ImersãoDados.ipynb

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  "metadata": {
    "colab": {
      "name": "ImersãoDados.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "kmWeBIX4cjBM",
        "ugOX9SOoWjTB",
        "hmR6cbjMtz0x"
      ],
      "authorship_tag": "ABX9TyPukJDENCPtykRWjA7FWqfm",
      "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/Strubis/DataScience/blob/main/Imers%C3%A3oDados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUBqv_mStMew"
      },
      "source": [
        "##Códigos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "jHyJH3rGcPY3",
        "outputId": "da455fc8-60f7-41de-c5cc-4033d674ce0a"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "url_data = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "dados = pd.read_csv(url_data, compression='zip')\n",
        "\n",
        "dados.tail()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>23812</th>\n",
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              "      <td>24</td>\n",
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              "      <td>0.9038</td>\n",
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              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
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              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
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              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.9170</td>\n",
              "      <td>-1.8640</td>\n",
              "      <td>-2.5090</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "23809  id_fffb1ceed     com_droga     24   D2  ...  0.0631  0.9171  0.5258  0.4680\n",
              "23810  id_fffb70c0c     com_droga     24   D2  ... -0.2084 -0.1224 -0.2715  0.3689\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...  0.2256  0.7592  0.6656  0.3808\n",
              "23812  id_fffcb9e7c     com_droga     24   D1  ...  0.1732  0.7015 -0.6290  0.0740\n",
              "23813  id_ffffdd77b     com_droga     72   D1  ... -3.5770 -0.4775 -2.1500 -4.2520\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4Vq-Vx3pG06"
      },
      "source": [
        "dados['tratamento'].value_counts(normalize = True)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "8guqr8AvpbKS",
        "outputId": "c30ff7fd-a3ab-4234-aadc-ae4ddecbc5ac"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "graf = dados['tempo'].value_counts().plot.bar()\n",
        "\n",
        "graf.set_title('Experiment Time')\n",
        "graf.set_xlabel('Hours')\n",
        "graf.set_ylabel('Value')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3-ClQ7qrdPB",
        "outputId": "8d83a8b2-2798-476d-e741-d46589f82142"
      },
      "source": [
        "dados['droga'].value_counts().shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3289,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "fde0uFElr4wR",
        "outputId": "b8e0fd72-a11f-4a97-ec8c-80b6e0ca6bb9"
      },
      "source": [
        "dados.query('tempo > 50').head()\n",
        "\n",
        "dados_formatados = dados['g-0'].replace(\"-\", \"\")\n",
        "\n",
        "dados.query('@dados_formatados > 0')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>...</th>\n",
              "      <th>c-60</th>\n",
              "      <th>c-61</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>id_001bd861f</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>746ca1f5a</td>\n",
              "      <td>2.0440</td>\n",
              "      <td>1.7000</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>5.9440</td>\n",
              "      <td>-2.1670</td>\n",
              "      <td>-4.0360</td>\n",
              "      <td>3.6950</td>\n",
              "      <td>1.4530</td>\n",
              "      <td>0.9724</td>\n",
              "      <td>-2.4380</td>\n",
              "      <td>5.1340</td>\n",
              "      <td>-4.6600</td>\n",
              "      <td>5.7960</td>\n",
              "      <td>4.9700</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>2.4790</td>\n",
              "      <td>-3.8010</td>\n",
              "      <td>-1.0230</td>\n",
              "      <td>5.3110</td>\n",
              "      <td>-0.5660</td>\n",
              "      <td>-4.4450</td>\n",
              "      <td>1.2210</td>\n",
              "      <td>1.3750</td>\n",
              "      <td>0.3932</td>\n",
              "      <td>0.3392</td>\n",
              "      <td>4.3510</td>\n",
              "      <td>5.7540</td>\n",
              "      <td>-1.6590</td>\n",
              "      <td>5.3000</td>\n",
              "      <td>5.6050</td>\n",
              "      <td>5.5320</td>\n",
              "      <td>3.7860</td>\n",
              "      <td>0.9197</td>\n",
              "      <td>1.9310</td>\n",
              "      <td>2.4870</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.8904</td>\n",
              "      <td>2.0590</td>\n",
              "      <td>0.5052</td>\n",
              "      <td>0.6890</td>\n",
              "      <td>0.5340</td>\n",
              "      <td>0.5135</td>\n",
              "      <td>0.6507</td>\n",
              "      <td>0.9838</td>\n",
              "      <td>0.4532</td>\n",
              "      <td>-0.3212</td>\n",
              "      <td>0.2662</td>\n",
              "      <td>0.6864</td>\n",
              "      <td>1.6300</td>\n",
              "      <td>1.3810</td>\n",
              "      <td>0.0061</td>\n",
              "      <td>0.2908</td>\n",
              "      <td>0.2598</td>\n",
              "      <td>0.1266</td>\n",
              "      <td>-0.3526</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>-1.1030</td>\n",
              "      <td>1.3650</td>\n",
              "      <td>1.0610</td>\n",
              "      <td>0.4034</td>\n",
              "      <td>1.0880</td>\n",
              "      <td>-0.4230</td>\n",
              "      <td>0.4013</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.7464</td>\n",
              "      <td>0.9418</td>\n",
              "      <td>0.1855</td>\n",
              "      <td>1.1720</td>\n",
              "      <td>0.8325</td>\n",
              "      <td>0.6486</td>\n",
              "      <td>0.8090</td>\n",
              "      <td>1.5880</td>\n",
              "      <td>2.4670</td>\n",
              "      <td>0.0357</td>\n",
              "      <td>0.1351</td>\n",
              "      <td>-0.3179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>id_0020d0484</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.2711</td>\n",
              "      <td>0.5133</td>\n",
              "      <td>-0.1327</td>\n",
              "      <td>2.5950</td>\n",
              "      <td>0.6980</td>\n",
              "      <td>0.5846</td>\n",
              "      <td>-0.2633</td>\n",
              "      <td>-2.1490</td>\n",
              "      <td>0.4881</td>\n",
              "      <td>1.4750</td>\n",
              "      <td>-0.0488</td>\n",
              "      <td>-0.0561</td>\n",
              "      <td>0.1641</td>\n",
              "      <td>0.1749</td>\n",
              "      <td>-0.3464</td>\n",
              "      <td>-0.1811</td>\n",
              "      <td>0.5685</td>\n",
              "      <td>1.2970</td>\n",
              "      <td>1.0510</td>\n",
              "      <td>-0.9585</td>\n",
              "      <td>0.7911</td>\n",
              "      <td>-0.2991</td>\n",
              "      <td>2.7420</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>-1.0240</td>\n",
              "      <td>0.7268</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>-1.9730</td>\n",
              "      <td>1.1340</td>\n",
              "      <td>-2.5720</td>\n",
              "      <td>0.2068</td>\n",
              "      <td>0.9992</td>\n",
              "      <td>-0.1958</td>\n",
              "      <td>0.2262</td>\n",
              "      <td>1.0140</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2623</td>\n",
              "      <td>-0.5500</td>\n",
              "      <td>-0.0530</td>\n",
              "      <td>0.2077</td>\n",
              "      <td>-0.0825</td>\n",
              "      <td>0.2156</td>\n",
              "      <td>0.8142</td>\n",
              "      <td>1.0990</td>\n",
              "      <td>1.0860</td>\n",
              "      <td>0.4011</td>\n",
              "      <td>-0.1342</td>\n",
              "      <td>0.4653</td>\n",
              "      <td>-0.1104</td>\n",
              "      <td>0.3935</td>\n",
              "      <td>1.2370</td>\n",
              "      <td>-0.5072</td>\n",
              "      <td>0.0351</td>\n",
              "      <td>0.5084</td>\n",
              "      <td>-0.1248</td>\n",
              "      <td>0.4351</td>\n",
              "      <td>0.1389</td>\n",
              "      <td>0.6726</td>\n",
              "      <td>0.6070</td>\n",
              "      <td>0.3431</td>\n",
              "      <td>-0.4116</td>\n",
              "      <td>0.1194</td>\n",
              "      <td>0.3420</td>\n",
              "      <td>0.1331</td>\n",
              "      <td>0.8541</td>\n",
              "      <td>0.6282</td>\n",
              "      <td>0.3230</td>\n",
              "      <td>-0.4140</td>\n",
              "      <td>0.2532</td>\n",
              "      <td>0.0513</td>\n",
              "      <td>0.8600</td>\n",
              "      <td>1.4250</td>\n",
              "      <td>0.6633</td>\n",
              "      <td>0.4562</td>\n",
              "      <td>-0.9622</td>\n",
              "      <td>0.0260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23807</th>\n",
              "      <td>id_fff7e6992</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>a28556d51</td>\n",
              "      <td>0.1420</td>\n",
              "      <td>-0.3696</td>\n",
              "      <td>-0.0093</td>\n",
              "      <td>-0.2495</td>\n",
              "      <td>-0.0175</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>-0.2201</td>\n",
              "      <td>0.2443</td>\n",
              "      <td>0.4879</td>\n",
              "      <td>0.3432</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>0.9056</td>\n",
              "      <td>0.3538</td>\n",
              "      <td>0.5246</td>\n",
              "      <td>-0.4441</td>\n",
              "      <td>-0.1605</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-0.5619</td>\n",
