Aula_4.ipynb

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  "metadata": {
    "colab": {
      "name": "Aula_4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOq6zJOIGrp7ET3BZKeiqLJ",
      "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/zaptudo/Alura-imersaodados3/blob/main/Aula_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBHaddO0gTfE"
      },
      "source": [
        "#Aula 04"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAUcav1MgaTk"
      },
      "source": [
        "##Exemplos em aula"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iu4WdBnmgl6Z"
      },
      "source": [
        "###Biblotecas usadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLzq596f5vvc"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzPZGC3XgqVq"
      },
      "source": [
        "###Bases de Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teqjjHf0gS3R"
      },
      "source": [
        "dados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true', compression='zip')\n",
        "dados_resultados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "c_Vn1LBuhlx_",
        "outputId": "40d01cdc-ec04-4a6d-ad4f-6f3f6f97d728"
      },
      "source": [
        "dados_resultados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 207 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ...  wnt_inhibitor\n",
              "0  id_000644bb2  ...              0\n",
              "1  id_000779bfc  ...              0\n",
              "2  id_000a6266a  ...              0\n",
              "3  id_0015fd391  ...              0\n",
              "4  id_001626bd3  ...              0\n",
              "\n",
              "[5 rows x 207 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6l2ohzXSifz8"
      },
      "source": [
        "A variável `id` da base de dados de resultados corresponde ao `id` da base de dados de experimentos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfxGVEFjiyot"
      },
      "source": [
        "As outras variáveis da base de resultados são do tipo *categórica nominal*, com valores `0` ou `1`. Essas variáveis representam o mecanismo de ação. A última parte do nome depois do *underscore* é a ação (inibe, ativa, etc) e a parte do nome anterior a ação é o alvo do mecanismo (nome da proteína ou enzima, por exemplo). O valor `1` indica que o mecanismo de ação foi ativado e o valor `0` que o mecanismo de ação não foi ativado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5LABIfQh6xQ",
        "outputId": "853790da-7f7b-41d3-9cb9-791f5885be70"
      },
      "source": [
        "dados_resultados['acat_inhibitor'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jUd7VHdiKJs",
        "outputId": "14e8d85e-265e-4599-ae5c-c31830c7a535"
      },
      "source": [
        "dados_resultados['acetylcholine_receptor_agonist'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYkFPZM0ka_e"
      },
      "source": [
        "Obs.: moa = mecanismo de ação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFDig4ZYpbgo",
        "outputId": "ec326e66-16d1-4ce6-cdb6-39c9bb6ccb67"
      },
      "source": [
        "dados_resultados.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23814 entries, 0 to 23813\n",
            "Columns: 207 entries, id to wnt_inhibitor\n",
            "dtypes: int64(206), object(1)\n",
            "memory usage: 37.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGxDmpIIiVEz",
        "outputId": "d0dc8770-35c5-485f-eb19-d031e8da4404"
      },
      "source": [
        "dados_resultados.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                       object\n",
              "5-alpha_reductase_inhibitor               int64\n",
              "11-beta-hsd1_inhibitor                    int64\n",
              "acat_inhibitor                            int64\n",
              "acetylcholine_receptor_agonist            int64\n",
              "                                          ...  \n",
              "ubiquitin_specific_protease_inhibitor     int64\n",
              "vegfr_inhibitor                           int64\n",
              "vitamin_b                                 int64\n",
              "vitamin_d_receptor_agonist                int64\n",
              "wnt_inhibitor                             int64\n",
              "Length: 207, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Z8T8OA1x_78"
      },
      "source": [
        "Quantidade vezes que cada mecanismo de ação (moa) foi ativado em todas as observações:\n",
        "\n",
        "Somando as colunas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGsLy-U2ksHz",
        "outputId": "77103407-44a9-401b-9a4e-593877f8b15e"
      },
      "source": [
        "# Forma 1 de remover o id\n",
        "#contagem_moa = dados_resultados.drop('id', axis=1).sum().sort_values(ascending=False)\n",
        "\n",
        "# Forma 2 de remover o id\n",
        "contagem_moa = dados_resultados.select_dtypes('int64').sum().sort_values(ascending=False)\n",
        "contagem_moa"
      ],
      "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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Um6VG0qZx8qQ"
      },
      "source": [
        "Quantidade de mecanismos de ação (moa) que foram ativados em cada observação:\n",
        "\n",
        "\n",
        "Somando as linhas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfvO4D_ak_m3",
        "outputId": "54b2d2c0-c08c-4a88-9c4d-7161cfa6281d"
      },
      "source": [
        "n_moa = dados_resultados.drop('id', axis=1).sum(axis=1)\n",
        "n_moa"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        0\n",
              "2        3\n",
              "3        0\n",
              "4        1\n",
              "        ..\n",
              "23809    1\n",
              "23810    1\n",
              "23811    0\n",
              "23812    1\n",
              "23813    0\n",
              "Length: 23814, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNY1t5a2y2DB"
      },
      "source": [
        "Criando a coluna *n_moa* na base de resultados, para indicar a quantidade de aticações de mecanismos de ativação na observação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "wO6V7udpy0jT",
        "outputId": "a319e6fd-0a21-4ee2-bc6d-6d5f8362380a"
      },
      "source": [
        "dados_resultados['n_moa'] = n_moa\n",
        "dados_resultados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
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              "      <th>acetylcholinesterase_inhibitor</th>\n",
              "      <th>adenosine_receptor_agonist</th>\n",
              "      <th>adenosine_receptor_antagonist</th>\n",
              "      <th>adenylyl_cyclase_activator</th>\n",
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              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
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              "      <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",
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              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
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              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
              "      <th>trpv_antagonist</th>\n",
              "      <th>tubulin_inhibitor</th>\n",
              "      <th>tyrosine_kinase_inhibitor</th>\n",
              "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
              "      <th>vegfr_inhibitor</th>\n",
              "      <th>vitamin_b</th>\n",
              "      <th>vitamin_d_receptor_agonist</th>\n",
              "      <th>wnt_inhibitor</th>\n",
              "      <th>n_moa</th>\n",
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              "      <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",
