Análise_Aula_4.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Análise Aula 4.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPuVGngs6IvyJUoSU9qsf4B",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MisterGusMan/Alura_ImersaoDados3/blob/main/An%C3%A1lise_Aula_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jp-Ei6m19wA1"
      },
      "source": [
        "Nesta aula, vamos explorar o método `merge` para combinar diferentes tipos de dados, junto com uma análise mais sucinta dos resultados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "l7X9tVwD9gKq",
        "outputId": "86c89bb8-e673-43ff-8753-cc83af8ec91f"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (12,8)\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "df = pd.read_csv(url_dados, compression = 'zip')\n",
        "df.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>tratamento</th>\n",
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              "      <th>c-99</th>\n",
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              "  <tbody>\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",
              "      <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",
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              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
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              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</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",
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              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
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              "      <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",
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              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</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>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",
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              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</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",
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              "    <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>-0.5477</td>\n",
              "      <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",
              "      <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",
              "    </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>-2.1220</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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "Q-CdVAz4-bHd",
        "outputId": "ca1f44e5-d7dd-4504-cb7b-f16f06faad45"
      },
      "source": [
        "df.rename(columns={'droga':'composto'}, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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-60</th>\n",
              "      <th>c-61</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</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",
              "    </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>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</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",
              "    </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>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</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",
              "    </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>-0.5477</td>\n",
              "      <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",
              "      <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",
              "    </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>-2.1220</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": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paAxR3-a_Kvl"
      },
      "source": [
        "Junto com o DataFrame de nosso experimento, vamos trabalhar com um segundo DF focando nos resultados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "xHSdZLC6_mAG",
        "outputId": "161d1a59-b86f-4da3-9879-f21e38f111c3"
      },
      "source": [
        "dfr = pd.read_csv('https://raw.githubusercontent.com/alura-cursos/imersaodados3/main/dados/dados_resultados.csv')\n",
        "dfr.head()"
      ],
      "execution_count": 59,
      "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",
              "      <th>acat_inhibitor</th>\n",
              "      <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",
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              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
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              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
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              "      <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",
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              "      <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",
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              "      <th>rna_polymerase_inhibitor</th>\n",
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              "      <th>serotonin_receptor_antagonist</th>\n",
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              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
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              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
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              "      <th>thrombin_inhibitor</th>\n",
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              "      <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",
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              "      <th>vitamin_b</th>\n",
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              "      <th>wnt_inhibitor</th>\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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>4</th>\n",
              "      <td>id_001626bd3</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>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": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6SXcNTM_2G8",
        "outputId": "250cc77a-4ed9-4338-8eb5-f070e4bb8492"
      },
      "source": [
        "dfr.info()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23814 entries, 0 to 23813\n",
            "Columns: 207 entries, id to wnt_inhibitor\n",
            "dtypes: int64(206), object(1)\n",
            "memory usage: 37.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7i5OsNg1_7pF",
        "outputId": "724214aa-18e3-4a28-dc73-aa7f30eda5e4"
      },
      "source": [
        "dfr.columns"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', '5-alpha_reductase_inhibitor', '11-beta-hsd1_inhibitor',\n",
              "       'acat_inhibitor', 'acetylcholine_receptor_agonist',\n",
              "       'acetylcholine_receptor_antagonist', 'acetylcholinesterase_inhibitor',\n",
              "       'adenosine_receptor_agonist', 'adenosine_receptor_antagonist',\n",
              "       'adenylyl_cyclase_activator',\n",
              "       ...\n",
              "       'tropomyosin_receptor_kinase_inhibitor', 'trpv_agonist',\n",
