base com os sufixos dos componentes testados\n",
        "acao = [i.split('_')[-1] for i in contagem_moa.index] #for para cada linha de índice\n",
        "base_acao = contagem_moa.reset_index()\n",
        "base_acao['acao'] = acao # juntei as ações do medicamentos na tabela\n",
        "mapa = {'index':'moa',0: 'n','acao':'acao'} \n",
        "base_acao.rename(columns=mapa, inplace=True) #renomeio as colunas\n",
        "base_acao[['acao','n']].groupby(by=['acao']).sum()['n'].sort_values(ascending=False)[:10] #plot do resultado"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "acao\n",
              "inhibitor            9693\n",
              "antagonist           3449\n",
              "agonist              2330\n",
              "blocker               323\n",
              "agent                 150\n",
              "activator             115\n",
              "local                  80\n",
              "immunosuppressant      73\n",
              "anti-inflammatory      73\n",
              "antioxidant            73\n",
              "Name: n, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgrcgIGQSzAo"
      },
      "source": [
        "### Resposta 02\n",
        "#### Criar a coluna eh_controle para quando na linha tratamento == com_controle\n",
        "\n",
        "Utilizo a base de experimentos para identificar quais são controles.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "rpNicvAZ5rVA",
        "outputId": "8567827f-ae82-4412-d0e9-3c0550bc8397"
      },
      "source": [
        "mapa={'com_controle':1, 'com_droga':0}\n",
        "dados_experimentos['eh_controle'] = dados_experimentos['tratamento'].map(mapa)\n",
        "dados_experimentos.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
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              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</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",
              "      <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>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</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",
              "      <td>1</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>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</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",
              "      <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>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.8640</td>\n",
              "      <td>-2.5090</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 878 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo  ...    c_98    c_99  eh_controle\n",
              "23809  id_fffb1ceed     com_droga     24  ...  0.5258  0.4680            0\n",
              "23810  id_fffb70c0c     com_droga     24  ... -0.2715  0.3689            0\n",
              "23811  id_fffc1c3f4  com_controle     48  ...  0.6656  0.3808            1\n",
              "23812  id_fffcb9e7c     com_droga     24  ... -0.6290  0.0740            0\n",
              "23813  id_ffffdd77b     com_droga     72  ... -2.1500 -4.2520            0\n",
              "\n",
              "[5 rows x 878 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM1YnzZwTHVX"
      },
      "source": [
        "### Resposta 03\n",
        "#### Criar três colunas para indicar o tempo 24, 48, 72\n",
        "Usar a função get_dummies para gerar as variáveis binárias."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "a0NwL3fgBLZQ",
        "outputId": "ab36ddfd-034d-4386-e30e-0b1e2ec7295b"
      },
      "source": [
        "# Dummies de tempo\n",
        "dummies_tempo = pd.get_dummies(dados_experimentos['tempo'])\n",
        "dados_experimentos = pd.concat([dados_experimentos,dummies_tempo], axis=1) # função concatenar\n",
        "dados_experimentos.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</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>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>...</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</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",
              "      <th>eh_controle</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</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>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 881 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ... eh_controle  24  48  72\n",
              "0  id_000644bb2  com_droga     24   D1  ...           0   1   0   0\n",
              "1  id_000779bfc  com_droga     72   D1  ...           0   0   0   1\n",
              "2  id_000a6266a  com_droga     48   D1  ...           0   0   1   0\n",
              "3  id_0015fd391  com_droga     48   D1  ...           0   0   1   0\n",
              "4  id_001626bd3  com_droga     72   D2  ...           0   0   0   1\n",
              "\n",
              "[5 rows x 881 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_JRRHsPTio7"
      },
      "source": [
        "### Resposta 04\n",
        "#### Estudar sobre [combinações de Datas Frames](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html).\n",
        "\n",
        "Existem 4 formas de combinar os dados através do Pandas. \n",
        "1. Com base na documentação, temos a função ```concat``` que acrescenta colunas a uma base de dados.\n",
        "2. A segunda função é a ```append``` que acrescenta linhas a uma base de dados.\n",
        "3. Por fim a função ```merge``` que possui diversos tipos e combina duas bases através de uma ou mais variáveis que servem de índice. Além disso, existem tipos de combinações que preservam ou descartam dados. Por exemplo, o caso o argumento ```how='inner'```, a função irá preservar os dados que são **comuns** às duas bases de dados.\n",
        "4. São 4 tipos de *joins* (ou junções). Para entender melhor, suponha duas bases de dados *x* e *y*:\n",
        "- **left** - Mantêm os dados da base da esquerda (*x*), para combinar os dados;\n",
        "- **right** - Mantêm os dados da base da direita (*y*), para combinar os dados;\n",
        "- **outer** - Une os dados de ambas as bases comparadas e também é conhecida com *full join*;\n",