              "      <td>-0.1409</td>\n",
              "      <td>0.1255</td>\n",
              "      <td>0.1214</td>\n",
              "      <td>-0.1975</td>\n",
              "      <td>-0.4367</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4301</td>\n",
              "      <td>-0.7606</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>0.0270</td>\n",
              "      <td>-0.0367</td>\n",
              "      <td>-1.2480</td>\n",
              "      <td>0.0559</td>\n",
              "      <td>-0.0984</td>\n",
              "      <td>0.5537</td>\n",
              "      <td>0.1063</td>\n",
              "      <td>0.3893</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5554</td>\n",
              "      <td>0.2225</td>\n",
              "      <td>-0.0903</td>\n",
              "      <td>0.9227</td>\n",
              "      <td>-0.0460</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>1.0730</td>\n",
              "      <td>-0.6904</td>\n",
              "      <td>0.9298</td>\n",
              "      <td>1.0500</td>\n",
              "      <td>0.4646</td>\n",
              "      <td>0.4481</td>\n",
              "      <td>0.9771</td>\n",
              "      <td>0.7187</td>\n",
              "      <td>0.5299</td>\n",
              "      <td>0.1079</td>\n",
              "      <td>1.2010</td>\n",
              "      <td>0.2529</td>\n",
              "      <td>-0.4546</td>\n",
              "      <td>0.7998</td>\n",
              "      <td>0.6401</td>\n",
              "      <td>-1.3370</td>\n",
              "      <td>0.6542</td>\n",
              "      <td>0.8561</td>\n",
              "      <td>0.4829</td>\n",
              "      <td>0.3213</td>\n",
              "      <td>-0.0177</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.4626</td>\n",
              "      <td>0.0030</td>\n",
              "      <td>0.8775</td>\n",
              "      <td>0.5230</td>\n",
              "      <td>0.1589</td>\n",
              "      <td>0.7715</td>\n",
              "      <td>0.5497</td>\n",
              "      <td>0.6929</td>\n",
              "      <td>0.3738</td>\n",
              "      <td>0.8636</td>\n",
              "      <td>0.4494</td>\n",
              "      <td>0.1218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23808</th>\n",
              "      <td>id_fff8c2444</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>6c3a459be</td>\n",
              "      <td>0.1608</td>\n",
              "      <td>-1.0500</td>\n",
              "      <td>0.2551</td>\n",
              "      <td>-0.2239</td>\n",
              "      <td>-0.2431</td>\n",
              "      <td>0.4256</td>\n",
              "      <td>-0.1166</td>\n",
              "      <td>-0.1777</td>\n",
              "      <td>-0.7480</td>\n",
              "      <td>0.1368</td>\n",
              "      <td>0.5493</td>\n",
              "      <td>-0.5083</td>\n",
              "      <td>0.4112</td>\n",
              "      <td>0.1181</td>\n",
              "      <td>0.7861</td>\n",
              "      <td>-0.1877</td>\n",
              "      <td>0.2476</td>\n",
              "      <td>0.1956</td>\n",
              "      <td>0.0273</td>\n",
              "      <td>0.1182</td>\n",
              "      <td>-0.5037</td>\n",
              "      <td>-0.5939</td>\n",
              "      <td>0.4825</td>\n",
              "      <td>-0.5187</td>\n",
              "      <td>0.1618</td>\n",
              "      <td>-0.2253</td>\n",
              "      <td>0.3153</td>\n",
              "      <td>0.4839</td>\n",
              "      <td>0.0520</td>\n",
              "      <td>1.3850</td>\n",
              "      <td>0.6502</td>\n",
              "      <td>-0.4198</td>\n",
              "      <td>0.2723</td>\n",
              "      <td>-0.3629</td>\n",
              "      <td>1.0820</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2720</td>\n",
              "      <td>-0.6021</td>\n",
              "      <td>0.2451</td>\n",
              "      <td>0.6607</td>\n",
              "      <td>-0.1849</td>\n",
              "      <td>-0.6546</td>\n",
              "      <td>-0.5798</td>\n",
              "      <td>0.3449</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>0.2949</td>\n",
              "      <td>0.4158</td>\n",
              "      <td>0.8586</td>\n",
              "      <td>0.3868</td>\n",
              "      <td>1.0030</td>\n",
              "      <td>-0.4593</td>\n",
              "      <td>0.6498</td>\n",
              "      <td>0.8293</td>\n",
              "      <td>-0.7645</td>\n",
              "      <td>0.5379</td>\n",
              "      <td>0.5610</td>\n",
              "      <td>0.2577</td>\n",
              "      <td>-0.5493</td>\n",
              "      <td>0.2690</td>\n",
              "      <td>0.6392</td>\n",
              "      <td>-0.5774</td>\n",
              "      <td>-0.3393</td>\n",
              "      <td>0.5068</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-0.4559</td>\n",
              "      <td>0.3734</td>\n",
              "      <td>0.0789</td>\n",
              "      <td>0.3538</td>\n",
              "      <td>0.0558</td>\n",
              "      <td>0.3377</td>\n",
              "      <td>-0.4753</td>\n",
              "      <td>-0.2504</td>\n",
              "      <td>-0.7415</td>\n",
              "      <td>0.8413</td>\n",
              "      <td>-0.4259</td>\n",
              "      <td>0.2434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>0.4024</td>\n",
              "      <td>-0.0895</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9641</td>\n",
              "      <td>-0.1846</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11769 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "0      id_000644bb2     com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1      id_000779bfc     com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2      id_000a6266a     com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "6      id_001bd861f     com_droga     24   D2  ...  2.4670  0.0357  0.1351 -0.3179\n",
              "7      id_0020d0484     com_droga     48   D1  ...  0.6633  0.4562 -0.9622  0.0260\n",
              "...             ...           ...    ...  ...  ...     ...     ...     ...     ...\n",
              "23807  id_fff7e6992     com_droga     48   D1  ...  0.3738  0.8636  0.4494  0.1218\n",
              "23808  id_fff8c2444     com_droga     72   D1  ... -0.7415  0.8413 -0.4259  0.2434\n",
              "23809  id_fffb1ceed     com_droga     24   D2  ...  0.0631  0.9171  0.5258  0.4680\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...  0.2256  0.7592  0.6656  0.3808\n",
              "23812  id_fffcb9e7c     com_droga     24   D1  ...  0.1732  0.7015 -0.6290  0.0740\n",
              "\n",
              "[11769 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmWeBIX4cjBM"
      },
      "source": [
        "###Códigos Aula 02"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1-5JJOQ0cpen",
        "outputId": "c6316a5f-2ff3-4904-ccfe-e4fab3bf8f22"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Renomea toda a coluna 'droga' para 'composto'\n",
        "mapa = {'droga':'composto'}\n",
        "dados.rename(columns=mapa, inplace=True)\n",
        "\n",
        "#Pega os cinco primeiros elementos, somente o nome\n",
        "cod_compostos = dados['composto'].value_counts().index[0:5]\n",
        "\n",
        "#Ordenando crescentemente os valores\n",
        "composto_order = cod_compostos.sort_values(ascending=True)\n",
        "\n",
        "#Plotando o gráfico\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.set()\n",
        "ax = sns.countplot(x='composto', hue='composto', order=composto_order, \n",
        "                   data=dados.query('composto in @cod_compostos'))\n",
        "ax.set_title('Compostos', fontsize=15, fontweight='bold')\n",
        "ax.set_xlabel('Nome do Composto', fontsize=10)\n",
        "ax.set_ylabel('Quantidade', fontsize=10)\n",
        "\n",
        "#Formatando o topo do gráfico\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "95Pdnrotl7_8",
        "outputId": "c321907e-2abb-47cd-8e0c-0853ffab9b3d"
      },
      "source": [
        "#Descrevendo os dados dos compostos\n",
        "dados.loc[:, 'g-0':'g-771'].describe().T\n",
        "\n",
        "#Descrevendo os dados (mean) dos compostos com um histograma\n",
        "ax = dados.loc[:, 'c-0':'c-99'].describe().T['mean'].hist(bins=20)\n",
        "\n",
        "ax.set_title('Compostos C-0 até C-99', fontsize=15, fontweigth='bold')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "vlUnXr5mq9J6",
        "outputId": "8da2d315-7466-4daa-bda7-b9e14b880328"
      },
      "source": [
        "#Gráfico boxplot\n",
        "#sns.boxplot(x='g-0', data=dados)\n",
        "\n",
        "ax = sns.boxplot(x='tempo', y='g-4', data=dados, linewidth=1.5)\n",
        "ax.set_title('Boxplot')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "K7-_wCfCpBV5",
        "outputId": "5f28bdb2-482d-45ea-bb38-7ff6ab941772"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(data=dados, x='g-10', bins=50, hue='tempo')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugOX9SOoWjTB"
      },
      "source": [
        "###Códigos Aula 03"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "WSVGzv9GWqM3",
        "outputId": "3112958e-89a8-48a0-e247-d7e7e76e18b0"
      },
      "source": [
        "# Proporção dos valores pela linha\n",
        "pd.crosstab([dados['dose'], dados['tempo']], dados['tratamento'], normalize='index')"
      ],
      "execution_count": null,
      "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>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.077458</td>\n",
              "      <td>0.922542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.078778</td>\n",
              "      <td>0.921222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.078577</td>\n",
              "      <td>0.921423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.078487</td>\n",
              "      <td>0.921513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.078285</td>\n",
              "      <td>0.921715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.078507</td>\n",
              "      <td>0.921493</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.077458   0.922542\n",