              "       'trpv_antagonist', 'tubulin_inhibitor', 'tyrosine_kinase_inhibitor',\n",
              "       'ubiquitin_specific_protease_inhibitor', 'vegfr_inhibitor', 'vitamin_b',\n",
              "       'vitamin_d_receptor_agonist', 'wnt_inhibitor'],\n",
              "      dtype='object', length=207)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "zZL2EUck_4F0",
        "outputId": "65393bb6-b113-4dfc-d7ff-64c68ab336ed"
      },
      "source": [
        "dfr.describe()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <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>autotaxin_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",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.00000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.000714</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.001008</td>\n",
              "      <td>0.007979</td>\n",
              "      <td>0.012640</td>\n",
              "      <td>0.003065</td>\n",
              "      <td>0.002268</td>\n",
              "      <td>0.004031</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.011338</td>\n",
              "      <td>0.015117</td>\n",
              "      <td>0.002771</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>0.001764</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.002016</td>\n",
              "      <td>0.003737</td>\n",
              "      <td>0.003359</td>\n",
              "      <td>0.001512</td>\n",
              "      <td>0.001554</td>\n",
              "      <td>0.003065</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.001806</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.000546</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.003065</td>\n",
              "      <td>0.001512</td>\n",
              "      <td>0.000966</td>\n",
              "      <td>0.002058</td>\n",
              "      <td>0.001974</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.004073</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>0.004031</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004325</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>0.002352</td>\n",
              "      <td>0.009364</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>0.002813</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.00147</td>\n",
              "      <td>0.001554</td>\n",
              "      <td>0.001050</td>\n",
              "      <td>0.009910</td>\n",
              "      <td>0.016965</td>\n",
              "      <td>0.001848</td>\n",
              "      <td>0.001512</td>\n",
              "      <td>0.001512</td>\n",
              "      <td>0.001050</td>\n",
              "      <td>0.011212</td>\n",
              "      <td>0.001050</td>\n",
              "      <td>0.002981</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>0.002520</td>\n",
              "      <td>0.001260</td>\n",
              "      <td>0.000798</td>\n",
              "      <td>0.001554</td>\n",
              "      <td>0.001260</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>0.001512</td>\n",
              "      <td>0.005333</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.001050</td>\n",
              "      <td>0.002016</td>\n",
              "      <td>0.013270</td>\n",
              "      <td>0.003065</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.007139</td>\n",
              "      <td>0.001092</td>\n",
              "      <td>0.001638</td>\n",
              "      <td>0.001260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.026709</td>\n",
              "      <td>0.027483</td>\n",
              "      <td>0.031731</td>\n",
              "      <td>0.088967</td>\n",
              "      <td>0.111716</td>\n",
              "      <td>0.055283</td>\n",
              "      <td>0.047566</td>\n",
              "      <td>0.063365</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.105876</td>\n",
              "      <td>0.122022</td>\n",
              "      <td>0.052573</td>\n",
              "      <td>0.017143</td>\n",
              "      <td>0.041960</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.044851</td>\n",
              "      <td>0.061020</td>\n",
              "      <td>0.057864</td>\n",
              "      <td>0.038852</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.055283</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.042456</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.023359</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.027483</td>\n",
              "      <td>0.055283</td>\n",
              "      <td>0.038852</td>\n",
              "      <td>0.031063</td>\n",
              "      <td>0.045315</td>\n",
              "      <td>0.044383</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.006480</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.063693</td>\n",
              "      <td>0.028236</td>\n",
              "      <td>0.063365</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>...</td>\n",
              "      <td>0.065625</td>\n",
              "      <td>0.028236</td>\n",
              "      <td>0.048437</td>\n",
              "      <td>0.096317</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.052969</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.03831</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.032384</td>\n",
              "      <td>0.099057</td>\n",
              "      <td>0.129142</td>\n",
              "      <td>0.042946</td>\n",
              "      <td>0.038852</td>\n",
              "      <td>0.038852</td>\n",
              "      <td>0.032384</td>\n",
              "      <td>0.105293</td>\n",
              "      <td>0.032384</td>\n",
              "      <td>0.054522</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.028236</td>\n",
              "      <td>0.050133</td>\n",
              "      <td>0.035472</td>\n",
              "      <td>0.028236</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.035472</td>\n",
              "      <td>0.017143</td>\n",
              "      <td>0.038852</td>\n",
              "      <td>0.072834</td>\n",
              "      <td>0.027483</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.032384</td>\n",
              "      <td>0.044851</td>\n",
              "      <td>0.114429</td>\n",
              "      <td>0.055283</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.084190</td>\n",
              "      <td>0.033025</td>\n",
              "      <td>0.040436</td>\n",
              "      <td>0.035472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 206 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       5-alpha_reductase_inhibitor  ...  wnt_inhibitor\n",
              "count                 23814.000000  ...   23814.000000\n",
              "mean                      0.000714  ...       0.001260\n",
              "std                       0.026709  ...       0.035472\n",
              "min                       0.000000  ...       0.000000\n",
              "25%                       0.000000  ...       0.000000\n",
              "50%                       0.000000  ...       0.000000\n",
              "75%                       0.000000  ...       0.000000\n",
