Aula_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aula 4 Imersão Dados Alura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importação Dos Pacotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importação Dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_exp = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
    "url_results = 'https://github.com/alura-cursos/imersao-dados-desafio-final/blob/main/Dados/dados_resultados.csv?raw=true'\n",
    "\n",
    "dados_exp = pd.read_csv(url_exp, compression = 'zip')\n",
    "dados_result = pd.read_csv(url_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise e Limpeza Dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tratamento</th>\n",
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       "      <td>-1.4080</td>\n",
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       "<p>5 rows × 877 columns</p>\n",
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       "             id tratamento  tempo dose      droga     g-0     g-1     g-2  \\\n",
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       "1  id_000779bfc  com_droga     72   D1  df89a8e5a  0.0743  0.4087  0.2991   \n",
       "2  id_000a6266a  com_droga     48   D1  18bb41b2c  0.6280  0.5817  1.5540   \n",
       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "      g-3     g-4  ...    c-90    c-91    c-92    c-93    c-94    c-95  \\\n",
       "0 -0.6208 -0.1944  ...  0.2862  0.2584  0.8076  0.5523 -0.1912  0.6584   \n",
       "1  0.0604  1.0190  ... -0.4265  0.7543  0.4708  0.0230  0.2957  0.4899   \n",
       "2 -0.0764 -0.0323  ... -0.7250 -0.6297  0.6103  0.0223 -1.3240 -0.3174   \n",
       "3  0.5288  4.0620  ... -2.0990 -0.6441 -5.6300 -1.3780 -0.8632 -1.2880   \n",
       "4  0.6919  1.4180  ...  0.0042  0.0048  0.6670  1.0690  0.5523 -0.3031   \n",
       "\n",
       "     c-96    c-97    c-98    c-99  \n",
       "0 -0.3981  0.2139  0.3801  0.4176  \n",
       "1  0.1522  0.1241  0.6077  0.7371  \n",
       "2 -0.6417 -0.2187 -1.4080  0.6931  \n",
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       "4  0.1094  0.2885 -0.3786  0.7125  \n",
       "\n",
       "[5 rows x 877 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dados_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "             id  5-alpha_reductase_inhibitor  11-beta-hsd1_inhibitor  \\\n",
       "0  id_000644bb2                            0                       0   \n",
       "1  id_000779bfc                            0                       0   \n",
       "2  id_000a6266a                            0                       0   \n",
       "3  id_0015fd391                            0                       0   \n",
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       "\n",
       "   acat_inhibitor  acetylcholine_receptor_agonist  \\\n",
       "0               0                               0   \n",
       "1               0                               0   \n",
       "2               0                               0   \n",
       "3               0                               0   \n",
       "4               0                               0   \n",
       "\n",
       "   acetylcholine_receptor_antagonist  acetylcholinesterase_inhibitor  \\\n",
       "0                                  0                               0   \n",
       "1                                  0                               0   \n",
       "2                                  0                               0   \n",
       "3                                  0                               0   \n",
       "4                                  0                               0   \n",
       "\n",
       "   adenosine_receptor_agonist  adenosine_receptor_antagonist  \\\n",
       "0                           0                              0   \n",
       "1                           0                              0   \n",
       "2                           0                              0   \n",
       "3                           0                              0   \n",
       "4                           0                              0   \n",
       "\n",
       "   adenylyl_cyclase_activator  ...  tropomyosin_receptor_kinase_inhibitor  \\\n",
       "0                           0  ...                                      0   \n",
       "1                           0  ...                                      0   \n",
       "2                           0  ...                                      0   \n",
       "3                           0  ...                                      0   \n",
       "4                           0  ...                                      0   \n",
       "\n",
       "   trpv_agonist  trpv_antagonist  tubulin_inhibitor  \\\n",
       "0             0                0                  0   \n",
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       "   tyrosine_kinase_inhibitor  ubiquitin_specific_protease_inhibitor  \\\n",
       "0                          0                                      0   \n",
       "1                          0                                      0   \n",
       "2                          0                                      0   \n",
       "3                          0                                      0   \n",
       "4                          0                                      0   \n",
       "\n",
       "   vegfr_inhibitor  vitamin_b  vitamin_d_receptor_agonist  wnt_inhibitor  \n",
       "0                0          0                           0              0  \n",
       "1                0          0                           0              0  \n",
       "2                0          0                           0              0  \n",
       "3                0          0                           0              0  \n",
       "4                0          0                           0              0  \n",
       "\n",
       "[5 rows x 207 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_exp.columns = dados_exp.columns.str.replace('-', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_exp.rename(columns = {'droga':'composto'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>c98</th>\n",
       "      <th>c99</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>...</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>...</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>...</td>\n",
       "      <td>-0.7250</td>\n",
       "      <td>-0.6297</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>-1.3240</td>\n",
