Aula4AnaliseDadosFarmaceuticos.ipynb

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  "metadata": {
    "colab": {
      "name": "Aula4AnaliseDadosFarmaceuticos.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNG5Ktcq3iS/etkA3uW2qbW",
      "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/Gabrielvinicius27/DadosFarmaceuticosNotebooks/blob/master/Aula4AnaliseDadosFarmaceuticos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "Lb9ZTEknYikX",
        "outputId": "6fa4bec7-1244-415f-98aa-f3425196a6b6"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "dados_experimentos = pd.read_csv(url_dados, compression = 'zip')\n",
        "dados_experimentos.columns = dados_experimentos.columns.str.replace('-','')\n",
        "dados_experimentos.rename(columns={'droga':'composto'}, inplace = True)\n",
        "dados_experimentos.head()"
      ],
      "execution_count": 1,
      "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>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
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              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
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              "      <th>c89</th>\n",
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              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
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              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</th>\n",
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              "  </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",
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              "      <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  ...     c96     c97     c98     c99\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": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "QS9a8lsrY4ge",
        "outputId": "97f37c06-6137-4d58-c2e7-3e35a88f50c7"
      },
      "source": [
        "url_dados = 'https://github.com/alura-cursos/imersao-dados-desafio-final/blob/main/Dados/dados_resultados.csv?raw=true'\n",
        "dados_resultados = pd.read_csv(url_dados)\n",
        "dados_resultados.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "    </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",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "JbRlDReXaX0n",
        "outputId": "752b0871-0316-4cad-a9c6-0e680ee46c1c"
      },
      "source": [
        "dados_resultados.describe().T"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.000714</td>\n",
              "      <td>0.026709</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.027483</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.001008</td>\n",
              "      <td>0.031731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.007979</td>\n",
              "      <td>0.088967</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acetylcholine_receptor_antagonist</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.012640</td>\n",
              "      <td>0.111716</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.000252</td>\n",
              "      <td>0.015871</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vegfr_inhibitor</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.007139</td>\n",
              "      <td>0.084190</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitamin_b</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.001092</td>\n",
              "      <td>0.033025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitamin_d_receptor_agonist</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.001638</td>\n",
              "      <td>0.040436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnt_inhibitor</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.001260</td>\n",
              "      <td>0.035472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>206 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         count      mean  ...  75%  max\n",
              "5-alpha_reductase_inhibitor            23814.0  0.000714  ...  0.0  1.0\n",
              "11-beta-hsd1_inhibitor                 23814.0  0.000756  ...  0.0  1.0\n",
              "acat_inhibitor                         23814.0  0.001008  ...  0.0  1.0\n",
              "acetylcholine_receptor_agonist         23814.0  0.007979  ...  0.0  1.0\n",
              "acetylcholine_receptor_antagonist      23814.0  0.012640  ...  0.0  1.0\n",
              "...                                        ...       ...  ...  ...  ...\n",
              "ubiquitin_specific_protease_inhibitor  23814.0  0.000252  ...  0.0  1.0\n",
              "vegfr_inhibitor                        23814.0  0.007139  ...  0.0  1.0\n",
              "vitamin_b                              23814.0  0.001092  ...  0.0  1.0\n",
              "vitamin_d_receptor_agonist             23814.0  0.001638  ...  0.0  1.0\n",
              "wnt_inhibitor                          23814.0  0.001260  ...  0.0  1.0\n",
              "\n",