              "max                       1.000000  ...       1.000000\n",
              "\n",
              "[8 rows x 206 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGx9RTxTAS8j"
      },
      "source": [
        "Embora este seja um DF extenso com 207 colunas, os valores destas colunas representam mecanismos de ação, onde ocorreu a ativação ou não. Como estamos tratando de uma váriavel booleana (Verdadeiro ou Falso), as colunas apresentam apenas valores 0 (Não occoreu ativação) e 1 (Ocorreu ativação)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNrRyMphALn8",
        "outputId": "20db0029-8b09-4c7b-a523-80a17d8ea181"
      },
      "source": [
        "dfr['acetylcholine_receptor_agonist'].unique()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yc_BlUYIAix0",
        "outputId": "713c4164-fd38-4b4a-c5a4-fd930222bf39"
      },
      "source": [
        "dfr['11-beta-hsd1_inhibitor'].unique()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAu2IQZVBjxr"
      },
      "source": [
        "Para definir a contagem de ativações dos nossos compostos, vamos somar os valores de todas as colunas. Antes disso, entretanto, temos que remover os valores não inteiros, presentes na coluna `id`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggKKrUTXCnfl",
        "outputId": "aa6c4313-2e32-45ca-fb06-503ad2ba8203"
      },
      "source": [
        "atv = dfr.drop('id', axis=1).sum().sort_values(ascending=False)\n",
        "atv"
      ],
      "execution_count": 65,
      "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",
              "                                             ... \n",
              "protein_phosphatase_inhibitor                   6\n",
              "autotaxin_inhibitor                             6\n",
              "diuretic                                        6\n",
              "erbb2_inhibitor                                 1\n",
              "atp-sensitive_potassium_channel_antagonist      1\n",
              "Length: 206, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4CrxianDvde"
      },
      "source": [
        "Podemos perceber que os 5 compostos com mais ativações são inibidores, um resultado que será explorado mais tarde na análise. Vamos dar uma olhada nas ativações."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_cWdtvKF97k"
      },
      "source": [
        "Vamos verificar se a linha específica que vamos escolher foi ativada em algum momento, e quantas vezes isso ocorreu:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyAfiIzmE3gd",
        "outputId": "a4e10f20-9f63-4a06-f8aa-0db40a71caa5"
      },
      "source": [
        "dfr.drop('id', axis=1).sum(axis=1)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        0\n",
              "2        3\n",
              "3        0\n",
              "4        1\n",
              "        ..\n",
              "23809    1\n",
              "23810    1\n",
              "23811    0\n",
              "23812    1\n",
              "23813    0\n",
              "Length: 23814, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdEiTKt8Gdhj"
      },
      "source": [
        "E aqui temos o nosso resultado. Na primeira linha tivemos uma única ativação. Já na segunda não obtivemos nenhuma. Na terceira tivemos 3, e assim por diante. Nesse que tivemos a resposta como 0, precisaríamos saber se foi um controle, ou se realmente o composto não ativou nenhuma resposta."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "xEnZIhW2HTyu",
        "outputId": "e734822b-ea50-4db4-9484-bb9232973ec6"
      },
      "source": [
        "dfr.head()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "    .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>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
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              "      <th>acetylcholinesterase_inhibitor</th>\n",
              "      <th>adenosine_receptor_agonist</th>\n",
              "      <th>adenosine_receptor_antagonist</th>\n",
              "      <th>adenylyl_cyclase_activator</th>\n",
              "      <th>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <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",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <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",
              "    </tr>\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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "IOTrPKurHWGE",
        "outputId": "f1c5e133-fa1c-4e7d-bce2-2293f9d1d03e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
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              "      <td>48</td>\n",
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              "      <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>-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": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14KvGVrHJVop"
      },
      "source": [
        "Na base de dados resultados não temos a informação que queremos, referente ao tratamento. Nós só temos a coluna tratamento na base de dados inteira.\n",
        "\n",
        "No tratamento só temos a coluna id, e os mecanismos de ação que foram ativados por aquela coluna. Então, agora, precisamos de alguma maneira, mesclar essas informações.\n",
        "\n",
        "Antes de fazer esse passo, de mesclar, vamos criar duas novas colunas na base de dados resultado. Vamos criar uma primeira coluna que vamos chamar de n_moa(número de moas ativos), que é justamente o que havíamos calculado anteriormente, dessa maneira:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68WKyVvoHxNl",
        "outputId": "e3eff54f-1b42-4c48-fd9b-97fcb3d12f8d"
      },
      "source": [
        "dfr['n_moa'] = dfr.drop('id', axis=1).sum(axis=1)\n",
        "dfr['n_moa']"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        0\n",
              "2        3\n",
              "3        0\n",
              "4        1\n",
              "        ..\n",
              "23809    1\n",
              "23810    1\n",
              "23811    0\n",
              "23812    1\n",
              "23813    0\n",
              "Name: n_moa, Length: 23814, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHkY-MEUJh2e"
      },
      "source": [
        "Vamos então verificar se a coluna apresenta resultados diferentes de 0; se for diferente de 0 ela retorna a mensagem 'true'; se for igual a 0 retorna 'false':"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eNaD1_pJi4-",
        "outputId": "466bddb9-67d2-42b4-8c7c-4e14de13790a"
      },
      "source": [
        "dfr['n_moa'] != 0"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         True\n",
              "1        False\n",
              "2         True\n",
              "3        False\n",
              "4         True\n",
              "         ...  \n",
              "23809     True\n",
              "23810     True\n",
              "23811    False\n",
              "23812     True\n",
              "23813    False\n",
              "Name: n_moa, Length: 23814, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Blc5E3f1Juy5"
      },
      "source": [
        "Com isso, vamos criar nossa segunda coluna:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "hVzhBxk2JxTr",