              "     48         0.078778   0.921222\n",
              "     72         0.078577   0.921423\n",
              "D2   24         0.078487   0.921513\n",
              "     48         0.078285   0.921715\n",
              "     72         0.078507   0.921493"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "PIQ76UhBcQpM",
        "outputId": "bcb82510-b23f-46c3-8d12-f91faa58252c"
      },
      "source": [
        "#Normalizando pela coluna\n",
        "pd.crosstab([dados['dose'], dados['tempo']], dados['tratamento'], normalize='columns')"
      ],
      "execution_count": null,
      "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>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.161308</td>\n",
              "      <td>0.163341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.183816</td>\n",
              "      <td>0.182750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.164523</td>\n",
              "      <td>0.164024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.163451</td>\n",
              "      <td>0.163158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.163451</td>\n",
              "      <td>0.163614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.163451</td>\n",
              "      <td>0.163113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.161308   0.163341\n",
              "     48         0.183816   0.182750\n",
              "     72         0.164523   0.164024\n",
              "D2   24         0.163451   0.163158\n",
              "     48         0.163451   0.163614\n",
              "     72         0.163451   0.163113"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "oZSLDzCRW-SJ",
        "outputId": "968f7758-4b30-4fab-c950-ab3ac3e9eb7c"
      },
      "source": [
        "#Usando o groupby()\n",
        "pd.crosstab([dados['dose'], dados['tempo']], dados['tratamento'], normalize='index').groupby('dose').count()"
      ],
      "execution_count": null,
      "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>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>D1</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose                               \n",
              "D1                     3          3\n",
              "D2                     3          3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "qljktNp7rNPO",
        "outputId": "9859f982-7e01-42b2-f645-d5fb1841dc35"
      },
      "source": [
        "com_droga = dados[dados['tratamento']=='com_droga'].groupby(['dose','tempo']).count()['tratamento']\n",
        "com_controle = dados[dados['tratamento']=='com_controle'].groupby(['dose','tempo']).count()['tratamento']\n",
        "\n",
        "concat = pd.concat([com_controle, com_droga], axis=1) #Juntando as duas tabelas\n",
        "concat.columns = ['com_controle', 'com_droga'] #Renomeando as colunas\n",
        "concat.div(concat.sum(axis=1), axis=0) #Dados normalizados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.077458</td>\n",
              "      <td>0.922542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.078778</td>\n",
              "      <td>0.921222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.078577</td>\n",
              "      <td>0.921423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.078487</td>\n",
              "      <td>0.921513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.078285</td>\n",
              "      <td>0.921715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.078507</td>\n",
              "      <td>0.921493</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.077458   0.922542\n",
              "     48         0.078778   0.921222\n",
              "     72         0.078577   0.921423\n",
              "D2   24         0.078487   0.921513\n",
              "     48         0.078285   0.921715\n",
              "     72         0.078507   0.921493"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "JWJBuu-Cbrl4",
        "outputId": "b3498527-6f1a-4876-b4ca-4d8065779cb3"
      },
      "source": [
        "pd.crosstab([dados['tratamento'], dados['dose']], dados['tempo'], normalize='index')"
      ],
      "execution_count": null,
      "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>tempo</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tratamento</th>\n",
              "      <th>dose</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">com_controle</th>\n",
              "      <th>D1</th>\n",
              "      <td>0.316509</td>\n",
              "      <td>0.360673</td>\n",
              "      <td>0.322818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">com_droga</th>\n",
              "      <th>D1</th>\n",
              "      <td>0.320204</td>\n",
              "      <td>0.358253</td>\n",
              "      <td>0.321543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>0.333054</td>\n",
              "      <td>0.333984</td>\n",
              "      <td>0.332961</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tempo                    24        48        72\n",
              "tratamento   dose                              \n",
              "com_controle D1    0.316509  0.360673  0.322818\n",
              "             D2    0.333333  0.333333  0.333333\n",
              "com_droga    D1    0.320204  0.358253  0.321543\n",
              "             D2    0.333054  0.333984  0.332961"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "lrG4Q2UBZ7TT",
        "outputId": "4731b3c5-2572-44f1-d536-e6ecf754265f"
      },
      "source": [
        "pd.crosstab(dados['tratamento'], dados['tempo'], normalize='index').groupby('tratamento').mean()"
      ],
      "execution_count": null,
      "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>tempo</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tratamento</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>com_controle</th>\n",
              "      <td>0.324759</td>\n",
              "      <td>0.347267</td>\n",
              "      <td>0.327974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>0.326499</td>\n",
              "      <td>0.346364</td>\n",
              "      <td>0.327137</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tempo               24        48        72\n",
              "tratamento                                \n",
              "com_controle  0.324759  0.347267  0.327974\n",
              "com_droga     0.326499  0.346364  0.327137"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "uC_SRkMYefdI",
        "outputId": "9bf25eb7-8e93-42bc-996e-2ab04fffdbd5"
      },
      "source": [
        "#Usando a aggfunc para pegar os valores menores para o g-10\n",
        "pd.crosstab(dados['dose'], dados['tempo'], values=dados['g-10'], aggfunc='min')"
      ],
      "execution_count": null,
      "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>tempo</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>D1</th>\n",
              "      <td>-8.754</td>\n",
              "      <td>-9.394</td>\n",
              "      <td>-9.281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>-9.267</td>\n",
              "      <td>-8.367</td>\n",
              "      <td>-10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tempo     24     48      72\n",
              "dose                       \n",
              "D1    -8.754 -9.394  -9.281\n",
              "D2    -9.267 -8.367 -10.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "qoRpgnnOgKRu",
        "outputId": "0834f3b4-f2d4-4492-d0e8-3c6df5ad2bbb"
      },
      "source": [
        "#Usando melt()\n",
        "pd.melt(frame=dados, id_vars='g-0', value_vars=['tempo', 'dose'])"
      ],
      "execution_count": null,
      "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>g-0</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0620</td>\n",
              "      <td>tempo</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0743</td>\n",
              "      <td>tempo</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.6280</td>\n",
              "      <td>tempo</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.5138</td>\n",
              "      <td>tempo</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.3254</td>\n",
              "      <td>tempo</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47623</th>\n",
              "      <td>0.1394</td>\n",
              "      <td>dose</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47624</th>\n",
              "      <td>-1.3260</td>\n",
              "      <td>dose</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47625</th>\n",
              "      <td>0.3942</td>\n",
              "      <td>dose</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47626</th>\n",
              "      <td>0.6660</td>\n",
              "      <td>dose</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47627</th>\n",
              "      <td>-0.8598</td>\n",
              "      <td>dose</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>47628 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          g-0 variable value\n",
              "0      1.0620    tempo    24\n",
              "1      0.0743    tempo    72\n",
              "2      0.6280    tempo    48\n",
              "3     -0.5138    tempo    48\n",
              "4     -0.3254    tempo    72\n",
              "...       ...      ...   ...\n",
              "47623  0.1394     dose    D2\n",
              "47624 -1.3260     dose    D2\n",
              "47625  0.3942     dose    D2\n",
              "47626  0.6660     dose    D1\n",
              "47627 -0.8598     dose    D1\n",
              "\n",
              "[47628 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "5Tx3BELIiPZ4",
        "outputId": "9e5bb1b0-47d0-4e9c-f96f-0b00d50991e6"
      },
      "source": [
        "sns.scatterplot(x='g-0', y='g-8', data=dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f70513678d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "VsHbP7cKiGeB",
        "outputId": "d2929a3d-b609-465d-9055-7667427c0b4b"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "#Correlação G\n",
        "corr_g = dados.loc[:, 'g-0':'g-40'].corr()\n",
        "\n",
        "mask = np.triu(np.ones_like(corr_g, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_g, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabddd4ead0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "RgBd_Wr-nYm2",