              "[206 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CFa3ANsJfFvw",
        "outputId": "ecbe33b3-4cfa-48f0-9f54-6ff7a4e224b2"
      },
      "source": [
        "dados_resultados['acat_inhibitor'].unique()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inqtik2sf7Jy",
        "outputId": "5c3b6fe5-4b82-48eb-d9a4-cb8534a6c94c"
      },
      "source": [
        "contagem_moa = dados_resultados.select_dtypes('int64').sum().sort_values(ascending = False)\n",
        "contagem_moa"
      ],
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "iLujbiSleUV4",
        "outputId": "707fbf32-20c7-4ea2-b383-6667f319bdc6"
      },
      "source": [
        "dados_resultados['n_moa'] = dados_resultados.drop('id', axis = 1).sum(axis = 1)\n",
        "dados_resultados['ativo_moa'] = (dados_resultados['n_moa'] != 0)\n",
        "dados_resultados.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
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              "      <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",
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              "      <th>aldehyde_dehydrogenase_inhibitor</th>\n",
              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
              "      <th>angiogenesis_inhibitor</th>\n",
              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>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>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "    </tr>\n",
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              "      <td>0</td>\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9-Vrde_gD5b"
      },
      "source": [
        "dados_combinados = pd.merge(dados_experimentos, dados_resultados[['id','n_moa', 'ativo_moa']], \n",
        "                            on = 'id')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "SsJx0rM4hIPr",
        "outputId": "49789058-68f4-49a0-cb9e-d94ebb532c49"
      },
      "source": [
        "dados_combinados.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
              "    </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>0.3905</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  ...     c98     c99  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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m05veM6Iia8W",
        "outputId": "e74bd474-5d35-44be-e1c7-66b9f18c2a9f"
      },
      "source": [
        "dados_combinados.query('tratamento == \"com_controle\"')['ativo_moa'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    1866\n",
              "Name: ativo_moa, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enHKWMLPvE_E",
        "outputId": "3bf68229-af2c-4d37-b77c-9d350d404288"
      },
      "source": [
        "dados_combinados.query('tratamento == \"com_droga\"')['ativo_moa'].value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     14447\n",
              "False     7501\n",
              "Name: ativo_moa, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "KpwhEPuZvVD4",
        "outputId": "37b03fee-39a9-4b58-a1ba-28dcd47319ce"
      },
      "source": [
        "compostos_principais = dados_combinados['composto'].value_counts().index[0:5]\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.boxplot(data = dados_combinados.query('composto in @compostos_principais'), \n",
        "            y='g0', x='composto', hue = 'ativo_moa')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f90e34f11d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AADWn+AEAANSc4gcAAFBzih8AAEDNKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDNKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDNKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAwLQaGtqWZcuuzPDwUNVRYMZS/AAAmFarVq3IunVlVq68seooMGMpfgAATJuhoW0ZHFydVquVwcGbjPpBRRQ/AACmzapVK9JstpIkzWbTqB9URPEDAGDarF27JmNjo0mSsbHRrF27puJEMDMpfgAATJvFi/vT3d2TJOnu7snixf0VJ4KZSfEDAGDaLF16Qbq6GkmSrq6unH/+hRUngplJ8QMAYNr09c3NwMA5aTQaGRhYkt7evqojwYzUU3UAAADqbenSC7Jp011G+6BCih8AANOqr29urrjivVXHgBmt0Wq1qs4wFRYluWPr1pFdlwsG6um66z6eO+/cUNn+N24c3/fJJ59SWYYkOemkU3LxxZdWmgEAaB9dXY3MmzcnSU5Nsn7P+Ub8gI5y550bsv7272XhnGqevo5JM0ny6ObbK9l/kmweGa1s3wBAZ1L8gI6zcE5PXnfm8VXHqMw1t91fdQQAoMO4qicAAEDNKX4AAAA1p/gBAADUnOIHAABQc211cZeiKI5O8kdJXprk0SRry7L81WpTAQAAdLa2Kn5J3pfxwnd6WZatoih+tOpAAAAAna5til9RFHOSXJrkxLIsW0lSluU91aYCAADofG1T/JI8PcnWJL9dFMXPJBlJ8u6yLAcPdAMTd6oHamzWrO48WnWINjBrVncWLDiu6hgAQIdop+LXneRpSf6lLMt3FEXxgiSriqJ4RlmWDxzIBrZuHUmz2ZrWkEC1duwYqzpCW9ixYyxbtjxYdQxghliz5qYMDq4+5PWHh4eSJL29fYeVY2DgnPT3LzmsbUBddXU1Jh0Ia6erem5MMprk+iQpy/LrSe5LcnqVoQAAODzDw8MZHh6uOgbMaG0z4leW5X1FUXwlyblJ/qkoitOTnJDk9mqTAQDMbP39Sw5rpG358quSJJdf/p6pigQcpLYpfhN+LclfF0Xxh0l2JLmkLMuhijMBAAB0tLYqfmVZ/meSF1WdAwAAoE7a6Tt+AAAATAPFDwAAoOYUPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDmFD8AAICaU/wAAABqTvEDAACoOcUPAACg5hQ/AACAmlP8AAAAak7xAwAAqDnFDwAAoOYUPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDmFD8AAICaU/wAAABqTvEDAACoOcUPAACg5hQ/AACAmlP8AAAAak7xAwAAqDnFDwAAoOYUPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDmFD8AAICaU/wAAABqTvEDAACouZ6qAwAcjOHhoWwbGc01t91fdZTKbB4ZzdzhoapjAAAdxIgfAABAzRnxAzpKb29fZj9yX1535vFVR6nMNbfdn6N7+6qO0TaGhrblIx/5k7zpTW9Nr98LAOyV4se0WLPmpgwOrj7k9YcnTmM7nDdxAwPnpL9/ySGvD3SGVatWZN26MitX3phLLnl91XEAoC051ZO2NDw8nOHh4apjAG1uaGhbBgdXp9VqZXDwpl0fGgEAj2fEj2nR37/ksEbbli+/Kkly+eXvmapIQA2tWrUizWYrSdJsNo36AcA+GPEDoGOtXbsmY2OjSZKxsdGsXbum4kQA0J4UPwA61uLF/enuHj95pbu7J4sX91ecCADak+IHQMdauvSCdHU1kiRdXV05//wLK04EAO1J8QOgY/X1zc3AwDlpNBoZGFjidg4AsA8u7gJAR1u69IJs2nSX0T4AmITiB0BH6+ubmyuueG/VMQCgrTnVEwAAoOYUPwA62tDQtixbdqWbtwPAJBQ/ADraqlUrsm5dmZUrb6w6CgC0LcUPgI41NLQtg4Or02q1Mjh4k1E/ANgHxQ+AjrVq1Yo0m60kSbPZNOoHAPug+AHQsdauXZOxsdEkydjYaNauXVNxIgBoT4ofAB1r8eL+dHeP35mou7snixf3V5wIANqT4gdAx1q69IJ0dTWSJF1dXW7iDgD7oPgB0LH6+uZmYOCcNBqNDAwsSW9vX9WRAKAt9VQdAAAOx9KlF2TTpruM9gHAJBQ/ADpaX9/cXHHFe6uOAQBtzameAAAANdeWxa8oit8uiqJVFMWzqs4CAADQ6dqu+BVF8ZwkL0yyoeosAAAAddBWxa8oitlJrk7ypqqzAAAA1EVbFb8kVyb5ZFmW66sOAgAAUBdtc1XPoigWJ3lekisOdRvz5s2ZukBUatas7iTJggXHVZyEdjNrVncerTpEG5g1q9u/D6BjeF2H6rVN8UtyTpIfT3JHURRJcmKSLxRF8bqyLP/pQDawdetIms3WNEbkSNmxYyxJsmXLgxUnod3sPDZmuh07xvz7ADqG13WYfl1djUkHwtqm+JVluSzJsp3TRVGsT/Lysiy/U1UmAACAOmi37/gBAAAwxdpmxG9PZVkuqjoDAABAHRjxAwAAqDnFD4CONjS0LcuWXZnh4aGqowBA21L8AOhoq1atyLp1ZVauvLHqKADQthQ/ADrW0NC23Hzz6rRarQwOrjbqBwD7oPgB0LFWrVqRsbHRJMno6KhRPwDYB8UPgI51yy2DabVaSZJWq5VbbhmsOBEAtCfFD4CONW/evEmnAYBxih8AHWvr1q2TTgMA49r2Bu4A+7J5ZDTX3HZ/Jfse2d5Mksw5qrrPzTaPjGZRZXtvL2edNZCvfOVLSVpJGjnrrIGqIwFAW1L8gI5y0kmnVLr/ezduSJLMX1hdjkWp/vfQLpYuvSA33fSVjI2Npbu7O+eff2HVkQCgLSl+QEe5+OJLK93/8uVXJUkuv/w9leZgXF/f3Myff0LuuefuLFiwIL29fVVHAoC25Dt+AHSsoaFtuffee5Ik9957j/v4AcA+KH4AdKwbbvibtFrj37tsNpu54Ya/qTgRALQnxQ+AjvX1r9/yuOmvfW1NRUkAoL0pfgAAADWn+AHQsV7wgrMmnQYAxil+AHSsiy76xTQajSRJo9GVX/iFV1WcCADak+IHQMfq65ub5z73p5Mkz33u893OAYCDNjS0LcuWXVn7K0MrfgB0tKOOOipJMnv27IqTANCJVq1akXXryqxceWPVUaaV4gdAxxoa2pZvfvNrSZJvfONrtf+0FoCpNTS0LYODq9NqtTI4eFOtX0cUPwA61qpVK9JstpKM38ev7p/WAjC1ZtLriOIHQMdau3ZNxsZGkyRjY6NZu9Z9/AA4cDPpdUTxA6BjLV7cn+7uniRJd3dPFi/urzgRAJ1kJr2OKH4AdKylSy9IV9f47Ry6urpy/vkXVpwIgE4yk15HFD8AOlZf39wMDJyTRqORgYElbucAwEGZSa8jPVUHAIDDsXTpBdm06a5af0oLwPSZKa8jih8AHa2vb26uuOK9VccAoEPNlNcRxQ8AoMauu+7jufPODZVm2LhxfP/Ll19VaY6TTjolF198aaUZoCqKHwBAjd1554asv/17WTinurd9x6SZJHl08+2VZdg8MlrZvqEdKH4AADW3cE5PXnfm8VXHqNQ1t91fdQSolKt6AgAAM9bQ0LYsW3ZlhoeHqo4yrRQ/AABgxlq1akXWrSuzcuWNVUeZVoofAAAwIw0Nbcvg4Oq0Wq0MDt5U61E/xQ8AAJiRVq1akWazlSRpNpu1HvVT/AAAgBlp7do1GRsbv+Lr2Nho1q5dU3Gi6aP4AQAAM9Lixf3p7h6/0UF3d08WL+6vONH0UfwAAIAZaenSC9LV1UiSdHV15fzzL6w40fRR/ADoaDPlMtwATL2+vrkZGDgnjUYjAwNL0tvbV3WkaXPAN3AviuL0JD+R5LgkDyb5blmW/zFdwQDgQFx33cfzH//xvVx33cfypjddVnUcADrM0qUXZNOmu2o92pccQPEriuLkJJ9O8uwk308ynORJSZ5eFMW3k/xiWZYbpzUlAOzF0NC2fOtbX0+SfPObX8/FFw/V+tNaAKZeX9/cXHHFe6uOMe0O5FTPa5LcnGR+WZZnlGU5UJblmUlOmHj82mnMBwD7dN11H99j+mMVJQGA9nYgxe8FSd5dluXDuz9YluVDSd47MR8Ajrido307ffObX9/HkgAwsx1I8bszycv3Me+/JnGaJwAAQBs7kIu7vCXJ3xVF8bYk384Pv+P3kxm/2Mt/n754AAAAHK79jviVZfmlJE9P8rEkOzL+3b7RienTyrL88rQmBIB9WLDghEmnAWB/ZsptgQ7oPn5lWW5NcnKSB5L8YOL/JyV5a1EU7yqK4rVFUfzo9MUEgCe69NLXP276ta99Q0VJAOhUq1atyLp1ZVauvLHqKNPqYG7gflqSy5P8TJJnTPz/8iQ/leRNSf6zKIrzpjwhAOzDrbd+63HT/+f/fKOiJAB0oqGhbRkcXJ1Wq5XBwZtqPep3MMWvK+P37Du7LMuLy7I8O8krkoyVZfnCJG9Osmw6QgLA3txyy+Ck0wAwmVWrVqTZbCVJms1mrUf9Dqb4vSzJyj0e+2yS/zLx508medpUhAKAA9HX1zfpNABMZu3aNRkbG02SjI2NZu3aNRUnmj4HU/y+n/FTOnf3axOPJ8n8JA8HAI6QLVvunXQaACazeHF/urvHb3TQ3d2TxYv7K040fQ7kdg47vSHJjUVRXJ5kU5KnJhlLcuHE/CLJe6Y2HgDsW6vVmnQaACazdOkFGRxcnbGxpKurK+eff+H+V+pQB1z8yrK8tSiK05K8MMlTktydZG1Zljsm5t+U5KZpSQkAe9FoNB5X9hqNRoVpAOg0fX1zMzBwTr761S9lYGBJenvr+5WBgxnxy0TJu3masgDAQVmw4ITcc8/mx00DwMFYuvSCbNp0V61H+5KDLH4A0E6GhrZNOg0A+9PXNzdXXPHeqmNMu4O5uAsAtJW+vuMnnQYAxil+AHSse+/dPOk0ADBO8QOgY7mqJwAcGMUPAACg5trm4i5FUcxL8okkT0+yPcm6JG8sy3JLpcEAAIC2tGbNTRkcXH1Y2xgeHkqSw7qVw8DAOenvX3JYOaZbO434tZK8ryzLoizLM5J8P8myijMB0MZmz5496TQA7M/w8HCGh4erjjHt2mbEryzL+5N8dbeHvpbkTdWkAaATPPbYY3tMb68oCQBV6O9fctgjbcuXX5Ukufzy90xFpLbVNsVvd0VRdGW89K08mPXmzZszPYE44mbN6k6SLFhwXMVJ4PEcm1Pry1/+cr74xS9O4RZb+eAHf/+g1zr33HPz4he/eApzQPsYGXkg942M5prb7q86SqU2j4xm/sgDnr95gpny2t6WxS/JnyQZSfKnB7PS1q0jaTZd0a0OduwYS5Js2fJgxUng8RybU+uBBx7Z9TudKoeyvQceeMTfKbU1NtasOkLbGBtr+rfOE9Tltb2rqzHpQFjbFb+iKD6Q5LQkS8uy9EwFUGOHe4rOn/3Zh/Otb3191/TznveCvPnNl01FNKiN3t6+zH7kvrzuzOOrjlKpa267P0cfxsU7oNO108VdUhTF7yd5bpL/VpblY/tbHoCZ7eKLL33c9Ktf/dqKkgBAe2ub4lcUxU8k+a0kT0lyS1EU/1oUxYqKYwHQxvr65ubYY8dPa3ne815wWJfiBoA6a5tTPcuy/G6SRtU5AOgsCxcuzA9+sMloHwBMom1G/ADgUPT0zMrJJy8y2gcAk1D8AAAAak7xAwAAqDnFDwAAoOYUPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDm2uYG7rSP6677eO68c0OlGTZuHN//8uVXVZrjpJNOycUXX1ppBgAAOFyKH09w550bsv7272XhnOoOj2PSTJI8uvn2yjJsHhmtbN8AADCVFD/2auGcnrzuzOOrjlGpa267v+oIAAAwJXzHDwAAoOYUPwAAgJpT/AAAAGpO8SGfC9gAAB21SURBVAMAAKg5xQ8AAKDmFD8AAICaU/wAAABqTvEDAACoOcUPAACg5hQ/AACAmlP8AAAAak7xAwAAqDnFDwAAoOYUPwAAgJrrqToAwJG0Zs1NGRxcfcjrb9y4IUmyfPlVh5VjYOCc9PcvOaxtAAAcKMUP4CD09vZWHQEA4KApfsCM0t+/xEgbADDj+I4fAABAzSl+AAAANaf4AQAA1Jzv+AEA1NzmkdFcc9v9le1/ZHszSTLnqOrGHDaPjGZRZXuH6il+AAA1dtJJp1QdIfdO3Apn/sLqsixKe/wuoCqKHwBAjV188aVVR9h179PLL39PxUlg5vIdPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDmXNwFAACoxHXXfTx33rmh0gwbJ646u/MiRFU46aRTpv1CTIofAABQiTvv3JD1t38vC+dUV0uOyfh9Jh/dfHsl+988