        "outputId": "d82c7c92-5ec8-4fb7-a2b2-2158b993cb0e"
      },
      "source": [
        "dfr['ativo_moa'] = (dfr['n_moa'] != 0)\n",
        "dfr.head()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
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              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
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              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
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              "      <th>aromatase_inhibitor</th>\n",
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              "      <th>atp_synthase_inhibitor</th>\n",
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              "      <th>atr_kinase_inhibitor</th>\n",
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              "      <th>...</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
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              "      <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",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <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",
              "      <th>n_moa</th>\n",
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              "</table>\n",
              "<p>5 rows × 209 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  5-alpha_reductase_inhibitor  ...  n_moa  ativo_moa\n",
              "0  id_000644bb2                            0  ...      1       True\n",
              "1  id_000779bfc                            0  ...      0      False\n",
              "2  id_000a6266a                            0  ...      3       True\n",
              "3  id_0015fd391                            0  ...      0      False\n",
              "4  id_001626bd3                            0  ...      1       True\n",
              "\n",
              "[5 rows x 209 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5h18hxhKKGmY"
      },
      "source": [
        "Agora que temos as informações que queríamos, vamos combinar os dados usando o método `merge` do pandas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "IdYo_R3vKPyH",
        "outputId": "ae9b68bf-8469-45a7-c91f-05483c053ebb"
      },
      "source": [
        "combinado = pd.merge(df, dfr[['id','n_moa', 'ativo_moa']], on='id')\n",
        "combinado.head()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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>0.3905</td>\n",
              "      <td>0.7099</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>False</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>-0.0444</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",
              "      <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>3</td>\n",
              "      <td>True</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>-2.3820</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",
              "      <td>0</td>\n",
              "      <td>False</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>0.1535</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",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-98    c-99  n_moa  ativo_moa\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.3801  0.4176      1       True\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.6077  0.7371      0      False\n",
              "2  id_000a6266a  com_droga     48   D1  ... -1.4080  0.6931      3       True\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.3876 -0.8154      0      False\n",
              "4  id_001626bd3  com_droga     72   D2  ... -0.3786  0.7125      1       True\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wx3QZXIELFqf",
        "outputId": "e5aec987-0e42-4249-d176-b233d3f1e16f"
      },
      "source": [
        "combinado[combinado.tratamento == 'com_controle']['tratamento'].value_counts()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "com_controle    1866\n",
              "Name: tratamento, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psiQPPMTKo0e",
        "outputId": "6ac254be-5083-4f20-cc7f-e6c817110a80"
      },
      "source": [
        "combinado[combinado.tratamento == 'com_controle']['ativo_moa'].value_counts()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    1866\n",
              "Name: ativo_moa, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoQdk9fRLjqW"
      },
      "source": [
        "Como obtivemos todas as respostas com o 'false', não tivemos nenhuma resposta de algum mecanismo de ação no tratamento com controle."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "870CXAbNL2OQ",
        "outputId": "220fbf0e-c867-41b2-bc19-8bdd13d0494c"
      },
      "source": [
        "combinado[combinado.tratamento == 'com_droga']['tratamento'].value_counts()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "com_droga    21948\n",
              "Name: tratamento, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uICkZvbeLsqe",
        "outputId": "7a808571-f2f6-4506-e268-6d5e7d8b3749"
      },
      "source": [
        "combinado[combinado.tratamento == 'com_droga']['ativo_moa'].value_counts()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     14447\n",
              "False     7501\n",
              "Name: ativo_moa, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKDYUoZAMKJm",
        "outputId": "d5f5230c-05e0-4989-8f98-809488a0c9f6"
      },
      "source": [
        "combinado[combinado.tratamento == 'com_droga']['ativo_moa'].value_counts(normalize = True)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     0.658238\n",
              "False    0.341762\n",
              "Name: ativo_moa, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P37H3z5JMPdd"
      },
      "source": [
        "Como podemos ver 65.8% (14447) dos resultados tiveram ativação e 34.2% (7501) não tiveram ativação no tratamento com drogas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5EljBuxNYuH"
      },
      "source": [
        "Vamos fazer uma análise utilizando agora o boxplot, que gera uma imagem onde podemos identificar a mediana (linha central) e os quartis, entendendo assim como está a distribuição dos dados; dessa maneira vamos identificar, de forma visual, a comparação de categoria dos dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "4o5SXkLtMq_v",
        "outputId": "a075042d-d860-4c4f-eb7f-0b2272d70520"
      },
      "source": [
        "comp = combinado['composto'].value_counts().index[:5]\n",
        "f = sns.boxplot(data = combinado.query('composto in @comp'), y= 'g-0', x='composto', hue='ativo_moa', palette= 'Reds')\n",
        "f.set_title('Ativação dos 5 compostos principais', fontsize = 20)\n",
        "f.set_xlabel('Composto', fontsize = 15)\n",
        "f.set_ylabel('g-0', fontsize = 15)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'g-0')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-x4hnCfN7VV"
      },
      "source": [
        "Analisando os boxplots, percebemos que temos algumas categorias com a mediana próxima de 0; temos outra com a mediana próxima de 5 e a outra próxima de 2.\n",
        "\n",
        "É curioso observarmos também que o 3º boxplot, da esquerda para a direita, está bem concentrado; esse composto específico é na verdade o composto que nos indica que foi \"com tratamento\".\n",