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              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
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              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 208 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  5-alpha_reductase_inhibitor  ...  wnt_inhibitor  n_moa\n",
              "0      id_000644bb2                            0  ...              0      1\n",
              "1      id_000779bfc                            0  ...              0      0\n",
              "2      id_000a6266a                            0  ...              0      3\n",
              "3      id_0015fd391                            0  ...              0      0\n",
              "4      id_001626bd3                            0  ...              0      1\n",
              "...             ...                          ...  ...            ...    ...\n",
              "23809  id_fffb1ceed                            0  ...              0      1\n",
              "23810  id_fffb70c0c                            0  ...              0      1\n",
              "23811  id_fffc1c3f4                            0  ...              0      0\n",
              "23812  id_fffcb9e7c                            0  ...              0      1\n",
              "23813  id_ffffdd77b                            0  ...              0      0\n",
              "\n",
              "[23814 rows x 208 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrHAwWv0007t"
      },
      "source": [
        "Criando a coluna *ativo_moa* para indicar quando algum composto ativou algum mecanismo de ativação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "mlj-YaKV01_z",
        "outputId": "9c5ae578-8e3f-4620-d14c-474f410c8017"
      },
      "source": [
        "dados_resultados['ativo_moa'] = dados_resultados['n_moa'] != 0\n",
        "dados_resultados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>False</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
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              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 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",
              "23809  id_fffb1ceed                            0  ...      1       True\n",
              "23810  id_fffb70c0c                            0  ...      1       True\n",
              "23811  id_fffc1c3f4                            0  ...      0      False\n",
              "23812  id_fffcb9e7c                            0  ...      1       True\n",
              "23813  id_ffffdd77b                            0  ...      0      False\n",
              "\n",
              "[23814 rows x 209 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7MIeWexMQ7a"
      },
      "source": [
        "###Fazendo a junção dos dados do experimento com os dados do resultado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLR-c_xSMRec"
      },
      "source": [
        "dados_combinados = pd.merge(dados, dados_resultados[['id', 'n_moa', 'ativo_moa']], on='id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "ISs29r29P2_h",
        "outputId": "a48f6b89-75e8-494b-9e78-424008a598bc"
      },
      "source": [
        "dados_combinados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLAo7N_xSAqk"
      },
      "source": [
        "O controle não ativou nenhum mecanismo de ativação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jl537nfFQJRL",
        "outputId": "97af2546-9517-44c7-d676-286ea294fbc0"
      },
      "source": [
        "dados_combinados.query('tratamento == \"com_controle\"')[['tratamento', 'ativo_moa']].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tratamento    ativo_moa\n",
              "com_controle  False        1866\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MY3D_lPOyQv8"