MnpE9qP4AQAAlVk4pyevO/P4qmNU5prb7j8i+/EdPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDmXNyFJxgeHsq2kdEj9kXTdrV5ZDRzh4eqjgEAAIdN8QPgkLn/0g8diXswAcChUvx4gt7evsx+5L4ZfVndZPzSukf39lUdA9ranXduSLnu9nQfXd2/leZYd5Lk9jvvqyzD2KPODgCgvSl+AByW7qP7cswpL6k6RqUe3vClqiMAwKRc3AUAAKDmFD8AAICaU/wAAABqTvEDAACoOcUPAACg5hQ/AACAmnM7BwAAoBLDw0PZNjKaa267v+ooldk8Mpq5w9N/P1gjfgAAADXXViN+RVGcnuRjSeYl2Zrk0rIs11WbCgAAmA69vX2Z/ch9ed2Zx1cdpTLX3HZ/ju7tm/b9tNuI30eSXF2W5elJrk7y0YrzAAAAdLy2KX5FUZyQ5DlJrp946PokzymKYkF1qQAAADpf2xS/JCcl2VSW5ViSTPz/BxOPAwAAcIja6jt+h2vevDlVR6iFWbO682jVIdrErFndWbDguKpjQNuaNau76ghtw/MF7NvO5wr/RtiT953jjsRrSDsVvzuTPLUoiu6yLMeKouhO8pSJxw/I1q0jaTZb0xZwptixY6zqCG1jx46xbNnyYNUxoG15vvghzxewbzufK/wbYU9eR8ZNxWtIV1dj0oGwtjnVsyzLe5P8a5JXTTz0qiT/UpbllupSAQAAdL52GvFLkl9L8rGiKN6bZFuSSyvOA8AkhoeHMvboUB7e8KWqo1Rq7NGhDA+320sqAPxQW71KlWX5vSQvqDoHAABAnbRV8QOgs/T29mXLA6M55pSXVB2lUg9v+FJ6j8DNdwHgULXNd/wAAACYHoofAABAzSl+AAAANaf4AQAA1JziBwAAUHOu6gkAAFRm88horrnt/sr2P7K9mSSZc1Q1Y2KbR0az6AjsR/EDAAAqcdJJp1QdIfdu3JAkmb+wmiyLcmR+D4ofAABQiYsvvrTqCFm+/KokyeWXv6fiJNPLd/wAAABqTvEDAACoOcUPAACg5hQ/AACAmlP8AAAAas5VPdmrmX4/leTI3VMFAACmm+LHE7ifyrhFaY/fBbS7sUeH8vCGL1W2/+boo0mSrp6jK8sw9uhQkvmV7R8A9kfx4wncTwU4UO3w4cjGiQ+KTj6pyuI1vy1+FwCwL4ofAIfMB0UA0Blc3AUAAKDmFD8AAICaU/wAAABqTvEDAACoOcUPAACg5hQ/AACAmnM7BwAAJrVmzU0ZHFx9yOvvvN/mztuvHKqBgXPS37/ksLYBM5XiBwDAtOrt7a06Asx4ih8AAJPq719ipA06nO/4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDNKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDN9VQdAAAA4FCsWXNTBgdXH9Y2Nm7ckCRZvvyqQ97GwMA56e9fclg5ppviBwAAzFi9vb1VRzgiFD8AAKAj9fcvafuRtnbhO34AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADXXU3WAJCmK4uokL0nyWJKRJJeVZfmtalMBAADUQ7uM+P1DkjPKsnx2kj9I8umK8wAAANRGW4z4lWX52d0m1yY5sSiKrrIsm1VlAgAAqIt2GfHb3VuSfE7pAwAAmBpHZMSvKIpbk5y8j9k/Wpbl2MRyv5jk4iRLDmU/8+bNObSAtJ1Zs7qTJAsWHFdxEqDdeb4AgP07IsWvLMvn7G+ZoiguSPJ7SV5SluU9h7KfrVtH0my2DmVV2syOHWNJki1bHqw4CdDuPF8AQNLV1Zh0IKwtvuNXFMXLk3wwybllWa6vOA4AAECttEXxS3JNku1JbiiKYudjLynLcmt1kQCYbmvW3JTBwdWHtY2NGzckSZYvv+qQtzEwcE76+w/pWwYA0BHaoviVZbmg6gwAdKbe3t6qIwBA22uL4gfAzNTfv8RIGwAcAe14OwcAAACmkOIHAABQc4ofAB1taGhbli27MsPDQ1VHAYC2pfgB0NFWrVqRdevKrFx5Y9VRAKBtKX4AdKyhoW25+ebVabVaGRxcbdQPAPZB8QOgY61atSJjY6NJktHRUaN+ALAPih8AHeuWWwbTarWSJK1WK7fcMlhxIgBoT4ofAB1r3rx5k04DAOMUPwA61tatWyedBgDGKX4AdKyzzhpI0piYakxMAwB7UvwA6FhLl16Qnp7uJElPT0/OP//CihMBQHtS/ADoWH19c3P22S9Ko9HI2Wefk97evqojAUBb6qk6AAAcjqVLL8imTXcZ7QOASSh+AHS0vr65ueKK91YdAwDaWmPn/Y863KIkd2zdOpJmsxY/T8dbs+amDA6uPuT1N27ckCQ5+eRTDnkbAwPnpL9/ySGvDwAAnaKrq5F58+YkyalJ1u8534gfbam3t7fqCAAAUBtG/AAAADrc/kb8XNUTAACg5hQ/AACAmlP8AAAAak7xAwAAqDnFDwAAoOYUPwAAgJpT/AAAAGpO8QMAAKg5xQ8AAKDmFD8AAICaU/wAAABqTvEDAACoOcUPAACg5hQ/AACAmlP8AAAAak7xA6CjDQ1ty7JlV2Z4eKjqKADQthQ/ADraqlUrsm5dmZUrb6w6CgC0LcUPgI41NLQtN9/81bRardx882qjfgCwD4ofAB1r1aoVGR0dS5KMjo4a9QOAfVD8AOhYt9wymKQ1MdWamAYA9qT4AdCx5s2bN+k0ADBO8QOgY23dunXSaQBgnOIHQMc666yBNBqNJEmj0chZZw1UnAgA2pPiB0DHWrr0gnR39yRJenp6cv75F1acCADak+IHQMfq65ubs88+J41GIwMD56S3t6/qSADQlnqqDgAAh2Pp0guyadNdRvsAYBKNVqu1/6Xa36Ikd2zdOpJmsxY/DwAAwAHr6mpk3rw5SXJqkvVPmH+kAwEAAHBkKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDNtVXxK4riRUVRjBVF8ZaqswAAANRF2xS/oiiOS7I8yT9UnQUAAKBO2qb4Jflgkvcnua/qIAAAAHXSU3WAJCmK4r8k6S3L8oaiKF5+qNuZN2/OFKYCAACohyNS/IqiuDXJyfuanWRZknMPdz9bt46k2Wwd7mYAAAA6SldXY9KBsEarVW1RKopiIMmNSR6eeGh+kseSfLgsyysPcDOLktyh+AEAADPRbsXv1CTr95xf+ameZVkOJjlh53RRFNcm+VZZln9aWSgAAIAaaaeLuwAAADANKh/x21NZlr9UdQYAAIA6MeIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDNKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AHQ0YaGtmXZsiszPDxUdRQAaFuKHwAdbdWqFVm3rszKlTdWHQUA2pbiB0DHGhralsHB1Wm1WhkcvMmoHwDsg+IHQMdatWpFms1WkqTZbBr1A4B9UPwA6Fhr167J2NhokmRsbDRr166pOBEAtCfFD4COtXhxf7q7e5Ik3d09Wby4v+JEANCeFD8AOtbSpRekq6uRJOnq6sr5519YcSIAaE+KHwAdq69vbgYGzkmj0cjAwJL09vZVHQkA2lJP1QEA4HAsXXpBNm26y2gfAEyi0Wq1qs4wFRYluWPr1pFdV3cDAACYKbq6Gpk3b06SnJpk/RPmH+lAAAAAHFmKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzih8AAEDNKX4AAAA1p/gBAADUnOIHAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADWn+AEAANSc4gcAAFBzPVUHmCLdSdLV1ag6BwAAwBG3Wxfq3tv8uhS/JyfJ3LnHVp0DAACgSk9O8v09H2y0Wq0Ksky52Umen+TuJGMVZwEAADjSujNe+r6Z5LE9Z9al+AEAALAPLu4CAABQc4ofAABAzSl+AAAANaf4AQAA1JziBwAAUHOKHwAAQM0pfgAAADXXU3UAJlcUxcuTXJWkMfHf/yrL8saiKFpJjivLcuQg1lmU5DO7LdqX5EllWR6/nwwXJXn3bg+dmOSmsiwv3M96xyT5yyTPnsjxnSRvKMvywaIozkuyPEkryayJXO8uy9KNJTvE3o6zJLfmII6xncdxkvmTrVcUxQeS/Pcki5KcUZbld/ayrd9O8js75xdF0ZVkTZJjJha5O8mvlWW5fmL545NcneS5SXYk+XRZllfubx4zz76eb4uimJfkE0menmR7knVJ3liW5ZaJ59tvlWU5fx/bfH2S30gylmQ0yW+UZXnzxLzJjs0XJvlokh9Jsj7Ja8qyvHdKf2CmXVEU65M8OvFfklxeluUX9vV3XxTF6Rn/e39yxo+XbyZ5c1mWj0xs751JXjOxzoMZPw6/O8n+F2Ufx+f+njuZWYqi+LmMv9bPSnJ/kl8qy/KOoij+W5I/yPgx/ItJTste3ntObOP0JB9LMi/J1iSXlmW5bmLenttZnuTUJM0kI0l+vSzLf93Hsp/eLequ9w2H+n53JlD82lhRFI2Mv6k4e+KN7JlJ1hRF8ZlDWWfiSfsnd1v2QzmAY6AsyxuS3LDbev+S5LoD+BF+NclRSc6YmP7bJG9K8r4kg0meU5blWFEUszL+IvP1JCsPYLtUbF/HWZLesiwP5Rhbn8mPzc8k+XCSm/eR5zlJXphkw27bbBZFcV5ZlsMTy1yW5INJdn5gcW2SL5dl+aqJ+Qt32+Rk82CnVpL3lWX51SQpiuL9SZYl+eXJVpoojB9KclpZlvcURXF+xt/UP3NikWuzl+Nv4g35JzP+xmuwKIp3T+zv9VP8c3FkXLSXD7Guzd6fe7YneVtZlv8ycRxcn+TtSa4qiuInk7wxyTPLsnyoKIq3Jnl/kv96KKEO4LmTGaIoirkZL2xnlWX5H0VRvCbJnyc5L+PH3HvLsvzbifcEX8ve33s2k3wkydVlWX5yYhsfTfLiid3s2s7EPl+727H380n+Oslz9rZs9vG+4VDf784Efgntr5mkd+LPfUnunnhSTpJ3TPyj+JEk7yzL8u8mW2f3jRZFcVSSVyd52W6PfSpJkWR2ktuTvL4sy217rPecjI/4rZyYXpjxF6AnJTk6yefKsvzNicVbGf/EcNbE9LFJ7kqSPT45PzrjBfFxGWl7kx5n+zjGLkzy+xn/tO7vshd7W68sy8GJeXtbfnbGPyF/VZKv7j5v54vHhCdNZE5RFKclOTPJz++27Ob9zaOzFEWxOONvgI+beOgdSX42yTkZf865L+PPcxsmln95xkeNZ2X8WHltWZa37Vx3z+fbsizvz+OPua9l/MOt3TP8YZJzM/4J+JsnRvV2fiJ+XJJ7Mv7v566J5Sc7/p6b5NGd/x4y/mZqfRS/Wpjs737ijez6iT83i6L4RpIfn1hs55kzxyR5KOPPy3fttt3XJ7lsYnJ7kpfvNm9vx+c+nzsn1vmvSd6V8dfu7Rkfrf7axLzXJnlzxt9fDid5U1mW5aH9RmgDz0hyT1mW/zEx/fkknyiK4pokZycpiqJ4c8ZL3L7er56Q8eJ27sS865P8aVEUC5K8c/ftlGX5M3sce7354ev2H+257M6F9va+4UDmzUS+49fGJk57fEWSvy+KYkPGRz0u3W2RsYnRlfOT/EVRFCccwDo7nZ9kU1mWt+722GVlWT6vLMszknw3yeV7We/1ST5VluX2iemhJEvLsnxuxj9ded7EaZzJ+Cc6D2b8jc09SYbLstw1UlgUxfOKorgtyb1Jvpzkcwf2m6FqB3icPe4YK4riRzN+6u/PTxy3j+1j83s7NidzZZJP7us0pKIoPl8UxeYkr0zy1omHn5nxN0Z/VRTFrRPL/MQBzKNDTJwytyLJb5Zl+eyMv/H4ZpJlZVk+f+Kx6zN+WtHOU5H+KsmrJua9MMkdu23yCc+3e+yvK+Olb/ezFuYl+XZZlmcm+fUk1xdFMbssy/sy/sn1rUVRbMz4hyFvnlhnsuPv5Dx+VPu+JF0TPyud51NFUdxWFMWfFUXRlwN87imK4kcy/lq8MknKsvx2xkfk1hdFsSnjp8D91sSyL8r4m+uXTRzXP5PxQpbs4/jcbT9PeO4siuLpSd6T5L9MvO7/v/buPNju8Y7j+DsTQuRSNSHcuEmq+IzWtKilJovRsQTNKENRS2PrpLVMmW7o1HQRVMc+RltRlJLQ6FBKhj+a1lYuMTrGlyFNQhANWtJYsvSP73Mmv5yce+7Jlda9J5/XzJ1zzu/5befc5zy/Z/n+nnMKMKOkjSevCxNK2iXkaI0NXM8DW0vao7w+tjxeBTwBnFkaa83qBF3kNX05QHlcCHRFxFnV/dQOKum6UjZeAHy9bNdw3aJZvWFt6xRtzQ2/fkzSBmThfWhEjAYmATMkdZRVpgGU3rQngS+2sE3NSaxZIJ8gqVvSM8DXqAyTl/PZqCyvbjcYuETS00A3sHNlu/3K4zblb4ik79Q2jIgnygWni+zJHt/aJ2MftxbzWX0e2wt4stL7+6sedt8ob/Z0HnsDuwPX9LRORBwMdJKV/Nq9qoPJiv0NEbEbWeG/q4U0Gzj2Bp6NiIchKxslguEgSY9K+jsZKlcrr/YH7q3ddxIR70fEO5X9rVHe1h3vKvJ+lKsryz4gQzMp4aBLyd7qzYDTgT0iYhRwNnBnCZdy/ls/jC8NsT3I0baraeF/X8re28hw0FrkzWhylHD7iBhJhoveWDY5BLipMnL4bkTU7itsmD9rx+qh7DyQvKd1tqQ5wC3ABqVjbxJ5T/9jJe0i8vpuA1QZfTsKuEzSE8BWZIf/sup6a1H3bPW4p5Sy8VyyA6E3zeoNLdcp1gdu+PVvuwCdEfEQQHlcwqrwjj5tI2kkGep0S2XZeLK3emIZ8fshGcZRdRjwUiX0CbLC8klgr9KI+0NluynAzIh4r1xoppO9jaspvdZ/Ao5s8r6sf2mazxrlsVb0Ybt9yjHnKidL2Ba4X9IB1ZVKCOo04PiyaD4wvxLWNBPYRtLwXtJsACsV5MvIUb2dyQpBfTnXl/3+gpzY4Kj6sPoeHAC8XesEiYgZZGW6t/w3HxhdOe5wYEUJObUBJCIWlMf3yY6rsfRS9kgaTJaNb7EqegHy2vlMRLxaXt9Eg2ttH8+zvuwcBNwXEbtU/joj4vWSdn1l+edL5d0GsIh4ICLGRcTuZAfFUODFutWa1QkWACNL/q3l486yvLdj/xbYV3lfdEPN6g19rYu0Mzf8+reXgW1VbmyStBMwglVfuBPL8h2AXcn7S3rbBnLY/J6IWFxZtjkZ/rG4jOw1umekUa9JLY77vfIFO7SSNhc4UNKgEgY1kZzZE0k7lmVIGgYcBDzT0qdi/UFv+axRHnsU2LXkV8gQoXqNtutRRFxUKh1jImJMOa8DI2KWpC3rGmtHsiqPdQNLamFUkiaQs5Ut7iXNBo5HgM+UUeFaZWMUOcrxWil/plTWnwUcXMufkjaStGklvVF5i6SpZMTCV0olvmoIGSVR61wbCjxHlo271cJFJe0L/Ju857C3vDlU0riy/ynkpFk2gEgaJukT5fkgMjRzDk3+9yW/3kDOAntyrD4D9lxgXLmWQk7qUps05h4ymmdE2WeHpFpnR8P82UvZOQuYWA1BrYQB3l2OtW1ZPljSF/ryGVn/odUnl5oKXBsRS+pW67FOEDnr8BzyPnzK41MR8UaDY3VI6qq8nkR+B5p1bjWrN6xVnWJ94Mld+rGIeE3SN4E7JNV6kU+KiDfLd2sD5Qybm5BTNy8C6Gmbyq4ns3pvIcB95FTQz5OVj9nAnrXE8kUcS8ZwV10J3F7Cpl4GHqyk/ZgM56tdgJ4m47UhG4iTJS0jw1vuJMNabABoljfL88nU5bGIWCTpG8DdkpbSeHKXNbYDkHQlOaPc1sADkhZHRG/33W0N3KCcNXYQWTk6rpzLSkknAr8pHR3/AQ6vVaaapdnAUMrJw4FLS4V4BRnaeTvwLFnO3QtMKOu/IOlUYHppJC4nKw21Cu8a5W2p/J5DlpsPl3J5bkQcVrZZDOwi6XtkHjwm8v7obkk/B/4s6QPyftcjWsh/KyUdD/yyVN7/QcnTNqCMAH5f8tlgMj9+q1m5pJxQ5Tjyetpd8tpDEXEaMJMMpe+W9D4ZinciZAinpAvJcnMFmdcmlfNomD9LRb+nsvMF5ayM05T3Gg4hZ3R+PCJmSzoPuKu8tyHk9637f/Mx2v/JzySNJf+fs4Af1K/QQp1gCnCjpB+RI9aN5p6AnATw9lJmLycbfJN6uf5OpkG9oYW09dKglStdlzEzMzMzM2tnDvU0MzMzMzNrc274mZmZmZmZtTk3/MzMzMzMzNqcG35mZmZmZmZtzg0/MzMzMzOzNueGn5mZmZmZWZvz7/iZmZn1I5LGkL+dtmFELPuYT8fMzNqER/zMzMzMzMzanH/A3czM2pakLuAKYDzZ2XkrcCZwLnAqMBS4DzgjIv5VGW07CfgJ0AGcA3QD04BRwM0RcXrZ/+Syn6eA44FXgdMi4sGS3glcC4wD3gQujohfl7Q9gWuAHYGlwC0Rcbak+UAXsKS8jf2Bx3o653X6gZmZWdvyiJ+ZmbUlSYOBPwLzgDHASOA2YHL52xfYjmzcXV23+V7ADsBRwOXAecB+wGeBr0rap27dF4HhwPnATElblLTbgJeBTuAIYKqkL5W0K4ArImIz4NPAjLJ8QnncPCI6IuKRFs/ZzMysR274mZlZu9qTbHB9NyKWRMR7EfFX4Fjg0oh4KSLeJUf0jpZUve/9p2X9WeTI260RsSgiXgH+AuxaWXcRcHlEfBgR04EADimjjWOB75d9zQGuA04o230IbC9peES8GxGPNnkvrZyzmZlZj9zwMzOzdtUFzGswQUonOQpYM4+c7GxEZdnrledLG7zuqLx+JSKq903MK8foBN6MiHfq0kaW5yeTYZ7PSXpc0pebvJdWztnMzKxHbviZmVm7WgCMajAqthAYXXk9CljG6o27tTFS0qC6/S0sf1tI2rQu7RWAiHghIo4BtgIuBu6QNAxodPP9uj5nMzNbz7jhZ2Zm7epv5GQrF0kaJmljSWPJCV7OkvQpSR3AVGD6R/jphK2AMyVtKOlIYCfg3ohYADwMXFiO/TlylO9mAEnHSdoyIlYAb5d9rQDeKI/bVY6xrs/ZzMzWM743wMzM2lJELJc0CbgSmE+OpP0O+DYZOjkb2Bi4HzjjIxzqMXIimH+SI3BHRMTiknYMOavnQuAt4PyIeKCkTQQulbQJGbp5dEQsBZB0AfCQpA3Letev43M2M7P1jH/OwczMrI/KzzmcEhHjPu5zMTMza8ahnmZmZmZmZm3ODT8zMzMzM7M251BPMzMzMzOzNucRPzMzMzMzszbnhp+ZmZmZmVmbc8PPzMzMzMyszbnhZ2ZmZmZm1ubc8DMzMzMzM2tzbviZmZmZmZm1uf8CHCUiiVGjSokAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxBjIeGchPLX"
      },
      "source": [
        "#Desafio 1: Encontrar um top 10, das ações do MOA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "O-E7ut4uhhNd",
        "outputId": "5c2b2595-5044-4805-fee3-9aa09f48420b"
      },
      "source": [
        "sufixo = [valor.split('_')[-1] for valor in dados_resultados.columns]\n",
        "sufixo = pd.Series(sufixo)\n",
        "\n",
        "sufixo_top10 = sufixo.value_counts()[0:10]\n",