        "\n",
        "Outro ponto interessante é que o último boxplot tem um comportamento muito semelhante ao 3º; provavelmente o composto não modificou tanto assim o gene 0."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8Pi0P7cSKBN"
      },
      "source": [
        "#Desafio 01: Encontrar o top 10, das ações do MOA (inibidor, agonista...)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vI8RvwszSPca",
        "outputId": "8e27f46c-7d69-4cc6-f1b7-4d3e7a8052df"
      },
      "source": [
        "indices = [i.split('_')[-1] for i in atv.index ]\n",
        "l = []\n",
        "for x in indices:\n",
        "      if x not in l:\n",
        "        l.append(x)\n",
        "l"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['inhibitor',\n",
              " 'antagonist',\n",
              " 'blocker',\n",
              " 'agonist',\n",
              " 'local',\n",
              " 'anti-inflammatory',\n",
              " 'antioxidant',\n",
              " 'immunosuppressant',\n",
              " 'medium',\n",
              " 'activator',\n",
              " 'agent',\n",
              " 'sensitizer',\n",
              " 'stimulant',\n",
              " 'antibiotic',\n",
              " 'antifolate',\n",
              " 'antiprotozoal',\n",
              " 'secretagogue',\n",
              " 'donor',\n",
              " 'b',\n",
              " 'antiviral',\n",
              " 'antimalarial',\n",
              " 'scavenger',\n",
              " 'antifungal',\n",
              " 'analgesic',\n",
              " 'antihistamine',\n",
              " 'anticonvulsant',\n",
              " 'laxative',\n",
              " 'steroid',\n",
              " 'antiarrhythmic',\n",
              " 'diuretic']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YYQdqsD8VKtC",
        "outputId": "3f7edfa9-a485-4165-b2d2-0baa9f51cd17"
      },
      "source": [
        "novo_atv = pd.DataFrame(data={'ação':indices,'contagem':atv.values})\n",
        "novo_atv.head()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ação</th>\n",
              "      <th>contagem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>inhibitor</td>\n",
              "      <td>832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>inhibitor</td>\n",
              "      <td>726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>inhibitor</td>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>antagonist</td>\n",
              "      <td>424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>antagonist</td>\n",
              "      <td>404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ação  contagem\n",
              "0   inhibitor       832\n",
              "1   inhibitor       726\n",
              "2   inhibitor       435\n",
              "3  antagonist       424\n",
              "4  antagonist       404"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "wqJtU5J6WNnB",
        "outputId": "2d782e11-c684-4ec6-b335-f44e203ec6fd"
      },
      "source": [
        "a = novo_atv.groupby(by=['ação']).sum()['contagem'].sort_values(ascending=False)[:10]\n",
        "plt.figure(figsize=(14,10))\n",
        "p = sns.barplot( x = a.values, y = a.index)\n",
        "plt.tight_layout()\n",
        "plt.title('Top 10 ações MOA', fontsize = 20)\n",
        "plt.ylabel('Ação', fontsize = 15)\n",
        "plt.yticks(fontsize = 15)\n",
        "for bar in p.patches:\n",
        "    p.text(bar.get_width() + 1,\n",
        "    bar.get_y() + bar.get_height() / 2,\n",
        "    '{:1.0f}'.format(bar.get_width()),\n",
        "    ha = 'left', va = 'center', size = 15)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhC-y3-mG4HD",
        "outputId": "71df12e6-b4e7-40f3-cfa9-85057dd0a436",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "a.index"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['inhibitor', 'antagonist', 'agonist', 'blocker', 'agent', 'activator',\n",
              "       'local', 'immunosuppressant', 'anti-inflammatory', 'antioxidant'],\n",
              "      dtype='object', name='ação')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdrdzRRzODi1"
      },
      "source": [
        "#Desafio 02: Criar a coluna eh_controle para quando na linha tratamento == com_controle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvAa-mmVQYOi",
        "outputId": "bb727876-96aa-43f4-b443-1de32d2851a5"
      },
      "source": [
        "combinado['tratamento'].value_counts()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "com_droga       21948\n",
              "com_controle     1866\n",
              "Name: tratamento, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xewdWpysPS-E",
        "outputId": "61a3358f-458a-4af3-8bae-3863bfc69507"
      },
      "source": [
        "combinado['eh_controle'] = combinado['tratamento'] == 'com_controle'\n",
        "combinado['eh_controle'].value_counts()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    21948\n",
              "True      1866\n",
              "Name: eh_controle, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZo54eehQPPU"
      },
      "source": [
        "Assim como esperavamos, temos 21948 valores `False` e 1866 `True` na coluna `eh_controle`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "SEt-vFG5uBTc",
        "outputId": "fbefbc01-bd7f-42e7-cfc6-89f2e54e3569"
      },
      "source": [
        "combinado.head()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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-63</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>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>eh_controle</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>0.8427</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>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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>0.7099</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>False</td>\n",
              "      <td>False</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>0.1894</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>3</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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>-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",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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>-0.4640</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",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 880 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-99  n_moa  ativo_moa  eh_controle\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.4176      1       True        False\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.7371      0      False        False\n",
              "2  id_000a6266a  com_droga     48   D1  ...  0.6931      3       True        False\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.8154      0      False        False\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.7125      1       True        False\n",
              "\n",
              "[5 rows x 880 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNm24Sa0Qwwj"
      },
      "source": [