      },
      "source": [
        "7501 observações que usaram tratamento com droga não ativaram nenhum mecanismo de ativação e 14447 ativaram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KbQVhyCx-iE",
        "outputId": "ac0b24c4-4a0e-47a9-a87c-d6ebc91568fc"
      },
      "source": [
        "dados_combinados.query('tratamento == \"com_droga\"')[['tratamento', 'ativo_moa']].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tratamento  ativo_moa\n",
              "com_droga   True         14447\n",
              "            False         7501\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXh1DGEyz97M"
      },
      "source": [
        "principais_drogas = dados_combinados['droga'].value_counts().index[:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "DhlddQB2zDch",
        "outputId": "0f1ed35e-2884-4dd3-90e4-db1ffd5851ae"
      },
      "source": [
        "plt.figure(figsize=[14, 8])\n",
        "sns.boxplot(data=dados_combinados.query('droga in @principais_drogas'), y='g-0', x='droga', hue='ativo_moa')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAgipWUFGvGjCkqLS2xuhQgqhGMAAAAolhe3kJt2bJZixe/YXUpQFQjGAEAAESpkpJieTyrZIyRx7OaUSPgJBCMAAAAolRe3kIFAkaSFAgEGDUCTgLBCAAAIEqtX79Wfr9PkuT3+7R+/VqLKwKiF8EIAAAgSvXqlSGXK06S5HLFqVevDIsrAqIXwQgAACBKZWcPldPpkCQ5nU7l5AyzuCIgehGMAAAAopTbnaLMzL5yOBzKzMxScrLb6pKAqBVndQEAAABouOzsodq163tGi4CT5DDGGKuLCBWvtyx4ZxYAkefll1/Uzp07wt5vfn5ln+3bdwh735LUrl0HjRx5vSV9AwCASk6nQ6mpSTXOZ8QIQNjs3LlD27/9Wq2TwrvraaaAJOlIwbdh7VeSCsp8Ye8TAACcOIIRgLBqnRSnG89paXUZYbPgiyKrSwAAAPXAzRcAAAAA2B7BCAAAAIDtRdSpdEePHtX06dO1fv16JSYmqnv37po6darVZQEAAACIcREVjB555BElJiZq2bJlcjgc2rdvn9UlAQAAALCBiAlGBw8e1D//+U+tWrVKDkflE5zT0tIsrgoAAACAHUTMNUY7d+6U2+3W3LlzNWzYMI0ePVofffSR1WUBAAAAsIGIGTHy+/3auXOnunTpovHjx+vzzz/Xb3/7Wy1fvlxJSTU/iOnHantgEwDrxce7dMTqIiwQH+9SenoLq8sAEMFWrFih5cuXN6htSUmJJMntdjeo/YABA9SvX78GtQViScQEozZt2iguLk6DBw+WJJ177rlKSUnRtm3b1K1bt3qtw+stUyBgGrNMACehosJvdQmWqKjwq7DwgNVlAIhg+/cfbvA+0uutfF5a8+YNOwCzf/9h9lGwBafTUetASsQEo5YtW+rCCy/U2rVrlZmZqW3btsnr9apDhw5WlwYAANCoMjKylJGR1aC2ubmVd/AdP/6BUJYE2E7EBCNJeuihh3TvvfcqNzdXcXFxmjlzpk455RSrywIAAAAQ4yIqGLVr105//etfrS4DAAAAgM1EzF3pAAAAAMAqBCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7cVYXAMA+SktLVFzm04IviqwuJWwKynxKKS2xugwAAFAHRowAAI2ipKRYM2ZMUSnBEAAQBRgxAhA2ycluJR7epxvPaWl1KWGz4IsiNUl2W12GJfLyFmrLls1avPgNjR79G6vLAQCgVgQjNNjatavl8axqUNtjR5CTT+IPxszMvsrIyGpwewCNp6SkWB7PKhlj5PGsVk7OsJP6fQcAoLFxKh0sUVpaqtLSUqvLANBI8vIWKhAwkqRAIKDFi9+wuCIAAGrHiBEaLCMjq8EjNrm5UyVJ48c/EMqSAESI9evXyu/3SZL8fp/Wr1/L6XQAgIjGiBEAIOR69cqQy1V57M3lilOvXhkWVwQAQO0IRgCAkMvOHiqn0yFJcjqdyskZZnFFAADUjmAEAAg5tztFmZl95XA4lJmZxY0XAAARj2uMAACNIjt7qHbt+p7RIgBAVGDECAAAAIDtEYwAAI3ixw94BQAg0hGMAAAh99MHvB57qDMAAJGKYAQACDke8AoAiDYEIwBAyFX3gFcAACIZwQgAEHI84BUAEG0IRgCAkOMBrwCAaEMwAgCEHA94BQBEGx7wCgBoFDzgFQAQTQhGAIBG4XanaMKESVaXAQBAvUTkqXRz587VWWedpW+++cbqUgAAAADYQMQFo6+++kqfffaZTjvtNKtLAQAAAGATERWMysvLNWXKFE2ePNnqUgAAAADYSERdYzRnzhzl5OSobdu2DWqfmpoU4orQWOLjXZKk9PQWFleCcIqPd+mI1UVYID7exbYOoNHwnQqERsQEo08//VQbNmzQ3Xff3eB1eL1lCgRMCKtCY6mo8EuSCgsPWFwJwunYz91uKir8bOsAGg3fqUD9OJ2OWgdSIuZUug8//FBbt27VJZdcon79+qmgoEA33XSTPB6P1aUBAAAAiHERM2J0yy236JZbbglO9+vXT88884w6depkYVUAAAAA7CBiRowAALGlpKRYM2ZMUWlpidWlAABQp4gNRitWrGC0CACiWF7eQm3ZslmLF79hdSkAANQpYoMRACB6lZQUa82aVTLGyONZxagRACDiEYwAACGXl7dQfr9PkuTz+Rg1AgBEPIIRACDk1q3zyJjKxycYY7RuHXcYBQBENoIRACDkUlNTa50GACDSRMztugHYQ0GZTwu+KAprn2XlAUlSUkL4jwUVlPnUMey9Ws/r9dY6DQBApCEYAQibdu06WNLv3vwdkqS01uHvv6Ose99W6t07U++9906VaQAAIhnBCEDYjBx5vSX95uZOlSSNH/+AJf3bUXb20CrBKCdnmIXVAABQN64xAgCE3P79pbVOAwAQaQhGAICQmz//ySrT8+bNtagSAADqh2AEAAi5H37YVes0AACRhmAEAAi5n/3stFqnAQDRqaSkWDNmTFFpaYnVpYQcwQgAEHK33HJblelbbx1rUSUAgFDKy1uoLVs2a/HiN6wuJeQIRgCAkDvllORapwEA0aekpFgezyoZY+TxrI65USOCEQAg5PLyFsrprPyKcTqdMXlkEQDsJi9voQIBI0kKBAIxt28nGAEAQm79+rUKBAKSKr88169fa3FFAICTtX79Wvn9PkmS3++LuX07wQgAEHK9emXI5ap8hrjLFadevTIsrggAcLJifd9OMAIAhFx29lA5nQ5JlafS5eQMs7giAMDJivV9O