        "plt.figure(figsize = (15,10))\n",
        "ax = sns.barplot(x = sufixo_top10.index, y = sufixo_top10.values)\n",
        "ax.set(xlabel = 'Tipos de mecânismos de ação', ylabel = 'Contagem')\n",
        "ax.set_title('10 tipos de mecânismos de ação mais frequêntes', fontdict = {'fontsize':20})\n",
        "plt.plot()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KMd_iF_i37-"
      },
      "source": [
        "# Desafio 2: Criar uma nova coluna eh_controle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cmYbEq0jHpX"
      },
      "source": [
        "dados_combinados['eh_controle'] = (dados_combinados['tratamento'] == \"com_controle\")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "is5H_fqwjUTG",
        "outputId": "a76ede2f-ccbc-4802-dc71-2735047c0d0d"
      },
      "source": [
        "dados_combinados[['tratamento','eh_controle']]"
      ],
      "execution_count": 14,
      "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>tratamento</th>\n",
              "      <th>eh_controle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>com_controle</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tratamento  eh_controle\n",
              "0         com_droga        False\n",
              "1         com_droga        False\n",
              "2         com_droga        False\n",
              "3         com_droga        False\n",
              "4         com_droga        False\n",
              "...             ...          ...\n",
              "23809     com_droga        False\n",
              "23810     com_droga        False\n",
              "23811  com_controle         True\n",
              "23812     com_droga        False\n",
              "23813     com_droga        False\n",
              "\n",
              "[23814 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "015kk4Fgjmgx"
      },
      "source": [
        "#Desafio 03: Criar três colunas para indicar o tempo 24, 48, 72"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fLaZqh0jvB4",
        "outputId": "ea6032b4-0ba3-4c41-fa3c-525c12415699"
      },
      "source": [
        "dados_combinados['tempo_copy'] = dados_combinados['tempo']\n",
        "dados_combinados = pd.get_dummies(dados_combinados, prefix = 'tempo', columns=['tempo_copy'])\n",
        "print(dados_combinados[['tempo', 'tempo_24', 'tempo_48', 'tempo_72']])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       tempo  tempo_24  tempo_48  tempo_72\n",
            "0         24         1         0         0\n",
            "1         72         0         0         1\n",
            "2         48         0         1         0\n",
            "3         48         0         1         0\n",
            "4         72         0         0         1\n",
            "...      ...       ...       ...       ...\n",
            "23809     24         1         0         0\n",
            "23810     24         1         0         0\n",
            "23811     48         0         1         0\n",
            "23812     24         1         0         0\n",
            "23813     72         0         0         1\n",
            "\n",
            "[23814 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhi0xmtYmi4l"
      },
      "source": [
        "#Desafio 04: Estudar sobre combinações de DF https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhsL2ObgnAgP",
        "outputId": "ab08a4af-5aa9-40ea-9d03-88a82d872af9"
      },
      "source": [
        "tabela_a = pd.DataFrame({'animal':['coelho','cachorro','gorila','elefante','peixe']})\n",
        "tabela_b = pd.DataFrame({'animal':['gorila','gato','leão','tubarão','peixe']})\n",
        "print(\"TABELA A\\n\")\n",
        "print(tabela_a)\n",
        "print('\\nTABELA B\\n')\n",
        "print(tabela_b)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TABELA A\n",
            "\n",
            "     animal\n",
            "0    coelho\n",
            "1  cachorro\n",
            "2    gorila\n",
            "3  elefante\n",
            "4     peixe\n",
            "\n",
            "TABELA B\n",
            "\n",
            "    animal\n",
            "0   gorila\n",
            "1     gato\n",
            "2     leão\n",
            "3  tubarão\n",
            "4    peixe\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIy84Kx7oG2Q"
      },
      "source": [
        "1. Método INNER, traz apenas o que é comum nas duas tabelas, intersecção das chaves\n",
        "\n",
        "\n",
        "\n",
        "![Inner.png](data:image/png;base64,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)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "igbw2wBBn7MQ",
        "outputId": "a271b2f6-19e4-4d91-ac91-2060a09ba815"
      },
      "source": [
        "pd.merge(tabela_a, tabela_b, how = 'inner', on = 'animal')"
      ],
      "execution_count": 17,
      "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>animal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>gorila</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>peixe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   animal\n",