        "#Desafio 03: Criar três colunas para indicar o tempo 24, 48, 72"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aevL8pceQx62",
        "outputId": "8bd8a0bb-6355-400b-f938-ccd41ae21bde"
      },
      "source": [
        "combinado['tempo'].value_counts()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48    8250\n",
              "72    7792\n",
              "24    7772\n",
              "Name: tempo, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-upBDaNGRH9D",
        "outputId": "7a566a31-7db9-4e72-9577-94d4710006be"
      },
      "source": [
        "t_24 = combinado['tempo'] == 24\n",
        "t_48 = combinado['tempo'] == 48\n",
        "t_72 = combinado['tempo'] == 72\n",
        "\n",
        "combinado['t_24'],combinado['t_48'],combinado['t_72'] = [t_24,t_48,t_72]\n",
        "combinado[['t_24', 't_48', 't_72']].value_counts()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "t_24   t_48   t_72 \n",
              "False  True   False    8250\n",
              "       False  True     7792\n",
              "True   False  False    7772\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MCvq6t99RY-L",
        "outputId": "556492ac-acdb-4e28-94be-b559da310b43"
      },
      "source": [
        "combinado.head()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\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>composto</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
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              "      <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",
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              "      <th>g-22</th>\n",
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              "      <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-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>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>eh_controle</th>\n",
              "      <th>t_24</th>\n",
              "      <th>t_48</th>\n",
              "      <th>t_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",
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              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
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              "      <td>-0.6528</td>\n",
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              "      <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",
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              "      <td>0.5819</td>\n",
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              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
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              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
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              "      <td>0.5810</td>\n",
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              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
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              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
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              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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",
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              "      <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>-0.2840</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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</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>-0.4682</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>3</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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>-0.2491</td>\n",
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              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
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              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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>0.1500</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>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 883 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ... eh_controle   t_24   t_48   t_72\n",
              "0  id_000644bb2  com_droga     24   D1  ...       False   True  False  False\n",
              "1  id_000779bfc  com_droga     72   D1  ...       False  False  False   True\n",
              "2  id_000a6266a  com_droga     48   D1  ...       False  False   True  False\n",
              "3  id_0015fd391  com_droga     48   D1  ...       False  False   True  False\n",
              "4  id_001626bd3  com_droga     72   D2  ...       False  False  False   True\n",
              "\n",
              "[5 rows x 883 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8cDd4gdueP1"
      },
      "source": [
        "#Desafio 04: Estudar sobre combinações de DF https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77W6YKVmzxWs"
      },
      "source": [
        "\n",
        "\n",
        "Para fazer uma concatenação com um DF temos diversas opções, tanto horizontalmente e verticalmente, temos a função pd.merge() com diversos parâmetros para principalmente concatenação horizontal, ela também pode ser usada diretamente no dataframe sem criar um novo objeto, para o mesmo fim temos a pd.df.join() que apresenta menos flexibilidade.\n",
        "\n",
        "Além disso pandas oferece o pd.concat, que serve tanto para horizaltal e vertical, e para uma concatenação rápida verticalmente oferece o pd.df.append().\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8m-DRtX35DI"
      },
      "source": [
        "#Desafio 05: Fazer análise mais detalhada considerando tempo e dose, para comparar as distribuições (Escolher uma droga e comparar com controle)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "L7yWT2Jx3_u8",
        "outputId": "9b96e036-9f11-4fd5-af54-226286c2fa6c"
      },
      "source": [
        "combinado.groupby(['dose', 'tempo']).agg({'n_moa':'sum', 'ativo_moa':'sum'})"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>2754</td>\n",
              "      <td>2364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>3072</td>\n",
              "      <td>2632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2775</td>\n",
              "      <td>2376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>2741</td>\n",
              "      <td>2354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2757</td>\n",
              "      <td>2365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2745</td>\n",
              "      <td>2356</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            n_moa  ativo_moa\n",
              "dose tempo                  \n",
              "D1   24      2754       2364\n",
              "     48      3072       2632\n",
              "     72      2775       2376\n",
              "D2   24      2741       2354\n",
              "     48      2757       2365\n",
              "     72      2745       2356"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "W3auMLp85wbt",
        "outputId": "1ca4bf9d-fc9f-444d-b42a-080fddd22c9b"
      },
      "source": [
        "c = sns.countplot(x = combinado['n_moa'], palette='rainbow')\n",
        "\n",