        "outputId": "2c00398b-51a9-4f72-a178-cb2b19295924"
      },
      "source": [
        "#Correlação C\n",
        "corr_c = dados.loc[:, 'c-0':'c-40'].corr()\n",
        "\n",
        "mask = np.triu(np.ones_like(corr_c, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_c, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabc6729e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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msGHDNGzYsMpOBQAA4NXIygQAAHATHpOVuWNgT0N1HedvlCRtH9DNUH142gfa9tgfDffRafHH2vW/VsP1Hd5Za3r87U9GGq4PX/ihtj/exXh9+lbD2ZeSLf/S6Pjh6VsliWxNB9a7S96er9W747Hg6fXusm99rZ6sTM/jsEimefPmqUePHrrtttv00UcflXrv9OnTevrpp9WjRw9FR0dr7969jpoWAADAazjsjFlYWJi6deum8ePHl3nv73//u9q1a6d3333XHsm0adMmIpkAAECF5TzayVR9xPJtTurEcQwtzHJzc5WUlKSCggJJ0pgxYxQREVGqpm3bttf8+xs3btSHH34oyRbJFBAQoH379l337wAAAPiachdmRmKXrufs2bMqKSlRcHCw/bWQkBAdP36chRkAAMAVyr3HzGjsEgAAACqnQjf/Z2dny2q1ymq1KjU19bq1tWvXliTl5+fbX8vLy9NNN91UkakBAAC8VrmXMo3GLl1Pz549tXTpUg0dOlS7d+/WL7/8ojZt2lSqcQAAAG9T7sLMaOxSamqqFi5cqPz8fCUkJKhatWrKyspSzZo19Ze//EWjR4/WmjVrVK1aNSUlJclicdiTOgAAgC+yeN/THQx9K9NI7NKgQYM0aNCg332vXr16WrBggenmAAAAfAmRTAAAwCPl9I8ov+gKEUtznNSJ43A9EQAAwE14TFam2fxFM/mOObH3G+4j4r1s01mTZsc3m31pOuvzuRjD9R3mrDGdlWk0S7TDO7YvkJgd390y4sgX9L56dzwWPL3eXfatr9WTlel5XJKVeb33AAAAYOOwhVlYWJhSUlIUFhZm6j0AAIAK8fMz9+MBXJKVSfQSAABA+ZyelQkAAOBpjhw5ooSEBJ07d05BQUFKTExUs2bNStWcOnVKEydO1NGjR3Xp0iU9++yzslpt91kXFRXpr3/9q7Kzs+Xn56chQ4aob9++5c5LViYAAMBVJk2apLi4OG3atElxcXGaOHFimZq//e1vatOmjTIyMrR48WK98cYbysvLkyRlZGTou+++0+bNm7Vs2TLNnDlTR48eLXdep2dlAgAAeJIzZ87owIED6t3b9hSG3r1768CBA6VyvyXp4MGDuv9+25MXgoODddttt2nDhg2SpKysLPXt21cWi0XBwcHq2rWrNm7cWO7cLsnKBAAA+G87f/68zp8/X+b1wMBABQYG2n/Py8tTgwYN5O/vL8l2tbB+/frKy8tTcHCwve6OO+5QVlaW7rzzTh09elS5ublq1KiRfYyGDRvaa0NCQnT8+PFye3RJVub13gMAAKgIPz9zF/7S0tI0a9asMq8PGzZM8fHxpudPSEjQ1KlTZbVa1bBhQ3Xs2NG+mKsol2RlXu89AAAAVxgwYIAeeuihMq9febZMsp3dOnHihIqKiuTv76+ioiKdPHlSISEhpeqCg4P1+uuv238fPHiwbr31VvsYx44dsz+Z4uozaNdCViYAAPBI2+IeNFXfack/DNc+8cQTeuSRR2S1WrV27VqtXLlSixYtKlVz9uxZ1apVS1WqVNGOHTuUkJCgjRs3qnr16lq9erXWr1+vlJQUnTt3TjExMVq8eLEaN2583Xk9JpIJAADAVSZPnqyEhATNnj1bgYGBSkxMlGQ7K/bCCy/ozjvv1L/+9S+9+uqrslgsql27tt5++21Vr15dkmS1WrV37151795dkvT888+XuyiTPOiMmdF8xw5z1kiSdjzV3VB9xwWbTWdTfjr0YcP17Wev1vYB3YyPn/aBdgzsabi+4/yNpsc33f+TkcbGXvihJPNZmWb2lSTT/bhbphz5gu5f747HgqfXu8u+9bV6b8/KdOYZs/8Wp2dlFhcXKz4+Xj169FB0dLQGDhyo7777zlHTAgAAeA2XZGXGxMRow4YNWrdunSIjIzVhwgRHTQsAAHyVF2ZlGlqY5ebmKjY2VtHR0YqOjlZOTk6ZmrZt26pJkyZlJ7BYFBkZKYvFNlVoaKiOHTtWybYBAAC8j8uzMhcvXqwuXYzf0wUAAOAryl2YOTIrMyUlRYcPH1ZaWlqF/j4AAIA3c1lW5qJFi5SZmam5c+fav0oKAACA/3BJVubSpUu1fPlypaWlKSgoqFINAwAASOYjmTyB07MyJdtD2ho2bKiBAwdKkgICArRixQonbA4AAIDncklW5sGDB813BgAA4GM85sn/AAAAVzKT3CPZ0nvcnfddnAUAAPBQHhNivnNQL0N196Xa7mszuooOT9+qnP4RhvuIWJqjXUOiDNd3mJthenyjWZCSLQ8yJ/Z+4+O/l204y1Ky5Vlue+yPhmo7Lf5YkrRzcG9D9felZEqS4c8nYqntwcZGs9EuZ6J5cramu+Tt+Vq9O2ZNenq9u+xbX6v39qxMb+T0rExJmjJliqKiomS1WtWnTx/t2LHDUdMCAABfZfEz9+MBHHbGLCwsTN26ddP48ePLvDdy5EjVqmVbVR88eFADBgzQzp075echuVUAAACuYGhhlpubq6SkJBUUFEiSxowZo4iI0pef2rZte82/f3lRJkkXLlxgQQYAAPA7XJaVOWPGDGVkZOj8+fOaOXMmizMAAICrlHuPmaOyMocPH64tW7ZoxowZmjZtmgoLC813CwAA4MVclpV5WceOHXXx4kUdOnSoIlMDAAB4LadnZZaUlOibb75R8+bNJUn79u1Tfn6+GjduXLnOAQCAbyMr03xWZo0aNTRx4kT9+OOP8vf31x/+8AdNnz69QpdDAQAAvJlLsjIXL15svjMAAAAfQ1YmAADwSNsHdDNVH572gZM6cRzvuzgLAADgoTwmK9NoPmWHuRm2eoN5kB3eWWs4h1OyZXF+OuwRw/XtZ600Pb7ZLE7T/Q992HB9+9mrTWdf7h4Ra6i+3fT3JEk7BvY0VN9x/kZJ5nNTdz0XY6i+w5w1tn6e+ZOxfuZtkES+oDfWu2PWpKfXu8u+9bV6sjI9j0uyMi97//331apVq2u+DwAAYJSfn5+pH0/gsIVZWFiYUlJSFBYW9rvvHz9+XMuWLVNoaKijpgQAAPAqhhZmubm5io2NVXR0tKKjo5WTk1Ompm3btmrSpMk1x5gwYYLGjRungICAincLAADgxVySlblkyRLdeuutuuuuuyrcKAAAgLcrd2FW2azM77//XitXrtSSJUsq3iUAAIAPcHpW5p49e3TixAn16tVLXbp00Z49ezR+/HitXLmyQg0DAABIskUymfnxAE7PyoyKilJU1H8e//DEE0/o6aefVufOnSveNQAAgBMdOXJECQkJOnfunIKCgpSYmKhmzZqVqjlz5ozGjRunvLw8Xbp0SR06dNBLL72kKlWqaObMmVqyZInq168vyZaiNGnSpHLnLXf5eGVWZlRUlB5++GHt37+/TF1qaqoeeOAB5ebmKiEhQQ888IDpe9EAAADcwaRJkxQXF6dNmzYpLi5OEydOLFPz9ttvq3nz5srIyNC6deu0f/9+bd682f5+TEyM1q5dq7Vr1xpalEkuysq80qJFiww1BgAA8N9w5swZHThwQPPnz5ck9e7dW6+88ory8/MVHBxsr/Pz81NBQYGKi4tVWFio3377TQ0aNKjU3GRlAgAAj2Q0OeayO2Ys1/nz58u8HhgYqMDAQPvvX3zxhcaOHav169fbX+vVq5emTZumO+64w/7auXPnFB8fr8OHD+vnn3/WY489plGjRkmSZs6cqRUrVujGG29UvXr1FB8fr7vvvrvcHj0mkgkAAKAy0tLSNGvWrDKvDxs2TPHx8abH27hxo1q1aqW0tDQVFBRo8ODB2rhxo3r27Kn+/fvr2WefVdWqVbVt2zYNHTpUWVlZql279nXH9JiFmdm8xh1PdTdU33HBZm1/vIvhPsLTtxrO4ZRsWZxmx98+oJvx+rQPtP3JSOP1Cz8037/B8cMXfijJfDbltsf+aKi+0+KPJcnw5xmevtVWb7J/s/2YPTbJF3T/enfMmvT0enfZt75W7/VZmRZzMUsDBgzQQw89VOb1K8+WSVJISIhOnDihoqIi+fv7q6ioSCdPnlRISEipuvT0dE2dOlUWi0W1atVSly5dtGvXLvXs2VP16tWz13Xq1EkhISH6+uuv1b59++tvkqktuo7rZWU+8cQTioyMtD9iY9WqVY6aFgAAwJDAwEA1atSozM/VC7M6deqodevWysy0/Q91ZmamWrduXer+Mklq1KiRPvnkE0lSYWGhduzYoRYtWkiSTpw4Ya/78ssv9cMPP+jmm28ut0eHnTELCwtTt27dNH78+N99/6WXXuIRGQAAwCNMnjxZCQkJmj17tgIDA5WYmChJGjx4sF544QXdeeedevHFFzVp0iRFRUWpqKhIHTp00KOPPipJSk5O1v79+2WxWFS1alUlJSWVOot2LYYWZrm5uUpKSlJBQYEkacyYMYqIiChV07ZtW1MbDAAA4K6aN2+uFStWlHk9JSXF/ucmTZrYv7l5tcsLObNckpUpSUlJSUpOTlarVq00evToSn+dFAAAwNuUe49ZZbMyJduibMOGDVqzZo1uueUWjRgxomLdAgAAeDGnZ2VKsn+Lwd/fX08++aT27t2r4uLiikwNAAAgyfaAVzM/nsDpWZmXLl3SuXPnVLduXUnS+vXr1bJlS1ksnhEmCgAA4CrlLsyuzMr86aefZLFYNHbsWIWHh5eqS01N1cKFC5Wfn6+EhARVq1ZNWVlZslgsGjJkiH777TdJUv369ZWcnOycrQEAAPBgLsnKXL16tfnOAAAAfAxZmQAAwCPtHNTLVP19qVlO6sRxuNELAADATXhMVuanwx4xVNd+1kpJ0mfD+xmqD5uxTJ8OfdhwH+1nr9buP8cZrm/3xhJ99sKjhuvD3lxuuHfJ1v/uEbHG+5n+nnaPfNx4fXK66c8+98Xfv6R9tbun2r7Ra/TzCXtzeYXqTR87ZvsxcaxJ0qfP9zHWz1ur3CZvz9fq3TFr0tPr3WXf+lq912dl+nnf+SWXZGUWFxdr+vTp6tGjh6KiojRkyBBHTQsAAOA1XJKVmZaWpiNHjigzM1NVq1bV6dOnHTUtAACA13BJVua7776rJUuWqGrVqpJkf6YZAAAA/sPpWZkXLlzQuXPntGHDBn3wwQeyWCwaPHiwunbtWqnGAQAAvE25C7PKZmUWFRWpsLBQxcXFWrFihb799lvFxcWpZcuWatKkScU7BwAAPs1TYpbMcHpWZlBQkGrUqKHo6GhJUtOmTXX77bfrwIEDFZkaAADAazk9K1OSevfurezsbPXr109nzpzRwYMH1aJFi0o1DgAA4G2cnpVZs2ZN/fnPf9aLL76oRYsWyc/PTyNHjlTz5s2dtlEAAACeyCVZmcHBwXr77bfNdwcAAOBDyMoEAAAeadf/Wk3Vd3jH+G1Y/y3el2UAAADgoTwmK3Pn4N6G6u5LyZQk7XjmT4bqO87boO2PdzHcR3j6VtPZmmbHN9q7VLH+zfwfRod31mrHU92N9bJgsyRp13Mxxsaes0aStP3JSEP14Qs/tNUP6GasPu2DCtVve+yPhuo7Lf5YkrRzUC9D9felZtn6MbG9Zo81d8nn8/R6d8ya9PR6d9m3vlbv9VmZXshhC7N58+Zp+fLl+vbbbzVnzhx17txZklRYWKi+ffva63755Rd9//332r59u4KCghw1PQAAgMdzelZmQEBAqUdrLFiwQDt27GBRBgAAcBWXZGVeafXq1Ro2bJjJNgEAALyf07Myr7Rv3z6dOnXKfpkTAACgwiw+GMlU2azMK61atUrR0dGqWrVqhf4+AACAN3N6VuZlv/76q9avX68+ffpUZEoAAACv55KsTEnavHmzmjZtqpYtW1a4WQAAAG9W7hmzK7Myo6Ki9PDDD2v//v1l6lJTU/XAAw8oNzdXCQkJeuCBB0rdi7Z69WrOlgEAAFyHS7IyJWn+/PnmOgMAAPAxZGUCAACPZCYdRbIlpLg7j4lk+vR5Y5dB27+1SpJMxQg5O5JpW9yDhus7LfmH4QghyRYjZHb83SNiDde3m/6e6Ugjs/FZpiOWDO6v8PStkszFc0nmI5mMHg+X/4Ng5tj857hnDNVK0j2vzTMcDyXZIqLcJTbG3erdMdLI0+vdZd/6Wj2RTBV35MgRJSQk6P+Vtm0AACAASURBVNy5cwoKClJiYqKaNWtWqubMmTMaN26c8vLydOnSJXXo0EEvvfSSqlSpoqKiIv31r39Vdna2/Pz8NGTIkFJJSNdCiDkAAMBVJk2apLi4OG3atElxcXGaOHFimZq3335bzZs3V0ZGhtatW6f9+/dr82ZbbnRGRoa+++47bd68WcuWLdPMmTN19OjRcud12MJs3rx56tGjh2677TZ99NFHpd7bs2eP+vbtK6vVqv/5n//Re++956hpAQAADDl//ryOHj1a5uf8+fOl6s6cOaMDBw6od2/bFaDevXvrwIEDys/PL1Xn5+engoICFRcXq7CwUL/99psaNGggScrKylLfvn1lsVgUHBysrl27auPGjeX26PSsTMm26hwxYoQ6d+6sU6dOqXv37urWrZvq1q3rqOkBAACuKy0tTbNmzSrz+rBhwxQfH2//PS8vTw0aNJC/v78k28P169evr7y8PAUHB9vrhg4dqvj4eEVEROjnn3/WY489pnvvvdc+RsOGDe21ISEhOn78eLk9uiQr08/PTxcu2K5XFxQU6IYbblD16tWNTA0AAPD7/MxFMg0YMEAPPfRQmdcDAwMrNP3GjRvVqlUrpaWlqaCgQIMHD9bGjRvVs2fPCo0nuSgr87XXXtPQoUOVnJysH3/8UdOmTdMNN9xQ4aYBAADMCgwMNLQICwkJ0YkTJ1RUVCR/f38VFRXp5MmTCgkJKVWXnp6uqVOnymKxqFatWurSpYt27dqlnj17KiQkRMeOHbOfuLr6DNq1uCQrMzU1VaNHj9bHH3+sVatW6eWXX9axY8dMjQEAAOAKderUUevWrZWZaXt6QGZmplq3bl3qMqYkNWrUSJ988okkqbCwUDt27FCLFi0kST179tSKFStUXFys/Px8bdmyRT169Ch3bqdnZV5uplcv29f4b7nlFrVs2VJ79+6tyNQAAABON3nyZKWnp6tHjx5KT0/XlClTJEmDBw/Wvn37JEkvvviiPv/8c0VFRSkmJkbNmjXTo48+KkmyWq1q1KiRunfvrkcffVTPP/+8GjduXO68Ts/KvPHGGxUQEKDPPvtMYWFhOnXqlA4ePKhbb73V0N8HAABwtebNm2vFihVlXk9JSbH/uUmTJtdMNvL397cv5swod2F2ZVbmTz/9JIvForFjxyo8PLxUXWpqqhYuXKj8/HwlJCSoWrVqysrKUs2aNfXGG29o6tSpKioqUnFxseLj4+2n+gAAAGDjkqzMiIiIMt/iBAAAqBST38r0BGRlAgAAj/TpsEdM1beftdJJnTiO12Zl7vpfq6H6Du+s1a4hUYb76DA3Q5+98Kjh+rA3l2vXczHGx5+zxnDvkq1/s9mdZvs3m325e+TjhurbJadLMp81aXR/dZibYas3cSxUpN7o5xn25nLT45vNNXX2seAu+X/OrnfHrElPr3eXfetr9WRleh6yMgEAANyEy7Iy+/Xrp6ioKPXp00f79+931LQAAABew2ELs7CwMKWkpCgsLKzU6yUlJYqPj9eoUaOUkZGhcePGafTo0eLWNgAAgNKcnpV59uxZXbhwwb5ga9eunY4fP679+/erTZs2lekdAAD4MD+L992R5fSszODgYNWuXVtbtmxR165dtXXrVhUUFOjYsWMszAAAAK7gkqzMWbNmKT09XQ899JA++eQTtWjRQv7+/hXrGAAAwEtV6HEZ2dnZev311yVJUVFR13yw7GV33HGHFixYIMkW8tmpUycimQAAAK7i9KxMSTp16pTq1asnSZo7d67CwsLUtGnTincNAADghVySlbls2TJlZGSouLhYbdq00dSpU522QQAAwEd4YSSTS7Iyhw0bpmHDhpnvDgAAwIeQlQkAADzSZ8P7maoPm3H9k0zuwGOyMs3mF+4c1MtQ/X2pWdoxsKfhPjrO32gqNLX9rJWGsyYlW96k0d6lCvZvMk9xxzN/Mjb2vA2SjIfKXg6T3fFUd2PjL9hsqzfZj9HPp+P8jRXqx+yxaaYfs/tq+4BuhuvD0z4wPb7Z3Fd3yQs0W++OWZOeXu8u+9bX6snK9Dze92Q2AAAAD+WQM2bFxcUaPny4Dh06pGrVqqlOnTqaMmWKmjRpIkk6cuSIEhISdO7cOQUFBSkxMVHNmjVzxNQAAABew2FnzGJiYrRhwwatW7dOkZGRmjBhgv29SZMmKS4uTps2bVJcXJwmTpzoqGkBAICP8vOzmPrxBIa6zM3NVWxsrKKjoxUdHa2cnJzSg1gsioyMlOXfmVWhoaE6duyYJOnMmTM6cOCAeve23WfVu3dvHThwQPn5+Y7cDgAAAI/nlKzMxYsXq0uXLpKkvLw8NWjQwB7B5O/vr/r16ysvL0/BwcEO2AQAAADvUO7CzGxWZkpKig4fPqy0tDTHdQkAAOADKnTBNTs7W1arVVarVampqfbXFy1apMzMTM2dO1fVq1eXJIWEhOjEiRMqKiqSJBUVFenkyZMKCQlxQPsAAADew2FZmUuXLtXy5cuVlpamoKAg++t16tRR69atlZmZKavVqszMTLVu3ZrLmAAAAFdxSFbmxYsXNXnyZDVs2FADBw6UJAUEBGjFihWSpMmTJyshIUGzZ89WYGCgEhMTnbQ5AADAZ5CV+ftq1qypgwcPXvP95s2b2xdpAAAA+H1kZQIAAI+0+89xpurbvbHESZ04judkZQ6JMlTXYW6GJHN5hNsf72K4j/D0rYazESVbPqLZ8c3mHW5/MtJ4/cIPTWd9Gu0nPO0DSeazI82Ob3R7wxd+KMl8tua2x/5oqL7T4o8lyXB+ZIc5aySZ699MQG/YjGWGcz4lW9ans7M1zebE+lq+oC/Vu8u+9bV6sjI9j2c8BhcAAMAHOGRhVlxcrPj4ePXo0UPR0dEaOHCgvvvuO/v7iYmJ6tKli1q1aqVDhw45YkoAAACv47BLmTExMercubMsFovS09M1YcIE+0NmIyMj9eSTT+qxxx5z1HQAAMDXOTH/8siRI0pISNC5c+cUFBSkxMRENWvWrFTNmDFj9NVXX9l//+qrr/TWW28pMjJSM2fO1JIlS1S/fn1Jti9STpo0qdx5DS3McnNzlZSUpIKCAnsjERER9vcvZ2VeFhoaWurJ/+3atTMyDQAAgFuYNGmS4uLiZLVatXbtWk2cOFELFy4sVZOUlGT/88GDBzVgwADdf//99tdiYmI0duxYU/M6PSsTAADAk5w5c0YHDhzQ/PnzJUm9e/fWK6+8ovz8/Gs+IH/lypWKiopSQEBApeYmKxMAAPiE8+fP6/z582VeDwwMVGBgoP33vLw8NWjQQP7+/pJsa5/69esrLy/vdxdmhYWFysjI0IIFC0q9vn79euXk5KhevXqKj4/X3XffXW6PFbrHLDs7W6+//rokKSoqSoMGDZL0n6zMtLQ0e1YmAACAO0hLS9OsWbPKvD5s2DDFx8dXeNwtW7aoYcOGat26tf21/v3769lnn1XVqlW1bds2DR06VFlZWapdu/Z1x3J6ViYAAIAz+FnMRTINGDBADz30UJnXrzxbJkkhISE6ceKEioqK5O/vr6KiIp08eVIhISG/O+6qVavUp0+fUq/Vq1fP/udOnTopJCREX3/9tdq3b3/dHl2SlfnXv/5Vmzdv1unTpzVw4EAFBQVp/fr15U0NAADgMFdfsryWOnXqqHXr1srMzJTValVmZqZat279u5cxjx8/rs8//1zJycmlXj9x4oQaNGggSfryyy/1ww8/6Oabby53bpdkZb700kt66aWXjEwFAADwXzd58mQlJCRo9uzZCgwMVGJioiRp8ODBeuGFF3TnnXdKkt5//3117ty5zP33ycnJ2r9/vywWi6pWraqkpKRSZ9GuhaxMAADgkT4f9YSp+ntfX+SkThzHY7Iy/znuGUN197w2T5K0e+TjhurbJaebCkFt98YSUwfCva8v0u4RscbHn/6ePn2+T/mF/9b+rVWGt1Wybe+eCc8Zrg99ZY7hvMawGbazqv8cO9BQ/T2Jtq8hmx3f6P66HFZrth+z4+e+9L+G6u/+6zumx/9iWoKhWklqM/pvhnu53I/Z3Fejn6Vk+zzN9mP236Kn5wv6Ur27ZEf6Wj1ZmZ6HrEwAAAA34ZAzZsXFxRo+fLgOHTqkatWqqU6dOpoyZYqaNGmis2fPasyYMfruu+8UEBCgpk2b6uWXX77mA9oAAAAM8TP3rUxP4LAzZjExMdqwYYPWrVunyMhITZgwQZLk5+enQYMGadOmTcrIyFDjxo3tz0ADAADAfxhamOXm5io2NlbR0dGKjo5WTk5O6UH+nZVpsdiGCw0N1bFjxyTZHrfRoUMHe+2V7wEAAOA/XJqVWVxcrPfee48cTQAAgN/h0qzMV155RTVq1NDjjxv/FiEAAICvqNA9ZtnZ2bJarbJarUpNTbW/fjkrc+7cuWWyMhMTE/Xtt99q+vTp9kueAAAA+A+XZGUmJyfriy++0Ny5cxUQEODYLQAAAL7Jz/tO9Dg9K/Prr7/WO++8o2bNmql///6SpEaNGumtt95y0iYBAAB4JqdnZbZo0UJfffVVxboDAADwIWRlAgAAj/T5mKdM1d+btMApfTiSx2Rlfjr0YUN17WevliTtei7GUH2HOWu0c3Bvw33cl5KpT4c9Yri+/ayV2jGwp+H6jvM3mu5n15Aow/Ud5mYYzqaUbPmUOwf1MtZLapYk89mXZsc3ur0d5mbY6k0cCxUa32S90f17X0qm6RxU08eOwc9Gsn0+Zo8do/9uJdu/XbPHstncWnfLF/SlenfJjvS1erIyPY/33TUHAADgoZyelSlJQ4cO1dGjR2WxWFSjRg1NmDBBrVu3dsTUAADAR/l5YVamwy5lxsTEqHPnzrJYLEpPT9eECRPsD5lNTExUrVq2051btmzRiy++qPfff99RUwMAAHgFp2dlSrIvyiTbozW8cYULAABQWS7Lyhw/fry2bdumkpKSUmkBAAAAsHFZVuarr74qSVqzZo2SkpKUkpJSmb4BAICv88KIR5dlZV4WExOjXbt26ezZsxXrGAAAwEs5PSuzoKBA58+fV0hIiCRp69atuvHGG8vkaQIAAPg6p2dl/vzzzxo+fLh+/vlnWSwW3XjjjXr77bf5AgAAAMBVnJ6VWbduXS1fvrxi3QEAAPgQsjIBAIBH+ue4Z0zV3/PaPCd14jgek5VpNk9x+5ORhurDF36obY/90XAfnRZ/bDqP0Ggvl/txdv2nz/cxXN/+rVXa/niX8gslhadvlWQ+O9Ls+KbrB3QzVp/2gSQZPh46Lf5YUgW218Sx+dkLjxqqlaSwN5drx1PdDdd3XLDZdL3ZY3/HM38yPv68DeaPZZO5tWZzU90tj9CT690lO9LX6r09K9Mbb4vyvu+ZAgAAeCiHLMyKi4sVHx+vHj16KDo6WgMHDtR3331Xpm7WrFlq1aqVDh065IhpAQAAvIrDzpjFxMRow4YNWrdunSIjIzVhwoRS7+/fv1979uzR//t//89RUwIAAHgVl2RlFhYW6uWXX9bkyZMd1zkAAICXcUlW5owZMxQdHa1GjRpVvmMAAAAv5fSszNzcXH3xxRcaNWqUg1oGAACQ5Od932F0elbmZ599psOHDysyMlJdunTR8ePH9cwzz5S5HAoAAOAujhw5on79+qlHjx7q16+f/u///q9MzZgxY+zrIavVqttuu00ffvihJKmoqEhTpkxR165d1a1bN61YscLQvE7PyhwyZIiGDBli/71Lly56++231bJlS0MNAgAAuNqkSZMUFxcnq9WqtWvXauLEiVq4cGGpmqSkJPufDx48qAEDBuj++++XJGVkZOi7777T5s2bde7cOcXExKhjx47l3tZV7hmzK7Myo6Ki9PDDD2v//v2lai5nZRYUFGjgwIGyWq3q27ev4Y0HAABwtvPnz+vo0aNlfs6fP1+q7syZMzpw4IB697Y9VLt37946cOCA8vPzrzn2ypUrFRUVpYCAAElSVlaW+vbtK4vFouDgYHXt2lUbN24st0enZ2VebevWrYbqAAAAHCktLU2zZs0q8/qwYcMUHx9v/z0vL08NGjSQv7+/JNv99fXr11deXp6Cg4PL/P3CwkJlZGRowYIFpcZo2LCh/feQkBAdP3683B7JygQAAB4pd/yQ8ouu0Hzs62XOjklSYGCgAgMD7b9/8cUXGjt2rNavX29/rVevXpo2bZruuOOOMn8/KytLKSkpev/99+2vRUVF6dVXX1Xbtm0l2b4ceeLECb300kvX7dFrszKNZvrdl5Lp9LxA09mXBrMdJVu+o9HPRrJ9PmbzBc1kO0rSp0MfNjb27NWSzGVHStKOgT0N1Xecbztl7MycVUna9b9WQ/Ud3rHdl2n0eOu4YLN2j4g1VCtJ7aa/Z/pYMHvsG923km3/Gv1sJNvnY7ofk7mvZo8FM/tKcq9sSnerd5fsSF+r9/asTJnMyrx6AXYtISEhOnHihIqKiuTv76+ioiKdPHlSISEhv1u/atUq9enTp8wYx44dsy/Mrj6Ddi0uiWTq0qWLevbsaf/WQnZ2tiOmBQAAcLg6deqodevWyszMlCRlZmaqdevWv3sZ8/jx4/r8888VFRVV6vWePXtqxYoVKi4uVn5+vrZs2aIePXqUO7fDzpjFxMSoc+fOslgsSk9P14QJE+zPMpOkN998k29iAgAAjzB58mQlJCRo9uzZCgwMVGJioiRp8ODBeuGFF3TnnXdKkt5//3117ty5zDNerVar9u7dq+7dbWfen3/+eTVu3LjceQ0tzHJzc5WUlKSCggJJtud2RERE2N+/HMl0WWhoaKlFGQAAgCdp3rz57z57LCUlpdTvzz333O/+fX9/f02ZMsX0vC6JZJKkUaNGqaSkRPfee69Gjhxp6BovAACALyn3HrOKRjKNGDHC/trixYu1bt06rVq1SiUlJXr55Zcd0DoAAPBlfn4WUz+ewOmRTJLs32IICAhQXFyc/vnPf1aybQAAAO/j9Eimn376SUVFRapVq5ZKSkqUlZWl1q1bO35LAAAAPFy5C7MrI5l++uknWSwWjR07VuHh4faay5FMDRs21MCBAyXZzo6tWLFCZ86cUXx8vIqKilRcXKzmzZtr0qRJztsiAAAAD+X0SKbGjRtrzZo1FesOAADAhxDJBAAAPNKeCb//qIprCX1ljpM6cRyPiWQCAAAoxWIukskTeMzCzGwe4bbH/miovtPij7Ut7kHDfXRa8g/tei7GcH2HOWtMj7/98S7lF/5bePpW0+ObzRc02k94+lZJ0o5n/mSovuO8DZJkuP9OS/5hqzexbyUZzh4NT/tAkpTzaCdD9RHLt0kyf2yayeLcPfJxQ7WS1C453XTOqrOPHbPZnWaP/c+G9zNcHzZjmVP3lWQ+x9Xd8g7JyvS+eq/PyvRCDlmYFRcXa/jw4Tp06JCqVaumOnXqaMqUKWrSpIkk6ddff9XUqVO1Y8cOVatWTaGhoXrllVccMTUAAIDXcElW5rRp01StWjVt2rRJfn5+On36tKOmBQAA8BqGHjCbm5ur2NhYRUdHKzo6Wjk5OaUH+XdWpsViGy40NFTHjh2TJBUUFGjNmjUaPny4/Pxs14Lr1q3ryG0AAADwCk7Pyvz+++8VFBSkWbNmadeuXbrhhhs0fPhwtWvXzjFbAAAA4CWcnpVZVFSk77//XrfffrtWr16tUaNGKT4+vtzFHQAAwHX5+Zn78QBOz8oMCQlRlSpV1Lt3b0nSXXfdpdq1a+vIkSMOaB8AAMB7OD0rMzg4WB06dNC2bdsUERGhI0eO6MyZM2ratKnjtwYAAMCDOT0rU5KmTJmiF198UYmJiapSpYqSkpIUGBjopE0CAADwTE7PypRseZmLFi0y3x0AAIAPISsTAAB4pL1T4k3V3zVpppM6cZwK3fwPAAAAx/OYrEyjmXv3pWZJMpdZZzafz2jenmTL3DObX2i0d+nf/Zsc33T/JvMCjWaJdpizRpJMZ3Gazi98qruh+o4LNksyn8Vp9tg00/+uIVGGaiWpw9wMw2NXdHzT/Zg8Ns3W5744yHD93VNTDfffYW6GJPM5q2ZzYs0em+6Wj0hWpvvXk5XpeZyelXn06FE9//zz9toLFy7o4sWL+vTTTx0xNQAAgNdwelZmo0aNSj1a49VXX1VRUZGjpgUAAPAaTs/KvFJhYaEyMjLUp08fB7QOAADgXZyelXmlrVu3qkGDBrrjjjsq3jEAAIDkMTFLZpS7MKtoVmZaWlqZ91atWsXZMgAAgGtwelbmZSdOnNBnn32mqCjj3+oCAADwJU7Pyrzs/fff14MPPqjatWs7rnsAAAAv4pKsTMm2MBs/frwTNgEAAMA7uCQrU5I2bdpkrjMAAAAfQ1YmAADwSP96ZYSp+rYTphuuPXLkiBISEnTu3DkFBQUpMTFRzZo1K1OXlZWlOXPmqKSkRH5+fpo/f77q1q2rmTNnasmSJapfv74k20muSZMmlTuvx0QyAQAAuMqkSZMUFxcnq9WqtWvXauLEiVq4cGGpmn379mnWrFlKS0tTvXr1dOHCBQUEBNjfj4mJ0dixY03N6zELM7MZd2Yy6EznCzopa/Ly+EZ7l/7dv8msz89eeNRwfdiby03nBZreVyZyTaUKZGWazC80vb0Gj4cO79i+MGNmez8b3s9QrSSFzVhmPmfV5LH56dCHDde3n71aOwf3Nlx/X0qm6f53j4g1XN9u+nvmc1xNHgtGt/e+lExJ5rMyzeayulOeortkR/paPVmZFXPmzBkdOHBA8+fPlyT17t1br7zyivLz8xUcHGyvW7BggZ5++mnVq1dPklSrVuU/D6dnZUrSRx99pBkzZqikpEQlJSUaNmyYunc3vvgAAACorPPnz+v8+fNlXg8MDFRgYKD997y8PDVo0ED+/v6SbM9wrV+/vvLy8kotzA4fPqxGjRrpscce008//aRu3brpueeek9+/H3y7fv165eTkqF69eoqPj9fdd99dbo9Oz8osKSnRmDFjtHjxYrVs2VIHDx5UbGysunbtao9wAgAAcLa0tDTNmjWrzOvDhg1TfHy86fGKior01Vdfaf78+SosLNSgQYPUsGFDxcTEqH///nr22WdVtWpVbdu2TUOHDlVWVla5jw0ztDDLzc1VUlKSCgoKJEljxoxRRESE/f3LWZmXhYaGlnryv8Vi0YULttOiFy5cUP369VmUAQAAlxowYIAeeuihMq9febZMkkJCQnTixAkVFRXJ399fRUVFOnnypEJCQkrVNWzYUD179lRAQIACAgIUGRmpf/3rX4qJibFf3pSkTp06KSQkRF9//bXat29/3R6dnpXp5+en6dOna+jQoapRo4YKCgo0d+7c8qYFAAC4Pou5rMyrL1leS506ddS6dWtlZmbKarUqMzNTrVu3LnUZU7Lde/aPf/xDVqtVly5d0s6dO9WjRw9JtsSjBg0aSJK+/PJL/fDDD7r55pvLndvpWZmXLl3SO++8o9mzZ+vee+/V559/rhEjRmj9+vW64YYbym0QAADA1SZPnqyEhATNnj1bgYGBSkxMlCQNHjxYL7zwgu688079z//8j7744gv16tVLFotFEREReuSRRyRJycnJ2r9/vywWi6pWraqkpKRSZ9GupUL3mGVnZ+v111+XJEVFRWnQoEGS/pOVmZaWZs/K/PLLL3Xy5Ende++9kqR7771X1atX1+HDh9W2bduKTA8AAOBUzZs3L5VgdFlKSor9zxaLRePGjdO4cePK1F1eyJnl9KzMm266ScePH9c333yjW265RYcPH9aZM2fs39gEAACAjdOzMuvVq6fJkydr+PDh9q+PTp069XeDzgEAAHyZS7Iyo6OjFR0dbb47AACAa/Hzvic8kJUJAAA80r+m/sVUfdsX/+6kThzH+5aaAAAAHspzsjJNZtx9OuwRQ/XtZ600nO0o2fIdjY5tH99g75Ktf7N5hGb7MZu/aPaz/3zMU4bq701aIMn8vnV6vcnsS6P7q/3s1abHN5trajabcvef4wzXt3tjieljzfSxbLJ+z+TnDdeHTn7L8OcZ9uZySRX4787zfQzVt39rlSQZ/rcYNsN2K4nZY9Ps9pKV6X31ZGV6HpdkZX788ceaMWOGLl26pBtvvFGvvfaaGjdu7IipAQAAvIbDLmXGxMRow4YNWrdunSIjIzVhwgRJ0o8//qixY8cqOTlZGRkZ6tu3ryZPnuyoaQEAALyGoYVZbm6uYmNj7d+uzMnJKT3Iv7MyL+dfhoaG6tixY5Kkb7/9VnXr1rXHEDz44IPKyclRfn6+I7cDAAD4GD8/P1M/nsDpWZk333yzTp8+rX/9619q27atMjIyJEl5eXllMqcAAAB8mdOzMmvVqqU33nhDr732mn799Vc98MADCgwMlL+/v4M2AQAAwDs4PStTksLDw+1JAadPn9a8efOIZAIAALiK07MyJenUqVOqV6+eiouLlZycrP79+6tGjRqO3RIAAAAP5/SsTEmaPn26/vnPf+q3335Tp06dNGrUKCdtDgAAgOdySVbmq6++ar4zAACA6/GQb1qaQVYmAADwSPv+NtpU/Z0J05zUieOQlQkAAOAmPCcr02RGnJn6HQN7Gu6j4/yNhvPwJFsmntnxzeYd7niqu/HxF2w2nUe4c1AvY72kZkkynxdotP+OCzZLkul+jH6e96VkuqQfo8dDx/kbtXtErKFaSWo3/T1tf7yL4frw9K2mc2Kdnd1p9LOUbJ/nP8c9Y7j+ntfmGd7eDnNtz1s0e+yYrjd77DzzJ0P1HedtkCTT22s295WsTPevJyvT8zhsYTZlyhTt3r1bFotFVapU0ahRo9SxY0dJtkdkjBkzRj/88IOqVaumV155RXfddZejpgYAAPAKDluYjRw5UrVq2VbOBw8e1IABA7Rz5075+fnp73//u9q1a6d3331Xu3fv1ujRo7Vp0yaPiUcAAABuyOJ9d2Q5JCtTkn1RJkkXLlwoLgIMGQAAIABJREFUtejauHGj+vfvL0lq166dAgICtG/fvsr2DgAA4FUcmpU5Y8YMZWRk6Pz585o5c6b8/Px09uxZlZSUlMrFDAkJ0fHjx9W2bVvHbQkAAICHK/eMmZmszOHDh2vLli2aMWOGpk2bpsLCQsd2CwAA4MUqdHE2OztbVqtVVqtVqampZd7v2LGjLl68qEOHDql27dqSpPz8fPv7eXl5uummmyrYMgAAgHdySFZmSUmJvvnmGzVv3lyStG/fPuXn56tx48aSpJ49e2rp0qUaOnSodu/erV9++UVt2rRx0iYBAABf4I1fInRIVmZJSYkmTpyoH3/8Uf7+/vrDH/6g6dOn2y95/uUvf9Ho0aO1Zs0aVatWTUlJSbJ44TcpAACA67QZ/bf/dgsO55CsTIvFosWLF1/z/Xr16mnBggWmmwMAAPAlZGUCAAC4Ca4nAgAAuAmPyco0ky8oSdse+6Oh+k6LP1ZO7P2G+4h4L9t0/p/Z8bc/GWm4Pnzhh9oW96Dh+k5L/mE6H9Ho+J2W/EOS+Xw+M/tKkul+jOZHhqdvlSTD+yvivWxJ5rM1zfRjNPNVsuW+ms3KdHaO6/YB3Yz3k/aB6frPRz9puP7eaQtNZ1OaPXbMZlmaHd/ofxvCF35oqzf4eYanfSDJfCaxmc/TXbIjfa2erEzP47AzZlOmTFFUVJSsVqv69OmjHTt22N+bN2+eevToodtuu00fffSRo6YEAADwKi7JygwLC1O3bt00fvx4R00HAADgdQwtzHJzc5WUlKSCggJJ0pgxYxQREVGq5npZmUQvAQAAlM/pWZkAAAAwhqxMAAAAN+H0rEwAAAAY45KsTAAAAJTPJVmZqampWrhwofLz85WQkKBq1aopKytLNWvWdN6WAQAAeBiXZGUOGjRIgwYNMt8dAACADyErEwAAwE2QlQkAAOAmPCYr02hG330pmbZ6ExluZvMCP32+j+H69m+tMtzL5X7M5hGazVPc9VyM8fo5a0znlO7+c5yh+nZvLJFkft+azSM0m49o+lhz4rFpdt+aPdY+G97PcH3YjGWmj02z/Zjd3j0TnjNcH/rKHPNZkCb37adDHzZU3372alu9wf+WtH9rlSTzx77Rf+sd5qyx9WO2fxP1ZnNQ3SVr0tPrycr0PA5bmE2ZMkW7d++WxWJRlSpVNGrUKHXs2FHFxcUaPny4Dh06pGrVqqlOnTqaMmWKmjRp4qipAQAAvILTszIlKSYmRp07d5bFYlF6eromTJigtLQ0R00NAADgFQzdY5abm6vY2FhFR0crOjpaOTk5ZWqulZVpsVgUGRkpi8U2VWhoqI4dO+aI3gEAALyKy7MyFy9erC5dulS+cwAAAC/j0qzMlJQUHT58WCNGjHBA6wAAAN7FZVmZixYtUmZmpubOnavq1atXvGMAAAAv5ZKszKVLl2r58uVKS0tTUFCQkzYFAADAszk9K/PixYuaPHmyGjZsqIEDB0qSAgICtGLFCudtFQAAgAdyelZmzZo1dfDgwYp1BwAA4EPIygQAAHATHhPJ9P/bu/OoKM68e+AXFGLUuI2j45iJZzIqGlyIsojgggsawiJxIahA4ojOm7iNy/xcxhHjMWrMMOPyZvISY4wSlMT1qDEqblExbhFc0BwXokbFgIggqCB8f3/0oQfsFuuRbqzC+zmHc7Tr+vRTXVVt0Utd1SoVrfUfXb/ciYPDemqeh89XezXXkACmKhLV8Q+94685771iBw4M8dGc9/36oHKtjspjCajX2Bwc2kNT3idhHwAgebi2y610jd9tykf21pZfuQsAcCC8m6a87+r9ANRrcrTuDz5f7cXRcUM0ZQHAY/HXmucOmOav9bEETI/nkTGDNOc9l65VruFRzf/4/97VnO+04AvlfdPu+77CvvA081GtU1OtiLLn86xqPZdeKpD0lmclk/GwxJyIiIhIJ+zelfmkZURERERkYveuTAcHhwqXEREREZGJ3bsyn7SMiIiIiEyqrCtTS48mERER0fOsyroyn9SjSURERPS8q7KuTC3LiIiIiJ5ndu/KfFKPJhERERGZ2L0rs6Sk5LHLiIiIiOi/7N6VWdEyIiIiIvovdmUSERER6YRhujLt2XGn2hd4+H8GaM57/Wej3fsCVfP2mn9pP59yr6lq96VqX6DG7lHvFTtM4yt2ax4eFaQp7xW32TS+wvoeHR+mKQsAHosSlXtWlfcdxf5C1fFV56/aJWrPzl1AvTfV3uP/MDJAU77Lsm8BQHMXqufStabxFZ6XVbetvfc1vXRZ2jvPrkzjYVcmERERkU7Y7MRs9uzZCAoKQkhICAYOHIhDhw5ZZDZs2AAXFxfs2bPHVndLREREVG1USVcmAGRkZCAxMRFubm62uksiIiKiaqVKujIBYObMmZg2bRqcnZ0rOWUiIiKi6qlKujITEhLQsmVLdOzY0bazJyIiIqpG7N6VefXqVaxduxbjx4+37cyJiIiIqhm7d2WmpKTg5s2bCAgIQK9evZCSkoIZM2Zg7dq1lZ48ERERUXVi967Mdu3aISjov9d5ioiIwIgRI+Dn52eH1SEiIiIyLrt3ZRIRERGRNnbvynzUqlWrtM2MiIiI6DnDrkwiIiIinai2XZkqHXGH3u2veR7eX3yHI++9pTnv+cl65fG19uEBpk48resKmNbXXvP3/uI7ANDc7+ixyPQqrOr4qn2Bql2Zyn2Eivumyvqq9poq78vvD9Sc9/zfdcr9har7stbeUcDUPXpixijN+dfnxtn1eQRQ74nVun29/rMRgPqxYu/5qIyv3JuquC+rPg+q7mt66b5UzbMr03jYlUlERESkEzZ7xWz27Nk4duwYHB0dUbNmTUyePBne3t4ATN/EvH79OurWrQsAiIyMxMCB2n9TJyIiInoeVFlX5t///ndeIoOIiIioAlXWlUlEREREFauSrkwA+OijjxAbGwsXFxdMmTIFTZs2td1aEBEREVUDdu/KBEwnZdu2bcPGjRvx6quvYsKECTZcBSIiIqLqwe5dmQDQrFkzAKaTusjISKSmpqKkpKQS0yYiIiKqfuzelfnw4UPk5OSgcePGAICtW7eidevWcHTklTqIiIiIyrJ7V2ZBQQFGjRqFoqIiAECTJk0QGxtrvzUiIiIiMii7d2XWrl0b69evf7rZERERET1H2JVJREREpBPG6cpU7KxLjuytKd915S4cHNZT8zx8vtqr3BeoOn7y8F6a813jd2vuggRMfZBHxgzSnPdculbzfLrG7wag3rentUOv65c7TXnF+aj2C9p7fVW6O49NCNeUBQD3f69W7gvUepwApmNFtbtTNa86H+WuT9V9U+F5BFDvWVXd11SfB5V7bscN0ZT3WPw1ALV9X/V5SnXfUd33Vbs4VefDrkx6WvwEPhEREZFOVElXZklJCRYvXoxt27bB2dkZzZo1