              "0  gorila\n",
              "1   peixe"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kV_DXcMmtgBY"
      },
      "source": [
        "2. Método LEFT, traz apenas as chaves da primeira coluna\n",
        "\n",
        "![left.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "qZvGb0Gmsi8N",
        "outputId": "a7c7cb4f-b241-49e1-f30d-0dcb29084ee3"
      },
      "source": [
        "pd.merge(tabela_a, tabela_b, how = 'left', on = 'animal')"
      ],
      "execution_count": 18,
      "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>animal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>coelho</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cachorro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>gorila</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>elefante</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>peixe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     animal\n",
              "0    coelho\n",
              "1  cachorro\n",
              "2    gorila\n",
              "3  elefante\n",
              "4     peixe"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0_pTR7MuSnd"
      },
      "source": [
        "3. Método RIGHT, tras apenas as chaves da segunda tabela\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "md9mcCS1sjGQ",
        "outputId": "534c1e7e-9e71-4e87-bed4-9f5e02e886dc"
      },
      "source": [
        "pd.merge(tabela_a, tabela_b, how = 'right', on = 'animal')"
      ],
      "execution_count": 19,
      "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>animal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>gorila</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>gato</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>leão</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tubarão</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>peixe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    animal\n",
              "0   gorila\n",
              "1     gato\n",
              "2     leão\n",
              "3  tubarão\n",
              "4    peixe"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1G1QESPulKb"
      },
      "source": [
        "4. Método OUTER, traz as chaves de ambas as tabelas, união das chaves\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "54-eEmAprtf3",
        "outputId": "0adba489-1c45-4214-864d-06cfe2058be7"
      },
      "source": [
        "pd.merge(tabela_a, tabela_b, how = 'outer', on = 'animal')"
      ],
      "execution_count": 20,
      "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>animal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>coelho</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cachorro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>gorila</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>elefante</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>peixe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>gato</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>leão</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tubarão</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     animal\n",
              "0    coelho\n",
              "1  cachorro\n",
              "2    gorila\n",
              "3  elefante\n",
              "4     peixe\n",
              "5      gato\n",
              "6      leão\n",
              "7   tubarão"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1sP7oIdx8ev"
      },
      "source": [
        "#Desafio 05: Fazer analise mais detalhada considerando tempo e dose para comparar as distribuições (Um composto especifico)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "55VFT8vFy9kj",
        "outputId": "feffb14a-27c0-414d-d915-16871b9ca3eb"
      },
      "source": [
        "composto_principal = dados_combinados['composto'].value_counts().index[1]\n",
        "fig, axes = plt.subplots(1, 3, figsize = (20,10))\n",
        "fig.suptitle('Boxplots composto {}'.format(composto_principal), fontsize = 20)\n",
        "sns.boxplot(ax = axes[0], \n",
        "            data = dados_combinados.query('composto in @composto_principal'), \n",
        "            y='g0', x='composto', hue = 'n_moa')\n",
        "axes[0].set_title('Boxplot MOA')\n",
        "sns.boxplot(ax = axes[1], \n",
        "            data = dados_combinados.query('composto in @composto_principal'), \n",
        "            y='g0', x='composto', hue = 'tempo')\n",
        "axes[1].set_title('Boxplot Tempo')\n",
        "sns.boxplot(ax = axes[2], \n",
        "            data = dados_combinados.query('composto in @composto_principal'), \n",
        "            y='g0', x='composto', hue = 'dose')\n",
        "axes[2].set_title('Boxplot Dose')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot Dose')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJndzzodyC8D"
      },
      "source": [