        "plt.title('Contagem de experimentos por ativações MOA', fontsize = 20)\n",
        "plt.xlabel('Ativações', fontsize = 15)\n",
        "plt.xticks(color = 'tab:blue')\n",
        "plt.ylabel('Experimentos', fontsize = 15)\n",
        "for bar in c.patches:\n",
        "    c.annotate(format(bar.get_height()), \n",
        "                   (bar.get_x() + bar.get_width() / 2, \n",
        "                    bar.get_height()), ha='center', va='center',\n",
        "                   size=15, xytext=(0, 8), color = 'red',\n",
        "                   textcoords='offset points')"
      ],
      "execution_count": 98,
      "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": "markdown",
      "metadata": {
        "id": "_cNZK0Sd737F"
      },
      "source": [
        "#Desafio 06: Descobrir se tem algum composto que dependendo da configuração do experimento, ativa ou não ativa algum MOA."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dzDZU1z-787l",
        "outputId": "3851e7c2-c7ab-417e-db45-ba8ef08b810b"
      },
      "source": [
        "d_comp = combinado['composto'].value_counts() == 1\n",
        "d_comp = d_comp.reset_index()\n",
        "d_comp"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <tbody>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>3289 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          index  composto\n",
              "0     cacb2b860     False\n",
              "1     87d714366     False\n",
              "2     9f80f3f77     False\n",
              "3     8b87a7a83     False\n",
              "4     5628cb3ee     False\n",
              "...         ...       ...\n",
              "3284  d778ccd51      True\n",
              "3285  122c63321      True\n",
              "3286  0e6ee26ff      True\n",
              "3287  6e2b418c3      True\n",
              "3288  125db1da8      True\n",
              "\n",
              "[3289 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ctTSFir88ckf",
        "outputId": "22cc1fc7-53a1-45e3-ced9-289a7cdc475a"
      },
      "source": [
        "d_comp = d_comp.loc[d_comp['composto'] == True,'index']\n",
        "filtrado = combinado.drop(index=combinado[combinado['composto'].isin(d_comp.to_list())].index)\n",
        "filtrado"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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>0.1500</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>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</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",
              "      <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",
              "      <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",
              "      <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>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</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",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.7390</td>\n",
              "      <td>-1.3270</td>\n",
              "      <td>0.9925</td>\n",
              "      <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>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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>0.4934</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>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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>0.4220</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>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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>-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",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23685 rows × 883 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo dose  ... eh_controle   t_24   t_48   t_72\n",
              "0      id_000644bb2     com_droga     24   D1  ...       False   True  False  False\n",
              "1      id_000779bfc     com_droga     72   D1  ...       False  False  False   True\n",
              "2      id_000a6266a     com_droga     48   D1  ...       False  False   True  False\n",
              "3      id_0015fd391     com_droga     48   D1  ...       False  False   True  False\n",
              "4      id_001626bd3     com_droga     72   D2  ...       False  False  False   True\n",
              "...             ...           ...    ...  ...  ...         ...    ...    ...    ...\n",
              "23809  id_fffb1ceed     com_droga     24   D2  ...       False   True  False  False\n",
              "23810  id_fffb70c0c     com_droga     24   D2  ...       False   True  False  False\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...        True  False   True  False\n",
              "23812  id_fffcb9e7c     com_droga     24   D1  ...       False   True  False  False\n",
              "23813  id_ffffdd77b     com_droga     72   D1  ...       False  False  False   True\n",
              "\n",
              "[23685 rows x 883 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oEh4SygJ9luA",
        "outputId": "14319573-f2df-40d4-d362-0c7b519c1227"
      },
      "source": [
        "filtrado.drop_duplicates(['composto', 'ativo_moa'], keep=False)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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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",
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              "      <th>g-9</th>\n",
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              "      <th>g-16</th>\n",
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              "      <th>g-20</th>\n",
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              "      <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-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>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>eh_controle</th>\n",
              "      <th>t_24</th>\n",
              "      <th>t_48</th>\n",
              "      <th>t_72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 883 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, tratamento, tempo, dose, composto, g-0, g-1, g-2, g-3, g-4, g-5, g-6, g-7, g-8, g-9, g-10, g-11, g-12, g-13, g-14, g-15, g-16, g-17, g-18, g-19, g-20, g-21, g-22, g-23, g-24, g-25, g-26, g-27, g-28, g-29, g-30, g-31, g-32, g-33, g-34, g-35, g-36, g-37, g-38, g-39, g-40, g-41, g-42, g-43, g-44, g-45, g-46, g-47, g-48, g-49, g-50, g-51, g-52, g-53, g-54, g-55, g-56, g-57, g-58, g-59, g-60, g-61, g-62, g-63, g-64, g-65, g-66, g-67, g-68, g-69, g-70, g-71, g-72, g-73, g-74, g-75, g-76, g-77, g-78, g-79, g-80, g-81, g-82, g-83, g-84, g-85, g-86, g-87, g-88, g-89, g-90, g-91, g-92, g-93, g-94, ...]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 883 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCfp-tcj9srn"
      },
      "source": [
        "Observamos que eliminando primeiramente todas as drogas que participaram de apenas um experimento e após isso todos os experimentos que tiveram o mesmo composto e que a ativação desse composto não mudou percebemos que não achamos nenhum composto que dependendo da configuração do experimento, ativa ou não ativa algum MOA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLrA3mrK92FT"
      },
      "source": [