Q1xcnK3umoiIiKhaqJKuzC+//BLp6enYsmULnJyckJWVZau7JSIiIqo2NJ2YnThxAh999BHy8/MBAH/729/g6+tbLlNRV+by5cuRkJAAJycnADBf04yIiIiI/svuXZl5eXnIycnBtm3bsHPnTjg6OiI6Ohp9+vSx+coQERERGdkTT8xUuzLHjx+PQ4cOYeHChUhISEBxcTEKCwtRUlKCb775BpcvX8bQoUPRunVrvPLKK7ZdGyIiIiIDs3tXZoMGDVC7dm0EBwcDAFq0aIHXXnsNaWlplZs5ERERUTVj965MAAgMDMT+/fsRFhaGW7du4dy5c2jVqpWdVomIiIjImOzelQkAf/3rXzF9+nSsWrUKDg4OmDhxovkkjoiIiIhM7N6VCQCNGjXCp59+qj47IiIioucIuzKJiIiIdMIwXZlHx4dpynksMr2yp9L5ptq3d3xyhOZ8549XKXdTqvYjquaPT4nUnO+8cKVyf96JGaM05V+fa2p/UB1fNa/18XH/9+qnG/+vQ7WN/68E5fFV9zWtYz/t+Kr7zrGJwzXn3WPjlfflk3MmaM53mPlvzevb+eNVANSfd1TH1/r4uMfGm/KK+7Lqvqk6f5W83Z8HNa4rYFpf5X1TdXzF+bMrk0qxK5OIiIhIJ+zelVlYWIjBgwebc/fv38fVq1eRnJyMBg0a2OruiYiIiAzP7l2Zzs7O5S6tsWLFChw6dIgnZURERESP0PRW5okTJxAeHo7g4GAEBwfjwIEDFpmKujLLWr9+PQYO1P6ZLiIiIqLnhd27Mss6deoUMjMz4efnZ5vZExEREVUjT3zFTLUrMykpCYsWLcLChQtRWFhYbvm6desQHBwMJycnG0ydiIiIqHqxe1dmqQcPHmDr1q18G5OIiIjoMaqkKxMAduzYgRYtWqB169Z2WA0iIiIi46uSrkyAH/onIiIiepIq6coEgC+++EJtZkRERETPGXZlEhEREemEYboyj7z3lqac5yfrAQAHh/XUlPf5ai8OhHfTPA/f1ftx+H8GaM57/Wej8vjJkb0157uu3IUDb/tqH3/NAeVu0OThvbTNJX43AODQn9/QlPf+fBsAtW0FQPPj6bt6v2n8oT20jZ+wzzS+xsfTd43pen6HRwVpynvFbQYApcdTtW8vOaqv5nzXL3fafd8/9I6/5rz3ih3K+77q43N4dIimrNf/mT5Dq7pv/jAyQFO+y7JvAajvy1ofT+8VOwBA8+PZdeUuAOo9sT9EB2rKd/lsi+bHEjA9nqr7mtbjHDAd66r7mtZ1BUzrq7wvK3Zxsiuz+mJXJhEREZFO2L0rEzBdC23u3LkoLCzEw4cPMXz4cISHa/9Nl4iIiOh5YPeuTAcHB8yaNQsTJkyAn58fMjMz4e/vj759+6Jx48a2unsiIiIiw6uSrkwHBwfk5Zner87Pz0edOnXw4osvVnbuRERERNVKlXRlzps3D++99x5iY2Nx584dLFy4EHXq1LHtmhAREREZXJV0ZS5btgxTpkzB3r17sW7dOnzwwQe4fv26DVeDiIiIyPjs3pWZnZ2NpKQkBASYvkb+6quvonXr1khNTa3czImIiIiqGbt3ZdatWxfOzs44evQoPDw8kJmZiXPnzqFly5b2WysiIiIiA6qSrsx//etf+PDDD1FcXIySkhKMHTsWrVq1st9aERERERlQlXRl+vr6wtdX+9XpiYiIiJ5H7MokIiIi0gnDdGUeere/ppz3F98BUOusU+0XVO5MUxxftV9QazclYOqnVO2gU+3b09rF6fm/6wCo9/mp9gWqdneqzke1H1Hr/tD1y51276ZU3Ze19oICpm5QrcctYDp2VfPHp0RqzndeuFJ5Wynva4p5e+47TzO+1v3N6z8blcdX7qbUOHbp+KrPg/bOq85ftfdVdduyK9M42JVJREREpBNV1pU5b948FBQUwNnZGR988AFcXV1tdddERERE1YLduzIBYOzYsYiNjYWHhweOHTuGKVOmYOvWreVqm4iIiIied3bvyrx9+zby8vLg4eEBAHB3d0dGRgbOnDlji/kTERERVRt278ps1KgRGjZsiKSkJPTp0we7d+9Gfn4+rl+/jnbt2tl8hYiIiIiMqkq6MpcuXYr4+HiEhobi+++/R6tWrVCjRg0brgYRERGR8T3VZ8z279+Pjz/+GAAQFBSEkSNHlltetiuzXbt2cHV1xYoVKwAAhYWF8PHxYSUTERER0SPs3pUJAJmZmfjtb38LAIiLi4OHhwdatGhhj/UhIiIiMqwq6cpMTEzE5s2bUVJSgnbt2uHDDz+03xoRERERGRQrmYiIiIh0glf+JyIiItIJ43RlKvYdHhzWU1Pe56u9OPC2r+Z5+K45oNwveCC8m/bxV+/XPHfANP+DQ3tozyfsU+5fVHksAWjuU/SK2wwAmufvk7DvqfKq3Zdat5fv6v0AnqKLc3gvbfOJ340jYwZpygKA59K1yr2sqvva4dEhmvNe/7dJuV9QtU/x6LghmvMei79W7hdU3fdVuzLtPb5qt6ZqV6bKvq/6PKXcSazxuAJMx5Zyh7Hqvqw4H9V9WfV5UHXfYVfms2PzV8w2bNgAFxcX7Nmzx3xbeno6wsLC0K9fP4SFheHnn3+29d0SERERGZ5NT8wyMjKQmJgINze3crfPmjULQ4cOxfbt2zF06FD84x//sOXdEhEREVULNqtkAoCZM2di2rRpcHZ2Nt9269YtpKWlITDQ9LJ0YGAg0tLSkJ2dbYPpExEREVUfNqtkSkhIQMuWLdGxY8dyt9+4cQNNmzY1X+m/Ro0aaNKkCW7cuIFGjRrZaDWIiIiIjO+JJ2ZaKpmuXr2KtWvXIiEhwT6zJCIiInoO2KSSqWnTprh58yYCAkzfeMrMzMSMGTMwceJE+Pn54ebNmyguLkaNGjVQXFyMX3/9Fc2aNbPdWhARERFVAzapZAJMJ2ilIiIiMGLECPj5+QEA2rZtiy1btiAkJARbtmxB27Zt+TYmERER0SNsUsn0JDExMZg6dSo++eQT1KtXDwsWLKjUpImIiIiqI01vZXbq1AmJiYmaB121alW5v//pT3/CN998ozYzIiIioucMuzKJiIiIdIJdmUREREQ6wRMzIiIiIp3giRkRERGRTvDEjIiIiEgneGJGREREpBM8MSMiIiLSCZ6YEREREekET8yIiIiIdIInZkREREQ6wRMzIiIiIp3giRkRERGRTvDEjIiIiEgnasTExMQ860lodfr0aZw8eRKXLl1CQUEBmjRpojxGfn4+nJ2dbTqvhw8f4ty5c6hVqxZeeOEFTf9m/fr1aNu2rU3nUerq1as4fPgwHB0d0bBhQ4vlubm5mudZKjs7GwDg5OSEjIwMHDx4EA4ODmjUqNFj/01aWhpSU1Nx+fJl1KxZE/Xr16/wPqrL9uW2tXT79m38/PPPyMrKQs2aNfHiiy8qzdGe7ty5g1q1amnOJycn4w9/+INd5pKfn4/z58+jdu3aNt+PAeDevXv46aefKtyXc3JykJ6ejtu3b6NOnTqoWbNmhWNWl+MW4LFrjZ6P3WpLDCA1NVX8/f0lMDBQoqOjJTo6WgIDA6Vv376SkpKiNFaPHj0sbrty5YpERUWJv7+/zJ8/X+7fv29eNmTIEIt8cnKyeHl5ibe3txw5ckQGDRok/fv3F09PTzl06JBFfu/evRY/Pj4+5j8/av369eY/Z2RkSHh4uLi6ukpoaKikp6db5EePHi23bt0SEZGkpCTx8fGR6Oho6d69u2zcuNEi7+rqKqNHj5adO3fKw4cPrT5OZa1evVrc3NykW7dukpSUJN27d5eRI0eKr6+vfP311xb5M2fOSEBAgHTq1EnatGkjgYGB4uHhIe+//77cuXPHIm/k7cttW/G2vXz5skRGRoq7u7sEBARIQECAuLu7S2RkpNX1rUhgYKDFbdnZ2TJ9+nR59913JT4+vtyyMWPGWOTPnj0roaGhMnDgQLlw4YJER0dLhw4dpHv37pKWlmaRP3/+vMVP9+7d5cKFC3L+/HmL/IEDB8x/zs3NlcmTJ0vv3r1lzJgxkpmZaZGfOXOmefseO3ZMvL29JSAgQLp06SL79++3yHt6esqcOXOsztWaHTt2yOuvvy79+vWT1NRU6dmzp7zxxhvi6ekpu3btKpf95Zdf5M9//rO4uLhImzZtxNPTUzp06CDz5s2TBw8eWIxt5ONWhMduVR67pMYQJ2YhISFy7Ngxi9uPHj0qwcHBFrdbO+BKf7p06WKRHzFihMTHx8vp06dl6tSpEhYWJrm5ueb7ftSgQYPk7NmzcuTIEfH09JQffvhBREROnjwpYWFhFnkXFxcJCwuT4cOHm3/at28vw4cPl4iICIv8gAEDzH+eMmWKfPLJJ3L37l1Zs2aNjBw50iIfFBRk/nNYWJhcvnxZRESysrLKLSvl7+8vK1askODgYOnatavMnz/f6n8ypQIDAyUzM1PS09Pl9ddfl0uXLomIyK+//mr1P8tBgwaZt9euXbtk9uzZUlhYKEuWLJGJEyda5I28fbltK962YWFhsmnTJikuLjbfVlxcLBs3brT6n6u1E6HSHx8fH4v82LFjZcGCBbJ9+3Z555135P3335eioiIRsb5thw0bJklJSbJhwwbp2bOnbNq0ybwuUVFRFnkXFxfp1auX+Pn5mX9ee+018fPzk169elnky27f2bNny6xZs+Snn36S2NhYGT9+vEW+7DaMiIiQ1NRUERG5dOmShIaGWuT9/Pxk7ty50qVLFxkwYICsWrVKcnJyLHKlQkJC5Ny5c+Z9+fjx4yIicuHCBYvHZ/jw4bJp0ybJycmRlStXyqJFiyQrK0umT58uMTExVsc26nErwmPX1scu2Y4hTsz8/f2VlrVp00YiIiLKHXBlD7xHPXqQx8XFyYABAyQ7O7vcwWgt36dPnwrHEjH9phUWFmZ+0hUxPcE+TtkxgoKCpKSkpNzfH+Xv728+eAYPHlxumbUDtOw6nTx5UmJiYsTDw0MGDx4siYmJFc6nZ8+ej132uDmW/Q/G2vYy8vbltq142/br18/itoqWWTsRKv1xdXWtcD4lJSUSExMjI0aMkPv371udf9nHR8v6LlmyREaOHCnXrl0z36Z1+wYHB0thYaH579a2V9nH7K233iq3rKLtW1hYKNu2bZPo6Ghxc3OTCRMmlHu1ztp8Hp33o+v76LYdOHCgiJj+M+7bt2+Fc9eyTE/HrQiPXVsfu2Q7FX94QCdefvllfPrpp3j77bfRoEEDAKbPQaxevRq///3vLfItWrTA3LlzrX4OpEePHha3PXjwoNzfo6OjUatWLURGRuLevXsW+ZKSEvOfg4KCyi0rLi62yIeGhqJr166YOXMmWrdujXHjxsHBweExawvcvXsX+/btg4iguLi4XNbav3vjjTcwadIkTJo0CX369EFcXByCg4Px/fffo3nz5o+9HwBo37492rdvj2nTpmH79u3YsGEDhgwZUi7j7OyMffv2IS8vDw4ODti+fTv69euHY8eOwdHR8vsjtWvXxvHjx9G5c2fs3r273GcYrM3fyNuX27bibdugQQNs2bIFb775pnm5iGDz5s2oV6+eRb558+ZISEhA06ZNLZZZ27ZFRUXl7n/WrFlYsGABRo0aZbHdS++7lI+PT7llZbd7qTFjxiAtLQ0TJ05ESEgIwsPDK9y+hYWFuHjxIkQEDg4OcHJyMi+z9nh6e3tj/vz5GD9+PLy8vPDtt98iICAABw8eNB8L1jg5OaF///7o378/bt68iQ0bNmDOnDn47rvvyuUcHBxw8eJF5ObmoqCgACkpKXBzc0N6errFvlyzZk1cuXIFr7zyCk6fPm3+zJejo6PVz5kZ+bgFeOza+tglG3qGJ4WaZWVlybRp08TNza3cz9SpU61+buPzzz+XU6dOWR1r2bJlFreNHTtWdu/ebXH76tWrpU2bNha3z549W/Ly8ixuv3jxogwbNqzCdUlISJDQ0FCrb8uUevS3yYyMDBExPQ6P/lYtYnqlYPny5dKtWzdp3769uLi4SKdOnWTmzJmSnZ1tkbf221RFUlJSJCQkREJDQ+Xs2bMyadIk6dixo3h6esrBgwct8idOnJBu3bqJm5ub+Pn5mT8Pk5mZKUuWLLHIV5fty21ruW3T09MlMjJSPDw8JDAw0Py5loiICLl48aJFfv78+ea32x41Z84ci9uio6PlyJEjFrf/85//tLpt33vvPavb9saNGxW+PfPgwQNZuHChREVFSbdu3R6bK32Ls/RVvtLtm5eXZ/VVngcPHsicOXPE3d1d+vTpIy4uLuLq6iojRoyQK1euWORVt+/u3bvFw8NDvLy8JDk5Wd555x158803pXPnzrJ58+Zy2T179oiXl5cEBgaa8yKmbTtjxgyLsavLcSvCY1frsevu7v7YY5dsx0GkzK+QBpCTkwMAFf42qar0IbD2W0N+fj7q1KmjNFZFv3UBwC+//IKUlBQEBgYqzbO4uBiFhYUVfivm7t27KC4urvCbNkVFReV+k38at2/fRv369a3+ZlYqOzu7wm8HWWP07ctt+/j8jRs3AADNmjVT3i8eJycnBw4ODlYfkwsXLqBly5aaxikoKMC9e/fwm9/8psJcSkoKjhw5glGjRinN8969e8jKynrstzkLCgpw5coVlJSUoFmzZla/tQcA165de+KrLRUpLi7G2bNn8bvf/Q6NGze2WJ6bm4vLly/jj3/8I+rWrat5XKMft4DpW5Opqak8dq3k7XHsUgWe3Tlh5Xz++efMV+N8qbt37xo2r6e56DFP1UtRUZGcOXPG/AF95qtXXkRk3bp1mrP09Ax7YmbtbQHmq0++lLWv0Rslr6e5PKu86iUPmH92eXtfnoJ5Y+VVLydCtmOID/9bI4rvwDKv3/y+ffseu8zaB7j1lNfTXPSYj4mJQd++feHm5ob4+HhERUXhs88+w0svvcS8zvKqY8fGxmLFihXIy8vDmDFjsHjxYnh5eeHUqVOYO3cu1qxZw7yB86NHj4abm1u5t1dzc3OxbNkyODg4WP3CBtmGoU7MkpOT0b59e7z00kuYPHky8vLycPr0aXh7ezNv4Pxf/vIXeHh4WD15y8/Pt7hNT3k9zUWP+Vu3bmHYsGEAgHnz5uGzzz5DZGQkli9fbvUzP8w/u7zq2EVFRWjTpg0AoF69evDy8gJg+kbh/fv3mTd4ft68eUhMTMSUKVPQoUMHAECvXr2watUqiyzZWNW+QFc5ISEh5a4dU1xcXOFbZMwbI9+vXz+r30ATEenevbuu83qaix7z/fv3t7ht5cqVEhgYKL1792ZeR3nVscteF2vRokXlllm7ThfzxsqLmBoOoqOjZeHChfLgwQOrF1Um2zNUibk88s0aR0dHq9enYd5Y+SFDhuDOnTtWx4iMjNR1Xk9z0WO+VatW2LNnT7nbIiIiMGzYMFy7do15HeVVx3Z3d8fdu3cBAOPGjTPffunSJavfPmTeWHkAaNq0KeLi4tC8eXO8/fbbVt/SJjt4VmeETyM8PLysajPZAAAB0UlEQVRcB1tKSorVqg3mjZmn6qekpKTcq6ZlWfsWJ/PPLq86dkUeNw7zxs1fvXrV4tp3ZB+GOjH78ccfxcfHR6KioiQqKkp8fHzkxIkTzFeTfFl6u3yHSl5Pc2Ge+crk9TQX5o2Xp6djqBMzEZGcnBzz13UrKu9l3pj5Unq7fIdKXk9zYZ75yuT1NBfmjZenp2Oob2UCQP369ZW+psu8sfKlxECX+9DzXJhnvjJ5Pc2FeePl6enUiImJiXnWkyACTJfXaNCgAV544QW8/PLLaNiwIX788cfH1tjoKa+nuTDPfGXyepoL88bLkw1UwatyRJro5fIdT5PX01yYZ74yeT3NhXnj5anyDHW5DKreRCeX73iavJ7mwjzzlcnraS7MGy9PlccTM9KNOnXqIDU11fz31NRU1K5d2xB5Pc2FeeYrk9fTXJg3Xp4qz0GEn+YjfThx4gTGjh2Lli1bAgAuXLiApUuXws3NTfd5Pc2FeeYrk9fTXJg3Xp4qjydmpCt37txBSkoKAMDNze2xV6TWY15Pc2Ge+crk9TQX5o2Xp8rhiRkRERGRTvAzZkREREQ6wRMzIiIiIp3giRkRERGRTvDEjIiIiEgneGJGREREpBP/HxiAUi3if681AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "TzA-ijRXtnSo",
        "outputId": "f71ea0a3-9db6-45c5-98a0-c5a64a04faaf"
      },
      "source": [
        "corr = dados.loc[:,'g-0':'c-99'].corr()\n",
        "corr_cel_gen = corr.loc['g-0':'g-50','c-0':'c-50']\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_cel_gen, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabc656bcd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ORqTpnUCucBo",
        "outputId": "bfa2619d-e6b6-4fe2-f626-d2494a6e0c0a"
      },
      "source": [
        "sns.lmplot(data=dados, x='g-44', y='c-13', line_kws={'color': 'red'})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fabc65d8cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmR6cbjMtz0x"
      },
      "source": [
        "##Códigos Aula 04"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VlUymc1tt3kK",
        "outputId": "c59ac99a-eb79-4b8a-9b16-5f48417189cc"
      },
      "source": [
        "#Dados do arquivo resultado\n",
        "dados_resultado = pd.read_csv('https://github.com/alura-cursos/imersao-dados-desafio-final/blob/main/Dados/dados_resultados.csv?raw=true')\n",
        "dados_resultado"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
              "      <th>acetylcholine_receptor_antagonist</th>\n",
              "      <th>acetylcholinesterase_inhibitor</th>\n",
              "      <th>adenosine_receptor_agonist</th>\n",
              "      <th>adenosine_receptor_antagonist</th>\n",
              "      <th>adenylyl_cyclase_activator</th>\n",
              "      <th>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <th>aldehyde_dehydrogenase_inhibitor</th>\n",
              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
              "      <th>angiogenesis_inhibitor</th>\n",
              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>protein_synthesis_inhibitor</th>\n",
              "      <th>protein_tyrosine_kinase_inhibitor</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
              "      <th>ribonucleoside_reductase_inhibitor</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
              "      <th>trpv_antagonist</th>\n",
              "      <th>tubulin_inhibitor</th>\n",
              "      <th>tyrosine_kinase_inhibitor</th>\n",
              "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