8EIABBybneKMjP7yuFwKDMzS8nJbqtLAgCcpFjft8dZXQAAIDZlZw/Vrl3fx9wRRQCws1jetxOMAACNwu1O0YQJk6wuAwiLl19+UTt37rCk7/z8yn5zc6da0n+7dh00cuT1lvSN8IvlfTvBCAAA4CTt3LlD27/9Wq2Twv+nVTNV3gHySMG3Ye+7oMwX9j6BxkIwAgAACIHWSXG68ZyWVpcRVgu+KLK6BIRZSUmxnnnmCf3ud3fE3DVG3HwBAAAAQL3k5S3Uli2bY+7hrhLBCAAAAEA9lJQUy+NZJWOMPJ7VKi0tsbqkkCIYAQAAAKhTXt5CBQJGkhQIBGJu1Khe1xht3bpVixYt0pYtW3Tw4EE1b95cZ555poYMGaIzzjijsWsEAAAAYLH169fK76+84Ybf79P69Ws1evRvLK4qdOocMVqyZImGDx+ugoICXXDBBcrOzlbPnj21Z88ejRgxQm+99VY46gQAAABgoV69MuRyVY6ruFxx6tUrw+KKQqvOEaPZs2dr3rx5Ov/884+b9/HHH+t//ud/NGjQoEYpDgAQvfLztys3d6omTJikdu06WF0OAOAkZWcPlcezSn6/5HQ6Y+4hr3WOGBUXF+sXv/hFtfO6dOmi4uLikBcFAIh+f/7zLB0+fFh//vMsq0sBAISA252izMy+cjgcyszMst/tunv37q17771X+fn5VV7Pz8/X/fffr969ezdacQCA6JSfv11FRV5Jkte7Tzt37rC4IgBAKGRnD9WZZ54Vc6NFUj2C0fTp0yVJgwYN0nnnnafMzEydd955uuKKK6rMBwDgmJ+OEjFqBACxwe1O0YQJk2JutEiqxzVGycnJmj17tg4fPqzt27cH70rXsWNHNW3aNBw1AgCizLHRomO83n0WVQIAQP3U+zlGTZs2VefOndWjRw9t3bqVUAQAAAAgZjToAa+TJk0KdR0AgBiSmJhY6zQAIDqVlBRrxowpKi0tsbqUkGtQMDLGhLoOAEAMOeusLlWmO3fuUsOSAIBokpe3UFu2bNbixW9YXUrINSgY9ejRI9R1AABiyObNm6pMb9q0qYYlAQDRoqSkWB7PKhlj5PGsjrlRozpvvnDMnDlzgv/v2rVrcDohIUGtW7dWnz59lJaWFvoKAQBRx+12a8+egirTAIDolpe3UIFA5ZljgUBAixe/odGjf2NxVaFT7xGj7du369lnn9UHH3yg/Px8ffDBB3r22We1adMmvfLKK+rfv79Wr17dmLUCAKJEYeHeWqcBANFn/fq18vt9kiS/36f169daXFFo1XvEKBAI6LHHHtOAAQOCr73zzjtasmSJXnvtNS1cuFCzZs1SVlZWoxQKAIgeP70WlWtTASD69eqVodWrV8rv98nlilOvXhlWlxRS9R4x8ng86tevX5XXLr744uAoUU5Ojnbu3Bna6gAAUYlgBACxJzt7qJxOhyTJ6XQqJ2eYxRWFVr2DUfv27fXKK69Uee3VV19V+/btJUnFxcU82wgAIElyOJy1TgMAoo/bnaLMzL5yOBzKzMxScnJsXT9a71Pppk2bpttvv13PPvusTj31VO3Zs0cul0tPPPGEJGnbtm0aN25coxUKAIgeCQkJOnr0SJVpAED0y84eql27vo+50SLpBILRL37xCy1btkyff/659u7dq/T0dHXv3l3x8fGSpAsuuEAXXHBBoxUKAIgePw5F1U0DAKKT252iCRMmWV1Go6h3MJKk+Ph4nmEEAAAAIOacUDBqTMXFxbrnnnuUn5+vhIQEdejQQVOmTFHLli2tLg0AAACIGWvXrpbHs6pBbY891LWh1xdlZvZVRkZk3sU6Yq6GdTgcGjNmjJYtW6a8vDy1a9dOjz76qNVlAQAawOl01joNAIhOpaWlKi0ttbqMRhExI0Zut1sXXnhhcLp79+7H3QUPABAdAoFArdMAAOtkZGQ1eNQmN3eqJGn8+AdCWVJEiJhg9GOBQECvvPLKcc9NqktqalIjVYRQi493SZLS01tYXAnsgO2t4VasWKHly5eHZF2zZ08/oeUHDBhwwt8DgFXKyvZrX5lPC74osrqUsCoo8ymtbD/7VxuJ5e/UiAxGU6dOVbNmzTRq1KgTauf1likQ4CGC0aCiwi9JKiw8YHElsAO2t4bbv/9w8PM7EXFxcfL5fFWmT3Q9+/cf5meGqOH323dU1O8P8LtqI9H8nep0OmodSIm4YJSbm6sdO3bomWee4Zx0ALBYQ0+3yM/frsmT7w1OP/DAVLVr1yGUpQERJTnZrcTD+3TjOfa6adSCL4rUJMYe8gn7iqhgNHv2bG3YsEHz58/nYYAAEMXat+8YHDVKS2tFKAIARLyICUZbtmzRvHnz1LFjR40YMUKS1LZtWz355JMWVwYAaIjTTmur/Pwduv32u6wuBQCAOkVMMDrzzDO1efNmq8sAAIRIkyZN1anT2YwWAQCiAhfxAAAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALC9iLkrHazx8ssvaufOHWHvNz+/ss/c3Klh71uS2rXroJEjr7ekbwAAAEQegpHN7dy5Q9u//Vqtk8K7KTRTQJJ0pODbsPYrSQVlvrD3CQAAgMhGMIJaJ8XpxnNaWl1G2Cz4osjqEgAAABBhuMYIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYXpzVBQBAfaxdu1oez6oGtc3P3yFJys2d2uD+MzP7KiMjq8HtAQBAZCMYAYh5ycnJVpcAAAAiHMEIQFTIyMhixAYAADQarjECAAAAYHsEIwAAAAC2x6l0AAAAIVBQ5tOCL4rC3m9ZeUCSlJQQ/uPdBWU+dQx7r0DjIBgBAACcpHbtOljW997/3HkzrXX4a+goa987EEoEIwAAgJM0cuT1lvV97FEE48c/YFkNQCzgGiMAAAAAtkcwAgAAABdqx7UAACAASURBVGB7BCMAAAAAtsc1RgAAAECUefnlF7Vz546w95v/n5t9HLu2LdzatevQaNf0EYwAAACAKLNz5w5t//ZrtU4K75/zzVR5e/gjBd+GtV+p8vbwjYlgBAAAAESh1klxuvGcllaXETaN/ZwwrjECAAAAYHsEIwAAAAC2RzACAAAAYHtcY2RzpaUlKi7zNfo5m5GkoMynlNISq8sAwoY7FwEAUDeCEQDEuJ07d2jzlm/lauIOa78Bv0uS9O3OfWHtV5L8Rzj4AQA4MQQjm0tOdivx8D7b3dGkSXJ4/0AErOZq4lazDpdYXUbYHNrxrtUlAACiDNcYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2+PmCwAAAECU4ZEroceIEQAAAADbi6gRo23btmnChAkqKSmR2+1Wbm6uOnbsaHVZAAAAQEThkSuhF1EjRg8++KBGjhypZcuWaeTIkZo0aZLVJQEAAACwgYgJRl6vVxs3btTgwYMlSYMHD9bGjRtVVGSf8yYBAAAAWCNiTqXbvXu3Tj31VLlcLkmSy+VSq1attHv3brVsWb8hwtTUpMYsMSbFx7t0xOoiLBAf71J6egurywDCIj7eZXUJluD3HHZx7Hec7d1e+Bsu9CImGIWC11umQMBYXUZUqajwW12CJSoq/CosPGB1GUBY8HsOxLZjv+Ns7/bCvv3EOZ2OWgdSIuZUujZt2mjPnj3y+yt/yH6/X3v37lWbNm0srgwAAABArIuYEaPU1FR17txZS5Ys0ZAhQ7RkyRJ17ty53qfRAQCqV1paIv+REh3a8a7VpYSN/0iJSksj5isOABAFIupbY/LkyZowYYKeeuopnXLKKcrNzbW6JAAAAAA2EFHB6IwzztDrr79udRkAEFOSk90q3O9Tsw6XWF1K2Bza