        "#Desafio 07: Descobrir se tem algum composto que dependendo da configuração do experimento, ativa MOAs diferentes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3w3jKOLa96Sw",
        "outputId": "65ca287e-5c5b-47a4-8a4c-98c232ea2982"
      },
      "source": [
        "data = pd.merge(dfr, filtrado[['id', 'composto']], on= 'id')\n",
        "data.drop(columns='n_moa', inplace=True)\n",
        "data"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<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>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <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>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",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <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",
              "      <th>ativo_moa</th>\n",
              "      <th>composto</th>\n",
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              "  <tbody>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>True</td>\n",
              "      <td>b68db1d53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23685 rows × 209 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  5-alpha_reductase_inhibitor  ...  ativo_moa   composto\n",
              "0      id_000644bb2                            0  ...       True  b68db1d53\n",
              "1      id_000779bfc                            0  ...      False  df89a8e5a\n",
              "2      id_000a6266a                            0  ...       True  18bb41b2c\n",
              "3      id_0015fd391                            0  ...      False  8c7f86626\n",
              "4      id_001626bd3                            0  ...       True  7cbed3131\n",
              "...             ...                          ...  ...        ...        ...\n",
              "23680  id_fffb1ceed                            0  ...       True  df1d0a5a1\n",
              "23681  id_fffb70c0c                            0  ...       True  ecf3b6b74\n",
              "23682  id_fffc1c3f4                            0  ...      False  cacb2b860\n",
              "23683  id_fffcb9e7c                            0  ...       True  8b87a7a83\n",
              "23684  id_ffffdd77b                            0  ...      False  972f41291\n",
              "\n",
              "[23685 rows x 209 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RtoFSUVL-GOX",
        "outputId": "60af4e26-52db-4bcf-9090-f8e4950692a4"
      },
      "source": [
        "data = data.drop_duplicates(data.columns[1:], keep=False)\n",
        "data"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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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>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <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>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",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <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",
              "      <th>ativo_moa</th>\n",
              "      <th>composto</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "  </tbody>\n",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, 5-alpha_reductase_inhibitor, 11-beta-hsd1_inhibitor, acat_inhibitor, acetylcholine_receptor_agonist, acetylcholine_receptor_antagonist, acetylcholinesterase_inhibitor, adenosine_receptor_agonist, adenosine_receptor_antagonist, adenylyl_cyclase_activator, adrenergic_receptor_agonist, adrenergic_receptor_antagonist, akt_inhibitor, aldehyde_dehydrogenase_inhibitor, alk_inhibitor, ampk_activator, analgesic, androgen_receptor_agonist, androgen_receptor_antagonist, anesthetic_-_local, angiogenesis_inhibitor, angiotensin_receptor_antagonist, anti-inflammatory, antiarrhythmic, antibiotic, anticonvulsant, antifungal, antihistamine, antimalarial, antioxidant, antiprotozoal, antiviral, apoptosis_stimulant, aromatase_inhibitor, atm_kinase_inhibitor, atp-sensitive_potassium_channel_antagonist, atp_synthase_inhibitor, atpase_inhibitor, atr_kinase_inhibitor, aurora_kinase_inhibitor, autotaxin_inhibitor, bacterial_30s_ribosomal_subunit_inhibitor, bacterial_50s_ribosomal_subunit_inhibitor, bacterial_antifolate, bacterial_cell_wall_synthesis_inhibitor, bacterial_dna_gyrase_inhibitor, bacterial_dna_inhibitor, bacterial_membrane_integrity_inhibitor, bcl_inhibitor, bcr-abl_inhibitor, benzodiazepine_receptor_agonist, beta_amyloid_inhibitor, bromodomain_inhibitor, btk_inhibitor, calcineurin_inhibitor, calcium_channel_blocker, cannabinoid_receptor_agonist, cannabinoid_receptor_antagonist, carbonic_anhydrase_inhibitor, casein_kinase_inhibitor, caspase_activator, catechol_o_methyltransferase_inhibitor, cc_chemokine_receptor_antagonist, cck_receptor_antagonist, cdk_inhibitor, chelating_agent, chk_inhibitor, chloride_channel_blocker, cholesterol_inhibitor, cholinergic_receptor_antagonist, coagulation_factor_inhibitor, corticosteroid_agonist, cyclooxygenase_inhibitor, cytochrome_p450_inhibitor, dihydrofolate_reductase_inhibitor, dipeptidyl_peptidase_inhibitor, diuretic, dna_alkylating_agent, dna_inhibitor, dopamine_receptor_agonist, dopamine_receptor_antagonist, egfr_inhibitor, elastase_inhibitor, erbb2_inhibitor, estrogen_receptor_agonist, estrogen_receptor_antagonist, faah_inhibitor, farnesyltransferase_inhibitor, fatty_acid_receptor_agonist, fgfr_inhibitor, flt3_inhibitor, focal_adhesion_kinase_inhibitor, free_radical_scavenger, fungal_squalene_epoxidase_inhibitor, gaba_receptor_agonist, gaba_receptor_antagonist, gamma_secretase_inhibitor, glucocorticoid_receptor_agonist, glutamate_inhibitor, glutamate_receptor_agonist, ...]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 209 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqtedgrC-JAD",
        "outputId": "63b03e82-8cab-4cba-8fa3-89dc81bfbf22"
      },
      "source": [
        "data['composto'].unique()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHm8sQEo-YW0"
      },
      "source": [
        "Eliminando os compostos que só foram utilizadas em um experimento e depois eliminando os experimento que tinham todas as colunas(mesmos composto e mesmos MOAs ativados) não foi achado nenhum composto que dependendo da configuração do experimento, ativa MOAs diferentes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHzyG0Jy-Zh0"
      },
      "source": [
        "#Desafio 08: Resumo do que você aprendeu com os dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ho5NK7n-dWN"
      },
      "source": [
        "Nessa aula vimos a importância de concatenar dados de dois arquivos diferentes e como isso pode ser poderoso e ajudar a retirar informações concretas dos dados, vimos a real ação dos compostos a partir dos dados do resultado e que a maioria dos compostos possui uma ação inibidora.\n",
        "\n",
        "Vimos como criar novas colunas e como isso pode ser útil na hora de filtrar as informações.\n",
        "\n",
        "Aprendemos a função de um experimento controle, que não pode ativar nenhum mecanismo para ser possível comparar com os demais experimentos.\n",
        "\n",
        "Pelas análises até então feitas, podemos concluir que um determinado compostos sempre irá ativar a mesma quantidade de mecanismos e os mesmos mecanismos independente da dosagem e do tempo de análise do estudo.\n"
      ]
    }
  ]
}