              "      <th>vegfr_inhibitor</th>\n",
              "      <th>vitamin_b</th>\n",
              "      <th>vitamin_d_receptor_agonist</th>\n",
              "      <th>wnt_inhibitor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>23814 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",
              "23809  id_fffb1ceed  ...              0\n",
              "23810  id_fffb70c0c  ...              0\n",
              "23811  id_fffc1c3f4  ...              0\n",
              "23812  id_fffcb9e7c  ...              0\n",
              "23813  id_ffffdd77b  ...              0\n",
              "\n",
              "[23814 rows x 207 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmlR-iAQunb5",
        "outputId": "a7def619-4d3b-423b-c81f-586f61fd1067"
      },
      "source": [
        "#Exibe a soma total de cada coluna, exceto a primeira coluna (drop.('id', axis=1))\n",
        "#em ordem decrescente (ascending=False)\n",
        "dados_resultado.drop('id', axis=1).sum().sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nfkb_inhibitor                                832\n",
              "proteasome_inhibitor                          726\n",
              "cyclooxygenase_inhibitor                      435\n",
              "dopamine_receptor_antagonist                  424\n",
              "serotonin_receptor_antagonist                 404\n",
              "                                             ... \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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mESKxgBZvIIG"
      },
      "source": [
        "#Cria mais duas colunas contendo o número de MOA e se é ativo ou não\n",
        "dados_resultado['n_moa'] = dados_resultado.drop('id', axis=1).sum(axis=1)\n",
        "dados_resultado['ativo_moa'] = (dados_resultado['n_moa'] != 0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8K8pYBupv3Fx",
        "outputId": "83d95afe-3dac-473d-a82e-1c09dfa20a31"
      },
      "source": [
        "dados_resultado.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
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              "      <th>acetylcholine_receptor_agonist</th>\n",
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              "      <th>adenosine_receptor_agonist</th>\n",
              "      <th>adenosine_receptor_antagonist</th>\n",
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              "      <th>adrenergic_receptor_agonist</th>\n",
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              "      <th>akt_inhibitor</th>\n",
              "      <th>aldehyde_dehydrogenase_inhibitor</th>\n",
              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
              "      <th>angiogenesis_inhibitor</th>\n",
              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
              "      <th>ribonucleoside_reductase_inhibitor</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
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              "  </tbody>\n",
              "</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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "aVxsnlnTwAlE",
        "outputId": "44bb5453-a590-44b8-dc07-bb1ad5f9cd9c"
      },
      "source": [
        "#Merge de dados ('n_moa', 'ativo_moa') considerando como chave o 'id' (on='id')\n",
        "merge_dados = pd.merge(dados, dados_resultado[['id', 'n_moa', 'ativo_moa']], on='id')\n",
        "merge_dados.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <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>0.3680</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",
              "      <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  ...    c-98    c-99  n_moa  ativo_moa\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.3801  0.4176      1       True\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.6077  0.7371      0      False\n",
              "2  id_000a6266a  com_droga     48   D1  ... -1.4080  0.6931      3       True\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.3876 -0.8154      0      False\n",
              "4  id_001626bd3  com_droga     72   D2  ... -0.3786  0.7125      1       True\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNsgMSuAwTdy",
        "outputId": "5139d37f-fe49-40c1-fc41-e6dae41fcc20"
      },
      "source": [
        "#Exibe os valores contidos na combinação de dados, considerando os que tiveram\n",
        "#tratamento com controle\n",
        "merge_dados.query('tratamento == \"com_controle\"')['ativo_moa'].unique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "GlKTA8_swelN",
        "outputId": "8e97d236-5212-41eb-d26c-c43bb7874b08"
      },
      "source": [
        "#Seleciona os valores dos 5 primeiros compostos e depois plota o boxplot\n",
        "composto_principal = merge_dados['droga'].value_counts().index[:5]\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(data=merge_dados.query('droga in @composto_principal'), y='g-0', x='droga', hue='ativo_moa')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5705758690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7sinW8AwwrxM",
        "outputId": "52189fc3-dd09-4869-d514-055d1b30939d"
      },
      "source": [
        "#Coluna eh_controle indicando se teve um tratamento com controle ou não\n",
        "merge_dados['eh_controle'] = (merge_dados['tratamento'] == 'com_controle')\n",
        "merge_dados"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>...</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>eh_controle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9641</td>\n",
              "      <td>-0.1846</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>ecf3b6b74</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8129</td>\n",
              "      <td>-0.1514</td>\n",
              "      <td>-0.4652</td>\n",
              "      <td>-0.7390</td>\n",
              "      <td>-1.3270</td>\n",
              "      <td>0.9925</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 880 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo  ... n_moa ativo_moa  eh_controle\n",
              "0      id_000644bb2     com_droga     24  ...     1      True        False\n",
              "1      id_000779bfc     com_droga     72  ...     0     False        False\n",
              "2      id_000a6266a     com_droga     48  ...     3      True        False\n",
              "3      id_0015fd391     com_droga     48  ...     0     False        False\n",
              "4      id_001626bd3     com_droga     72  ...     1      True        False\n",
              "...             ...           ...    ...  ...   ...       ...          ...\n",
              "23809  id_fffb1ceed     com_droga     24  ...     1      True        False\n",
              "23810  id_fffb70c0c     com_droga     24  ...     1      True        False\n",
              "23811  id_fffc1c3f4  com_controle     48  ...     0     False         True\n",
              "23812  id_fffcb9e7c     com_droga     24  ...     1      True        False\n",
              "23813  id_ffffdd77b     com_droga     72  ...     0     False        False\n",
              "\n",
              "[23814 rows x 880 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7S4umwwCyPJb",
        "outputId": "a78c8c70-80d2-4156-af51-9674db3e2087"
      },
      "source": [
        "#Criando mais três colunas para indicar qual foi o tempo exposto\n",
        "#merge_dados['tempo_24'] = (merge_dados['tempo'] == 24)\n",
        "#merge_dados['tempo_48'] = (merge_dados['tempo'] == 48)\n",
        "#merge_dados['tempo_72'] = (merge_dados['tempo'] == 72)\n",
        "merge_dados = pd.get_dummies(dados, columns=['tempo'])\n",
        "merge_dados.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g-0</th>\n",
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              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
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              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
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              "      <th>g-25</th>\n",
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              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>g-35</th>\n",
              "      <th>...</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
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              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "      <th>tempo_24</th>\n",
              "      <th>tempo_48</th>\n",
              "      <th>tempo_72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
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              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
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              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
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              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
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              "      <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",
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              "      <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>0.2198</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
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              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</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>-0.2102</td>\n",
              "      <td>...</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>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>-1.1800</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>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>-1.6740</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>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>-0.7110</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento dose  ... tempo_24  tempo_48  tempo_72\n",
              "0  id_000644bb2  com_droga   D1  ...        1         0         0\n",
              "1  id_000779bfc  com_droga   D1  ...        0         0         1\n",
              "2  id_000a6266a  com_droga   D1  ...        0         1         0\n",
              "3  id_0015fd391  com_droga   D1  ...        0         1         0\n",
              "4  id_001626bd3  com_droga   D2  ...        0         0         1\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "qqb0EJs4zyHU",