        "#Desafio 06: Descobrir se temos algum composto que dependendo da configuração do experimênto ativa ou não ativa o MOA\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "id": "bxONIwVs2MD2",
        "outputId": "609e17c1-c82e-44d9-cf9b-26adcfbcd595"
      },
      "source": [
        "pd.merge(dados_combinados[['composto','ativo_moa']].query('ativo_moa == True'), \n",
        "         dados_combinados[['composto','ativo_moa']].query('ativo_moa == False'), \n",
        "         on = 'composto')"
      ],
      "execution_count": 49,
      "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>composto</th>\n",
              "      <th>ativo_moa_x</th>\n",
              "      <th>ativo_moa_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [composto, ativo_moa_x, ativo_moa_y]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13RY04E-Mwzg"
      },
      "source": [
        "Ao tentar fazer merge buscando compostos que tenham MOA ativo e MOA inativo não foi possível encontrar resultados, ou seja, não há compostos que em um experimento ative MOA e em outro experimento não ative MOA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VyC9EB5yUhQ"
      },
      "source": [
        "#Desafio 07: Descobrir se temos algum composto que dependendo da configuração do experimênto ativa MOAS diferentes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Up2-assoPt7V"
      },
      "source": [
        "O código abaixo traz os valores de n_moa para cada composto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEaP0zr0PUhG",
        "outputId": "1a04a138-c66b-472a-e822-45aaa28b8a46"
      },
      "source": [
        "for composto in set(dados_combinados['composto'][:3]):\n",
        "    print(dados_combinados.query('composto == @composto')['n_moa'])"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1        0\n",
            "5389     0\n",
            "6070     0\n",
            "12328    0\n",
            "14089    0\n",
            "19987    0\n",
            "Name: n_moa, dtype: int64\n",
            "0        1\n",
            "6175     1\n",
            "13774    1\n",
            "14257    1\n",
            "20190    1\n",
            "22507    1\n",
            "Name: n_moa, dtype: int64\n",
            "2        3\n",
            "3590     3\n",
            "3911     3\n",
            "8457     3\n",
            "11030    3\n",
            "14446    3\n",
            "15218    3\n",
            "Name: n_moa, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHOJzMfYP4iF"
      },
      "source": [
        "O desvio padrão mostra o quanto um valor é uniforme\n",
        "utilizando como exemplo o composto em que todos os valores n_moa são iguais a 3 o calculo do desvio padrão ocorre da seguinte forma:\n",
        "\n",
        "* Temos 7 ocorrencias então n = 7;\n",
        "* A média (M) de todas as ocorrencias é = 3;\n",
        "* A ocorrencia i = 3 menos a média de todas as ocorrencias é = 0;\n",
        "* O somatório em todas as ocorrências será 0 caso o valor de n_moa não variar;\n",
        "* Finalizando os calculos veremos que o desvio padrão será 0 se os valores de n_moa forem os mesmos para todas as ocorrências.\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9FKGa3MRYrX"
      },
      "source": [
        "O código abaixo calcula o desvio padrão dos valores de n_moa em todos os compostos e os armazena em uma lista, ao final é consultado todos os valores unicos da lista, o unico valor é 0, ou seja todos os desvios padrões são 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trQt9UMgNJrn",
        "outputId": "df7b992f-d7d1-4078-f507-3a2785a8057f"
      },
      "source": [
        "desvios = list()\n",
        "for composto in set(dados_combinados['composto']):\n",
        "    desvios.append(np.std(dados_combinados.query('composto == @composto')['n_moa']))\n",
        "\n",
        "print('Valores unicos da lista de desvio padrão são : {}'.format(set(desvios)))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Valores unicos da lista de desvio padrão são : {0.0}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6j4L5iL9NR5j"
      },
      "source": [
        "Conclui-se, que como todos os desvios padrões são 0 então o valor de n_moa não variou em nenhum composto, para todas as experiencias feitas em um composto ele sempre ativou o mesmo número de compostos, ou não ativou composto algum."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNE1JjllywGb"
      },
      "source": [
        "#Desafio 08: Resumo\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZ2JIxZ_SVSm"
      },
      "source": [
        "Na aula 4 foi possível entender melhor sobre os resultados dos experimentos, os principais tópicos abordados foram:\n",
        "\n",
        "* Conceitos sobre mecânismos de ação;\n",
        "* Função merge do pandas;\n",
        "* Experimentos com doses e tempos diferentes podem afetar o comportamento do composto em um gene;\n",
        "* Experimentos diferentes não alteram o número de mecânismos de ação que um composto ativa. "
      ]
    }
  ]
}