8a6SG/FZFwCA2BMx1xgBAAAAgFUIRgAAAABsj2AEAAAAwPYi6hojAAAAAPVTUObTgi+KwtpnWXlAkpSUEP7xlYIynzo24voJRgAAAECUadeugyX97s3fIUlKax3+/juqcd83wQgAAACIMiNHXm9Jv7m5UyVJ48c/YEn/jYlrjAAAAADYHsEIAAAAgO0RjAAAAADYHtcYgTuaAAAAwPYIRjbHHU0Ae/AfKdGhHe+Gtc+A74gkyRnXJKz9SpXvV0oLe78AgOhFMLI57mgCxD6rDgTk/+cASPt2VgSUNA6AAABOCMEIAGIcB0AAAKgbN18AAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2x80XAAAALLZ27Wp5PKsa1PbYHSCP3fDkRGVm9lVGRlaD2gKxhGAEAAAQxZKTk60uAYgJBCMAAACLZWRkMWoDWIxrjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYHsEIAAAAgO0RjAAAAADYXpzVBQAAAAAIn7VrV8vjWdWgtvn5OyRJublTG9Q+M7OvMjKyGtS2sRGMAAAAANRLcnKy1SU0GoIRAAAAYCMZGVkRO2pjJa4xAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAthcRt+t+6KGHtH79eiUkJKhZs2a677771K1bN6vLAgAAAGATETFilJWVpby8PC1evFi33nqr7rrrLqtLAgAAAGAjETFidPHFFwf/3717dxUUFCgQCMjpjIjcBgAAACDGRUQw+rGXXnpJF110UYNCUWpqUiNUhMYQH++SJKWnt7C4EgCNhd9zAEA0CUswGjp0qH744Ydq561bt04uV+WX55tvvqm8vDy99NJLDerH6y1TIGAaXCfCp6LCL0kqLDxgcSUAGgu/5wCASOJ0OmodSAlLMFq4cGGdyyxfvlyPPfaYXnjhBaWlpYWhKgAAAACoFBGn0r333nt6+OGHtWDBArVt29bqcgAA/7F27Wp5PKsa1DY/f4ckKTd3aoPaZ2b2VUZGVoPaAgBwoiIiGE2cOFHx8fG64447gq+98MILSklJsbAqAMDJSE5OtroEAADqLSKC0fvvv291CQCAamRkZDFqAwCwBe6HDQBoFCUlxZoxY4pKS0usLgUAgDoRjAAAjSIvb6G2bNmsxYvfsLoUAADqRDACAIRcSUmx1qxZJWOMPJ5VjBoBACIewQgAEHJ5eQvl9/skST6fj1EjAEDEIxgBAEJu3TqPjKl84LYxRuvWeSyuCACA2hGMAAAhl5qaWus0AACRhmAEAAg5r9db6zQAAJGGYAQACLnevTMlOf4z5fjPNAAAkYtgBAAIuezsoYqLc0mS4uLilJMzzOKKAACoHcEIABBybneK+vS5SA6HQ3369FVystvqkgAAqFWc1QUAAGJTdvZQ7dr1PaNFAICo4DDH7qcaA7zeMgUCMfN2It7atavl8axqUNv8/B2SpPbtOzS4/8zMvsrIyGpwewAAANiH0+lQampSjfMZMYIlkpOTrS4BAAAACGLECAAAAEDMq2vEiJsvAAAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAaRUlJsWbMmKLS0hKrSwEAoE4EIwBAo8jLW6gtWzZr8eI3rC4FAIA6EYwAACFXUlKsNWtWyhijNWtWMWoEAIh4BCMAQMjl5S2Uz+eXJPl8PkaNAAARj2AEAAi5des8ksx/psx/pgEAiFwEIwBAyKWmptY6DQBApCEYAQBCzuv11joNAECkIRgBAEKud+9MORwOSZLD4VDv3pkWVwQAQO0IRgCAkMvOHiqXK06SFBcXp5ycYRZXBABA7QhGAICQc7tT1KdPXzkcDmVm9lVystvqkgAAqFWc1QUAAGJTdvZQ7dr1PaNFAICo4DDGmLoXiw5eb5kCgZh5OwAAAABCxOl0KDU1qeb5YawFAAAAACISwQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7UVUMPrggw/UuXNn/e1vf7O6FAAAAAA2EjHBqKysTI8++qiysrKsLgUAAACAzURMMJoxY4ZuuukmpaSkWF0KAAAAAJuJiGC0atUqHThwQAMHDrS6FAAAAAA2FBeOToYOHaoffvih2nlLly7VrFmztGDBgpPuJzU16aTXAQAAAMB+HMYYY2UBH330kW6//XY1bdpUklRcXKyEhASNHj1aY8eOPaF1eb1lCgQsfTsAAAAAIpDT6ah1IMXyYPRTEyZMUNeuXTVq1KgTbkswAgAAAFCduoJRRFxjBAAAAABWirgRo5PBiBEAAACA6jBiBAAAAAB1IBgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQCgUZSUFGvGjCkqLS2xuhQAAOpEMAIANIq8vIXasmWzFi9+w+pSAACoE8EIABByJSXF8nhWyRgjj2c1o0YAgIhHMAIAhFxe3kIFAkaSFAgEGDUCAEQ8ghEAIOTWr18rv98nSfL7fVq/fq3FFQEAUDuCEQAg5Hr1ypDLFSdJcrni1KtXhsUVAQBQO4IRACDksrOHyul0SJKcTqdycoZZXBEAALUjGAEAQs7tTlFmZl85HA5lZmYpOdltdUkAANQqzuoCAACxKTt7qHbt+p7RIgBAVHAYY4zVRYSK11sWvAsSAAAAABzjdDqUmppU8/ww1gIAAAAAEYlgBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD24qwuIJScTofVJQAAAACIQHVlBYcxxoSpFgAAAACISJxKBwAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbC/O6gJQ6b333tOcOXNkjJExRmPHjtWll16qs846S5988omaN29e7zbff/+9brvttuByBw4cUFlZmf7973/XWsPSpUv19NNPB6cLCgp0wQUXaO7cubW2O3z4sO6//35t3rxZxhideeaZmjZtmpKSkrR69Wo9+uijcjgc8vl86t+/v+688045HI4T/IQQStVtO126dDmh7ebYtllcXFxru9zcXC1btky7du1SXl6eOnXqdNy65s6dqyeeeCI4PxAI6LrrrtPhw4clSenp6XrooYfUtm1bSVJJSYmmTJmir776SnFxcbr88ss1duzYOuchNtS0XywuLtY999yj/Px8JSQkqEOHDpoyZYpatmyp77//XldddZU++OCDatf597//XX/5y1/kdDrlcrl07733qkePHpJq36Y+++wzTZo0SUePHtVpp52mRx55RKmpqY37AaBe+vXrp4SEBCUmJkqS7r77bvXp06fGn+e2bds0adIkFRYWKi4uTt26ddODDz6oJk2aSJKeeeYZLV68WHFxcWrevLmmTJmiM888s8b+a9vm6trHIfqtXLlSc+bMkc/nU3Jysh5++GG1a9dO77zzjmbNmqXExETNnj1bO3bsqPZvOUnatm2bJkyYoJKSErndbuXm5qpjx46SdNx6Hn30UX3//fdyOp1q1qyZHnjgAXXu3LnaZe+6665gnT/+zm7o348xxcBygUDA9OjRw2zevNkYY8ymTZtM9+7djd/vN506dTJlZWUn1Oanpk2bZh566KETrmvIkCHmX//6V53LLViwwNx+++0mEAiYQCBgbr/9djN//nxjjDFlZWXG5/MZY4wpLy83V111lXnnnXdOuBaETn23nbq2m5q2zZ+2+/DDD80PP/xgLr744mCfP7ZhwwZz0003HTd///79wf+/8MIL5rbbbgtO33rrrWbBggXB6b1799ZrHmJDTdtecXGxef/994PTM2bMMBMnTjTGGLNz507Ts2fPatdXVFRkzjvvPFNYWGiMMeadd94xl19+eXB+TduU3+83/fv3Nx9++KExxpgnn3zSTJgw4eTeHEKmpn1OTT/PnTt3mq+++soYU/mzHTdunJk7d64xxpiNGzeaiy66yBw8eNAYY8xf/vIXM2bMmFr7r22bM6b2fRyiW0lJienZs6f57rvvjDHG/POf/zS/+c1vjDHG3HTTTeatt94yxtT9fTx69Gjzz3/+M7iO0aNHB/v48XqMqbo9LV++3Fx55ZU1LvtjtX3XN/Tvx2jGqXQRwul06sCBA5IqE3qrVq3kdFb+eJ5//nkNGTJEl112mZYtW1avNseUl5crLy9PV111VfC1P/7xjxo2bJiys7N12223qbS09Lh6vvrqKxUUFKhfv36SpMLCQo0ePVrDhg3TFVdcoZkzZwaXdTgcOnLkiCoqKlRRUaFDhw6pdevWkqTmzZvL5XJJko4ePaqKiorjakT41bXtVLfdvP322xo4cKCGDBmiJ598str1VteuR48eatOmTY3LT5kyRZMnTz5uXosWLYL/LysrC9a3fft2ffPNN7rhhhuC89PT0+ucB+t9+umnuu6665STk6OcnBx5PB7l5ubqqquuUk5Ojm644Qbt2rUruPx7772nYcOGKScnR1deeaW+/vrr4Lzq9otut1sXXnhhcJnu3bvrhx9+qFLDjBkzlJ2drezsbH300UeSFDxSe/DgQUmVvxPH9mG1bVMbNmxQYmJicGRpxIgRWrp0acg+L4RebT/Ptm3bqkuXLpIq95HnnHNOcPtxOByqqKjQkSNHJFXdRqTKEcdj2/VVV12lffv2BedVt81JNe/jJGnVqlUaMWKEhg0bpuHDh+uzzz4Lzlu4cKGuueYaDRs2TNdff72+++67kHw2CJ0dO3YoLS1NP//5zyVJffv2lcfj0cSJE/Xxxx/r0Ucf1ejRoyXV/H3s9Xq1ceNGDR48WJI0ePBgbdy4UUVFRZo+ffpx6/np9nTszJzqlj2muu/s+syLaVYnM1Rat26d6dmzp7nooovMBRdcYD799FNjTOWR0SeeeMIYY8zWrVtNz549zb59+2pt82P/+te/TE5OTpXXvF5v8P+zZ882jzzyyHHtHnroITNt2rTg9JEjR4JHaMvLy83o0aPNqlWrgvPuvPNO06NHD9OjRw9z5513VlnXF198YQYPHmy6detmpk+fbgKBwAl/Pgituradn243hYWFpmfPnmbr1q3GGGPmz59f7VH76ra3Y6o7ejtz5kzzt7/9rcb5Y8aMMb179zZXXHGFKSgoMMZUHgkbMWKEmThxornyyivNmDFjzDfffFPnPFiruLjY9O7d23z88cfGGGN8Pp8pKSmpsj967bXXgvuP7777zvTu3dts27bNGGPM0aNHzYEDB4wxte8Xj/H7/eaGG24wf/nLX4wxlUfvO3XqZBYuXGiMMeb99983ffr0MUePHjXGGLN48WJz3nnnmb59+5o+ffqY7du3G2Nq36aWLl1qbr755ir9nnPOOaa4uDg0HxpOysUXX2yys7PN4MGDzYMPPmhKS0vrvY84fPiwGTRoUJUzHJ577jlz7rnnmszMTDNo0CBTVFRkjKnclvr37x8ceSorKzNHjhypc5szpvp93I4dO8y1114b3N6/+eYb07dvX2NM5Qj8zTffHFzHypUrzfDhw0P8yeFk7d+/3/Ts2dN8/vnnxhhjXnzxRdOpUyezYcMGM2rUKLNixYrgsjV9H3/55Zdm0KBBVdZ7+eWXmw0bNhhjzHHrMcaYe++91/Tt29dkZWVV2a6rW9aY2r+za5sXyzh0HwF8Pp/mzZunp556Su+9956efvpp3XnnncGjl9dcc40k6fTTT1eXLl302Wef1dnmmH/84x/Hpf1FixYFR4yWLFmiTZs2VZlfXl6uJUuWVGnn9/s1c+ZM5eTkaNiwYdqyZUvw6O26deskSR6PRx6PRxUVFXr++eeDbbt166a8vDytXLlSX331VZUjZgi/+mw7P91uPv/8c3Xp0kWnn366JGn48OHVrru67a0mn376qTZs2KCRI0fWuMyzzz6rNWvW6Iorrghe/xYIBPT5559r2LBhwSOnv/vd7+qcB2t99tlnOuOMM/TLX/5SkuRyuZScnKzVq1fr2muv1eDBg/X8888H90fr1q1TVlZW8Hz6hIQEJSUlBddX3X7xx6ZOnapmzZpp1KhRwdfi4+OVk5MjSbrwwgvVpEkTfffddyorK9NLL72kv//971q5cqUmTJigsWPHyhjDNhXFXnrpJS1evFj/+Mc/ZIzRlClT6vXz9Pl8uuuuu/SrX/1Kl1xyiSRp165devfdd/X2229rzZo1Gjp0qCZMmCCp8lqSIUOGBEeemjdvHryuqaZt7pjq9nFr1qxRfn6+fv3rX2vIkCG6++675fP5tG/fPq1YsUJff/21rrnmGg0ZMkSzZs1SQUFB436QOGEtWrTQY489pocffljDhg2T1+vVKaecEjyD5pj6/i1XX3/605+0cuVK3XXXXVXO7KlJbd/ZJ/J9HksIRhFg06ZN2rt3r84//3xJ0vnnn6+mTZtq69atJ9Vmz549+vDDD5WdnR187aOPPtIrr7yi5557Tnl5ebrzzjtVXl5eZd3Lly9X27ZtdfbZZwdfW7Bggfbv36/XX39deXl56t+/v44ePSpJevXVVzVgwAAlJiYqMTFRgwYNqvZi05YtWyorK4tTTSxW17ZT3XZTHyfa7sMPP9TWrVt1ySWXqF+/fiooKNBNN90kj8dTZTmn06mrr75aixYtkiS1adNGbdq0CZ6+dOmll6qwsFBFRUW1zkPk2bVrlx5++GHNmjVLS5Ys0fTp04/bHzVEbm6uduzYoccff7xep+56PB61aNEiGPwHDRqk/Px8FRcX17m9/fhUvaKiIjmdTrnd7pN+Dzh5x07hTUhI0MiRI/XJJ5/UuY/w+/26++67lZycrPvvvz+4rqVLl6pTp05q1aqVJOnKK6+s8UYeJ+qn+zhJ6tOnjxYtWhT85/F4lJaWJmOMrrrqquDrixcv1sqVK0NSB0Krd+/eeuWVV/TGG29o1KhROnLkiNq3b19lmdq+j9u0aaM9e/bI7/dLqtw29+7dW+Op6T92bPssLi6ucZnavrMb+ndALCAYRYDWrVuroKAgeBRp69at8nq9wV+gf/zjH5Iqz43euHGjunfvXmcbqfI85L59+yolJSX42v79+5WUlCS3263y8vLgun+suqMEBw4cUHp6uhITE7Vnzx69++67wXlt27aVx+MJHl1ds2ZN8E4927ZtUyAQkCQdOnRIq1evrvauZAifurad6rab7t27a+PGjdq+fbsk6fXXXz9uvdW1q80tt9wij8ejFStWaMWKFWrdurWef/55ZWZmqqioqEqYWbp0qc466yxJUteuXdWsWTNt2bJFUmXASk5OVkpKSq3zYK3u3btr69at+vTTTyVVfsnv3r1b8fHxSk9PVyAQ0KuvvhpcPiMjQ6tXrw5uc+Xl5SorKwvOr26/KEmzZ8/Whg0b9OSTTyohIaFKDRUVFcrLy5NUeZDoyJEjOv3009W2bVtt3LhRXq9XkvT+++8rKSmpzm2qa9euOnLkSHAU/NVXX9XAgQND/dGhAQ4dOhS8bsMYo7feekudO3eu9ecZCAQ0YcIEuVwu/elPf6py99S2bdvq448/1qFDhyRVXgN07Hvuoosu0qJFi4LXFR08eDB44LCmba62fVxGRobWrFkTrFGSvvjiC0mVd9pbtGhRcJTI7/drw4YNIf70EAqFhYWSKs9kmD17tkaMGKFmzZpVWaa27+PU1FR17txZS5YskSQtWbJEnTt3VsuWLY/r6+DBg9q9e3dwesWKFUpOTq71IE1t39kn+n0eS7hddwRIT0/X5MmTNW7cuCoXyx3boP1+v6688kodPnxYU6ZMCd4KtrY2UuWGfd9991Xpq0+fPlq8eLEuu+wypaSkqEePHvryyy+D83fv3q1PPvlEjz/+eJV2o0eP1rhx4zR48GCdeuqp6tWrV3DebbfdpkmTJgUvEDz77LODpya8++67WrhwoVwulwKBgPr37x88BQbWqGt7q267SU1N1dSpU/Xb3/5WTZo0Cd5K9MeqaydJ06ZN09tvv619+/bpxhtvlNvt1ptvvllrjYWFhZo4caIqKiokKXgbZKnyIujp06dr4sSJKi8vV9OmTTV37twq76WmebCO2+3WE088oRkzZujQoUNyOp0aP368Bg4cqEGDBiklJUV9+/YNhoyOHTtq6tSpuuuuu+T3++VyuTRjxozgH4/V7Re3bNmiefPmqWPHjhoxYoSkyj9oj90sxO126+uvv9Zzzz0nqTJEJSQkqGvXrhozZoxGjRql+Ph4JSQkaM6cOXVuUw6HQzNnztSDDz5Y5XbdsJ7X6/1/7dw9aJNbHMfxb2JN7dKiYsVOgmgF40sDKtJiEYlEqlU7uai4CDZUCzpUHFKQIqWCYumLOiguCoJiFIVW0Lq1qBADLtLBRWmRQgcJJtbe4XKD5Vpd7jXRfD/Tw/M/5JwTwnP4cc4T2tramJmZ4evXr6xatYpEIvHD58fIyAjJZJI1a9bQ0tICQCQSIZFIsGvXrvwRvFAoRGVlJefPnwf+PiJ37Ngxjh49SiAQIBQKMTg4CMz/m/vRM27lypX09PRw9uzZ/B8bRSIRNmzYwObNm2lvb+f48ePMzMyQy+WIxWKEw+Ff/RXrJy5dusSrV6/I5XLU19dz+vTpf7X52Xrc2dlJR0cH/f39VFZW0t3d/d2+MpkMJ0+eJJPJEAwGqaqqYnBw8Idr33xr9s9qf7rA7OzsbKEHIUmSJEmF5FE6SZIkSSXPYCRJkiSp5BmMJEmSJJU8g5EkSZKkkmcwkiRJklTyDEaSpN9GR0cHFy9eLPQwJEl/IIORJEmSpJJnMJIk/fa+fPlS6CFIkn5zBiNJUtF68+YNBw4coK6ujvb2dj5//gzA6Ogo27dv5+rVq9TX13PmzBmy2SxdXV00NDTQ0NBAV1cX2Ww2/1nXrl3L1+7cuUNtbS3v3r0D4NmzZ+zfv59IJEJjYyO9vb0Fma8kqXAMRpKkopTNZonH4+zbt4+xsTFisRhDQ0P5+sePH5menubp06ecO3eOgYEBUqkU9+/fJ5lMkk6n6e/vB+D58+fcuHGD69evMzw8zOjo6Jy+Kioq6O7u5sWLF1y5coVbt27x5MmTXzpfSVJhGYwkSUUplUqRy+U4cuQICxcuJBaLsX79+nw9GAxy4sQJQqEQixYt4sGDB8TjcZYuXcqSJUuIx+Mkk0kAHj9+TEtLC6tXr6aiooK2trY5fW3dupXa2lqCwSBr166lqamJsbGxXzpfSVJhlRV6AJIkfc/k5CTLly8nEAjk79XU1OSvFy9eTHl5+Zz239ZramqYnJzM18LhcL62YsWKOX2lUikuXLjA27dvyeVyZLNZYrHYfz4nSVLxcsdIklSUli1bxsTEBLOzs/l779+/z19/G5gAqqur59Q/fPhAdXV1vjYxMTGn9q1Tp06xc+dORkZGePnyJQcPHpzTryTpz2cwkiQVpU2bNlFWVsbNmzfJ5XIMDQ2RTqfnbd/U1MTAwABTU1NMTU3R19fH3r17AYjFYty9e5fx8XEymUz+3aN/fPr0iaqqKsrLy3n9+jUPHz78X+cmSSo+BiNJUlEKhUL09vZy7949tmzZwqNHj4hGo/O2b21tJRwO09zcTHNzM+vWraO1tRWAxsZGDh06xOHDh4lGo2zcuDHfB0AikeDy5cvU1dXR19fH7t27//8JSpKKSmDWswKSpBIzPj7O/zNVJQAAAHhJREFUnj17SKfTlJX5uq0kyR0jSVKJGB4eJpvNMj09TU9PDzt27DAUSZLyDEaSpJJw+/Zttm3bRjQaZcGCBXR2dhZ6SJKkIuJROkmSJEklzx0jSZIkSSXPYCRJkiSp5BmMJEmSJJU8g5EkSZKkkmcwkiRJklTy/gImqkLRkmd70QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pNeUTqqvXtP"
      },
      "source": [
        "##Desafios"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ohSLNuWva2k"
      },
      "source": [
        "###Desfio 01: Encontra o top 10 das ações do MoA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQLpCc29vaEp"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sljT8JYOwB-t"
      },
      "source": [
        "###Desafio 02: Criar coluna eh_controle que terá valor `1` quando a variável *tratamento* for igual a *com_controle*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVhwp1-bwyid"
      },
      "source": [
        "###Desafio 03: Criar uma coluna para cada categoria de valor da variável *tempo*, assumindo valor `1` quando valor do *tempo* for igual ao nome da variável ou `0` caso contrário"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_TVVkGxxts1"
      },
      "source": [
        "###Desafio 04: Estudar sobre o merge de dataframes no pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6a9Fi2u8YmW"
      },
      "source": [
        "###Desafio 05: Fazer a análise considerando tempo e dose para comparar as distribuições (escolher uma droga e comparar com o controle)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXz55_o78jEV"
      },
      "source": [
        "###Desafio 06: Descobrir se temos algum composto que, dependo da configuração do experiemnto, ativou algum MoA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKSjBAw39CAW"
      },
      "source": [
        "###Desafio 07: Descobrir se temos algum composto que, dependo da configuração do experiemnto, ativou MoAs diferentes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhHyBeypwo5U"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}