        "outputId": "88dbe7dd-2937-495a-b480-7dfdcef33277"
      },
      "source": [
        "#Analisando o g-3 de acordo com o tempo e dose\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(data=merge_dados.query('droga in @composto_principal'), y='g-3', x='tempo', hue='dose')\n",
        "#Outlier do g-3\n",
        "merge_dados['g-3'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "T9g2weVU1klU",
        "outputId": "aa24c584-8c74-49e9-b9a0-0b1f26f5557b"
      },
      "source": [
        "#Dependendo da forma do experimento o MOA(s) é ativado ou não para o mesmo g\n",
        "merge_dados[['g-3', 'tratamento', 'tempo', 'ativo_moa', 'n_moa']]"
      ],
      "execution_count": null,
      "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>g-3</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>n_moa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.6208</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0604</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.0764</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.5288</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.6919</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>-0.5080</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>0.9905</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>-0.7389</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>0.2044</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>0.7952</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          g-3    tratamento  tempo  ativo_moa  n_moa\n",
              "0     -0.6208     com_droga     24       True      1\n",
              "1      0.0604     com_droga     72      False      0\n",
              "2     -0.0764     com_droga     48       True      3\n",
              "3      0.5288     com_droga     48      False      0\n",
              "4      0.6919     com_droga     72       True      1\n",
              "...       ...           ...    ...        ...    ...\n",
              "23809 -0.5080     com_droga     24       True      1\n",
              "23810  0.9905     com_droga     24       True      1\n",
              "23811 -0.7389  com_controle     48      False      0\n",
              "23812  0.2044     com_droga     24       True      1\n",
              "23813  0.7952     com_droga     72      False      0\n",
              "\n",
              "[23814 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQEdZodq3PlF",
        "outputId": "6f8ef78c-53ac-4b2c-e401-26f3b910c39e"
      },
      "source": [
        "#O tipo do top 10 de compostos/drogas existentes\n",
        "top_dados = dados_resultado.drop('id', axis=1).sum().sort_values(ascending=False)\n",
        "top_dados.filter(like='inhibitor').head()\n",
        "top_dados.filter(like='antagonist').head()\n",
        "top_dados.filter(like='_agonist').head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adrenergic_receptor_agonist        270\n",
              "glucocorticoid_receptor_agonist    266\n",
              "serotonin_receptor_agonist         236\n",
              "acetylcholine_receptor_agonist     190\n",
              "estrogen_receptor_agonist          158\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xWoKleCyTY0"
      },
      "source": [
        "##Códigos Aula 05 (Última)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-wiK4lZyYnz"
      },
      "source": [
        "#Importando as bibliotecas responsáveis pelo Machine Learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1Dkxeiw31NZ"
      },
      "source": [
        "#Selecionando do nosso conjunto de dados tudo o que for float\n",
        "x = merge_dados.select_dtypes('float64')\n",
        "#Selecionando o nosso target, o resultado de acordo com os nossos dados\n",
        "y = merge_dados['ativo_moa']\n",
        "\n",
        "#Pega os dados que são treinados, usando 20%\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.2, stratify=y)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7SPiMVN4Mto",
        "outputId": "e4e91ee2-8fef-4c38-c7f1-b6d1b3c45ada"
      },
      "source": [
        "#Treinando nossa máquina de acordo com os nossos dados, usando Regressão Logística\n",
        "modelo_rlog = LogisticRegression(max_iter=1000)\n",
        "modelo_rlog.fit(x_treino, y_treino)\n",
        "modelo_rlog.score(x_teste, y_teste)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6147386101196725"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBh76OXr4TeH",
        "outputId": "afe60d23-2585-4830-ce57-e890f9e3f68b"
      },
      "source": [
        "#Modelo para avaliar a frequência que ocorreu os valores, usando um método mais simples\n",
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "accuracy_score(y_teste, previsao_dummy)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6067604450976275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTM-PgEF4Z0X"
      },
      "source": [
        "#Árvore de Decisão, com um certo tamanho de nós\n",
        "teste=[]\n",
        "treino=[]\n",
        "for i in range(1, 15):\n",
        "   modelo_arv = DecisionTreeClassifier(max_depth=i)\n",
        "   modelo_arv.fit(x_treino, y_treino)\n",
        "   teste.append(modelo_arv.score(x_teste, y_teste))\n",
        "   treino.append(modelo_arv.score(x_treino, y_treino))"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ky7NzN156znU",
        "outputId": "0b40b82e-d771-41d4-a764-21d380f500c7"
      },
      "source": [
        "teste"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6067604450976275,\n",
              " 0.6067604450976275,\n",
              " 0.6147386101196725,\n",
              " 0.6246063405416754,\n",
              " 0.6285954230526979,\n",
              " 0.6346840226747849,\n",
              " 0.6233466302750368,\n",
              " 0.6214570648750787,\n",
              " 0.612639093008608,\n",
              " 0.6136888515641402,\n",
              " 0.6134788998530338,\n",
              " 0.5973126180978375,\n",
              " 0.5945832458534537,\n",
              " 0.5924837287423893]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HebDYd3E619Q",
        "outputId": "3551aaa6-0613-4f5f-cae0-0c953dd59203"
      },
      "source": [
        "treino"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6066348223190384,\n",
              " 0.6066348223190384,\n",
              " 0.6199674557765996,\n",
              " 0.6265812818224765,\n",
              " 0.6387066295732508,\n",
              " 0.6464227599601071,\n",
              " 0.6553461760537505,\n",
              " 0.6707259461445594,\n",
              " 0.6850559025772925,\n",
              " 0.7076268962259199,\n",
              " 0.7320875544590835,\n",
              " 0.760012597763897,\n",
              " 0.7923468584326282,\n",
              " 0.8227914545168232]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "6EiIAlKO4hsv",
        "outputId": "679cc279-6950-4998-c084-3472d314356a"
      },
      "source": [
        "#Plotando um gráfico para exemplificar o resultado\n",
        "sns.lineplot(x=range(1, 15), y=teste, label='teste')\n",
        "sns.lineplot(x=range(1, 15), y=treino, label='treino')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa7b25f1490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fc_u7PPY7BNB",
        "outputId": "4c46c497-fdd7-4d30-caef-edb9975b7000"
      },
      "source": [
        "x = merge_dados.drop(['id', 'n_moa', 'ativo_moa', 'droga'], axis=1)\n",
        "x = pd.get_dummies(x, columns=['tratamento', 'tempo', 'dose'])\n",
        "y = merge_dados['ativo_moa']\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.2, stratify=y, random_state=376)\n",
        "modelo_ranForest = RandomForestClassifier()\n",
        "modelo_ranForest.fit(x_treino, y_treino)\n",
        "modelo_ranForest.score(x_teste, y_teste)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6863321436069704"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skdaEnr6-bLV",
        "outputId": "ffa7915f-d4d4-4336-bc89-2bfad421a19a"
      },
      "source": [
        "#Desafio 01 - Utilizando o método dos vizinhos\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "num_neigh = 3\n",
        "\n",
        "neigh_fun = KNeighborsClassifier(num_neigh)\n",
        "neigh_fun.fit(x_treino, y_treino)\n",
        "neigh_fun.score(x_teste, y_teste)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6155784169640982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDzeAFc2_5PJ",
        "outputId": "0b4a879b-6bfc-4288-d66f-71e49acb30c9"
      },
      "source": [
        "#Desafio 04 - Testando outros problemas\n",
        "x = merge_dados.loc[:, 'g-0':'c-99']\n",
        "y = merge_dados['tempo'].map({24:0, 48:1, 72:2})\n",
        "\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "modelo_rlog2 = LogisticRegression(solver='newton-cg')\n",
        "modelo_rlog2.fit(x_treino, y_treino)\n",
        "modelo_rlog2.score(x_teste, y_teste)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.686122191895864"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "Y0VNPZISBWde",
        "outputId": "5d2767c4-7f98-48cf-f7ce-de10b87f6fd6"
      },
      "source": [
        "#Desafio 05 - Testes com a droga mais utilizada\n",
        "merge_dados['droga'].value_counts() #Para achar os compostos que mais aparecem\n",
        "\n",
        "#As duas drogas possuem um valor muito parecido, por isso é mais fácil de\n",
        "#chegar num resultado favorável\n",
        "dt_temp = merge_dados.query('droga in [\"8b87a7a83\", \"5628cb3ee\"]')\n",
        "\n",
        "x = dt_temp.loc[:, 'g-0':'g-12']\n",
        "y = dt_temp['droga'].map({'8b87a7a83':0, '5628cb3ee':1})\n",
        "sns.countplot(x=y)\n",
        "y.value_counts(normalize=True) #Porcentagem muito parecida\n",
        "\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y,random_state=376)\n",
        "\n",
        "#Aqui modificamos o x. Se colocamos mais colunas de g e c o algoritmo consegue \n",
        "#100% de taxa de acerto. A diferença da ativação entre as duas drogas é muito \n",
        "#grande e fácil do algoritmo identificar.\n",
        "modelo_rlog3 = LogisticRegression()\n",
        "modelo_rlog3.fit(x_treino, y_treino)\n",
        "modelo_rlog3.score(x_teste, y_teste)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9382716049382716"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}