        "- **inner** - Mantêm os dados que são comuns às duas bases de dados.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-OxNy9aEFYM"
      },
      "source": [
        "# Reproduzir o merge feito em aula para realizar a análise do desafio 5.\n",
        "dados_resultados['n_moa'] = dados_resultados.drop(['id'], axis=1).sum(axis=1)\n",
        "dados_resultados['ativo_moa'] = (dados_resultados['n_moa']!=0)\n",
        "dados_expr_result = pd.merge(dados_experimentos, dados_resultados[['id','n_moa', 'ativo_moa']], on='id')\n",
        "dados_expr_result.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBq3KwsqUF_T",
        "outputId": "20a69aeb-1473-4cd9-a416-d771b5686eec"
      },
      "source": [
        "# Verificar se os resultados batem com o da aula - dados de tratamento\n",
        "dados_expr_result.query('tratamento == \"com_droga\"' )['ativo_moa'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     14447\n",
              "False     7501\n",
              "Name: ativo_moa, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gy-3O5GpUVkJ",
        "outputId": "5715de2e-52a9-4962-a49c-0ff2944ebf6d"
      },
      "source": [
        "# Verificar se os resultados batem com o da aula - dados de controle\n",
        "dados_expr_result.query('tratamento == \"com_controle\"' )['ativo_moa'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    1866\n",
              "Name: ativo_moa, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loj6WHd5T3Uu"
      },
      "source": [
        "### Resposta 05\n",
        "#### Fazer análise mais detalhada considerando tempo e dose, para comparar as distribuições (Escolher uma droga e comparar com controle)\n",
        "\n",
        "Listar os compostos mais recorrentes, depois do que utilizado utilizadas como controle."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTNjrmMgTCZM",
        "outputId": "160ad019-4c53-44fd-cbaa-88d8fa67ac3e"
      },
      "source": [
        "dados_expr_result['composto'].value_counts()[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cacb2b860    1866\n",
              "87d714366     718\n",
              "9f80f3f77     246\n",
              "8b87a7a83     203\n",
              "5628cb3ee     202\n",
              "Name: composto, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhWqfpBvTmQb"
      },
      "source": [
        "Utilizando, iremos comparar os resultados de dois compostos 87d714366 e o composto de controle cacb2b860."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "veO3xcLqU3Kq",
        "outputId": "473fda15-0713-4ea3-d7f2-0e50666ec20d"
      },
      "source": [
        "composto_principal = dados_expr_result['composto'].value_counts().index[:2]\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.boxplot(data = dados_expr_result.query('composto in @composto_principal'), y= 'media_celulas', x='composto', hue='tempo')\n",
        "plt.title('Comparação do efeito do tratamento nas manifestações médias das células ao longo do tempo',fontsize = 14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "qHAc2GnmfN2J",
        "outputId": "c7715c4e-037c-43f3-e793-85154b1d00ab"
      },
      "source": [
        "composto_principal = dados_expr_result['composto'].value_counts().index[:2]\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.boxplot(data = dados_expr_result.query('composto in @composto_principal'), y= 'media_celulas', x='composto', hue='dose')\n",
        "plt.title('Comparação do efeito do tratamento nas manifestações médias das células em relação às doses aplicadas',fontsize = 12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "LSYoJg0zY5p0",
        "outputId": "2f898409-8b19-4894-e5c5-c1e03dd9745e"
      },
      "source": [
        "# histograma das manifestações dos dois componentes selecionados\n",
        "dados_comp_princ = dados_expr_result.query('composto in @composto_principal')\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(dados_comp_princ.query('tratamento==\"com_droga\"')['media_celulas'], bins=30)\n",
        "plt.hist(dados_comp_princ.query('tratamento==\"com_controle\"')['media_celulas'], bins=30)\n",
        "plt.legend({'tratamento','controle'})\n",
        "plt.title(\"Histograma das manifestações dos dois compostos nas células\", fontsize = 16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1QaYFv1UOMo"
      },
      "source": [
        "### Resposta 06\n",
        "#### Descobrir se tem algum composto que dependendo da configuração do experimento, ativa ou não ativa algum MOA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "runR7oQCglfL"
      },
      "source": [
        "# Não consegui desenvolver o desafio."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Z6qJYI18m2g"
      },
      "source": [
        "### Resposta 07\n",
        "#### Descobrir se tem algum composto que dependendo da configuração do experimento, ativa MOAs diferentes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSgpL6johig3"
      },
      "source": [
        "# Não consegui desenvolver o desafio."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thgMC3Gfcore"
      },
      "source": [
        "### Resposta 08\n",
        "#### Resumo do que você aprendeu com os dados\n",
        "\n",
        "1. Existem diversas formas de ativação do composto que podem modificar os genes ou células. As principais são as inibidoras, antagonistas e agonistas.\n",
        "2. A combinação de dados é uma poderosa ferramenta para comparar os resultados com os experimentos realizados. Através dessa função é possível verificar o número de ativações e quais ações são mais comuns desses componentes.\n",
        "3. O tempo parece ser um elemento extremamente importante para a análise, de modo que é possível observar mudanças consideráveis nas células após a administração da droga.\n",
        "4. Já a dosagem não parece ter diferenças estatisticamente significativas nas manifestações das células.\n",
        "5. Sobre as ativações, não consegui desenvolver. Não consegui entender o que seria a configuração do experimento.\n"
      ]
    }
  ]
}