       "      <td>-0.3174</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
       "      <td>0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>8c7f86626</td>\n",
       "      <td>-0.5138</td>\n",
       "      <td>-0.2491</td>\n",
       "      <td>-0.2656</td>\n",
       "      <td>0.5288</td>\n",
       "      <td>4.0620</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0990</td>\n",
       "      <td>-0.6441</td>\n",
       "      <td>-5.6300</td>\n",
       "      <td>-1.3780</td>\n",
       "      <td>-0.8632</td>\n",
       "      <td>-1.2880</td>\n",
       "      <td>-1.6210</td>\n",
       "      <td>-0.8784</td>\n",
       "      <td>-0.3876</td>\n",
       "      <td>-0.8154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D2</td>\n",
       "      <td>7cbed3131</td>\n",
       "      <td>-0.3254</td>\n",
       "      <td>-0.4009</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>0.6919</td>\n",
       "      <td>1.4180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>1.0690</td>\n",
       "      <td>0.5523</td>\n",
       "      <td>-0.3031</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>-0.3786</td>\n",
       "      <td>0.7125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id tratamento  tempo dose   composto      g0      g1      g2  \\\n",
       "0  id_000644bb2  com_droga     24   D1  b68db1d53  1.0620  0.5577 -0.2479   \n",
       "1  id_000779bfc  com_droga     72   D1  df89a8e5a  0.0743  0.4087  0.2991   \n",
       "2  id_000a6266a  com_droga     48   D1  18bb41b2c  0.6280  0.5817  1.5540   \n",
       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "       g3      g4  ...     c90     c91     c92     c93     c94     c95  \\\n",
       "0 -0.6208 -0.1944  ...  0.2862  0.2584  0.8076  0.5523 -0.1912  0.6584   \n",
       "1  0.0604  1.0190  ... -0.4265  0.7543  0.4708  0.0230  0.2957  0.4899   \n",
       "2 -0.0764 -0.0323  ... -0.7250 -0.6297  0.6103  0.0223 -1.3240 -0.3174   \n",
       "3  0.5288  4.0620  ... -2.0990 -0.6441 -5.6300 -1.3780 -0.8632 -1.2880   \n",
       "4  0.6919  1.4180  ...  0.0042  0.0048  0.6670  1.0690  0.5523 -0.3031   \n",
       "\n",
       "      c96     c97     c98     c99  \n",
       "0 -0.3981  0.2139  0.3801  0.4176  \n",
       "1  0.1522  0.1241  0.6077  0.7371  \n",
       "2 -0.6417 -0.2187 -1.4080  0.6931  \n",
       "3 -1.6210 -0.8784 -0.3876 -0.8154  \n",
       "4  0.1094  0.2885 -0.3786  0.7125  \n",
       "\n",
       "[5 rows x 877 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_exp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 01: Encontrar o top 10, das ações do MOA (inibidor, agonista...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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>MOA</th>\n",
       "      <th>quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nfkb_inhibitor</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>proteasome_inhibitor</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cyclooxygenase_inhibitor</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dopamine_receptor_antagonist</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>serotonin_receptor_antagonist</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>protein_phosphatase_inhibitor</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>autotaxin_inhibitor</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>diuretic</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>erbb2_inhibitor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>atp-sensitive_potassium_channel_antagonist</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            MOA  quantidade\n",
       "0                                nfkb_inhibitor         832\n",
       "1                          proteasome_inhibitor         726\n",
       "2                      cyclooxygenase_inhibitor         435\n",
       "3                  dopamine_receptor_antagonist         424\n",
       "4                 serotonin_receptor_antagonist         404\n",
       "..                                          ...         ...\n",
       "201               protein_phosphatase_inhibitor           6\n",
       "202                         autotaxin_inhibitor           6\n",
       "203                                    diuretic           6\n",
       "204                             erbb2_inhibitor           1\n",
       "205  atp-sensitive_potassium_channel_antagonist           1\n",
       "\n",
       "[206 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_moa = dados_result.drop('id', axis=1).sum().sort_values(ascending=False).reset_index()\n",
    "dados_moa.columns = ['MOA', 'quantidade']\n",
    "dados_moa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['inhibitor', 'inhibitor', 'inhibitor', 'antagonist', 'antagonist']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_acao = []\n",
    "\n",
    "moa_split = dados_moa['MOA'].str.split('_')\n",
    "for row in moa_split:\n",
    "    list_acao.append(row[-1])\n",
    "list_acao[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>MOA</th>\n",
       "      <th>quantidade</th>\n",
       "      <th>acao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nfkb_inhibitor</td>\n",
       "      <td>832</td>\n",
       "      <td>inhibitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>proteasome_inhibitor</td>\n",
       "      <td>726</td>\n",
       "      <td>inhibitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cyclooxygenase_inhibitor</td>\n",
       "      <td>435</td>\n",
       "      <td>inhibitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dopamine_receptor_antagonist</td>\n",
       "      <td>424</td>\n",
       "      <td>antagonist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>serotonin_receptor_antagonist</td>\n",
       "      <td>404</td>\n",
       "      <td>antagonist</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             MOA  quantidade        acao\n",
       "0                 nfkb_inhibitor         832   inhibitor\n",
       "1           proteasome_inhibitor         726   inhibitor\n",
       "2       cyclooxygenase_inhibitor         435   inhibitor\n",
       "3   dopamine_receptor_antagonist         424  antagonist\n",
       "4  serotonin_receptor_antagonist         404  antagonist"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_moa['acao'] = list_acao\n",
    "dados_moa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>acao</th>\n",
       "      <th>quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>inhibitor</td>\n",
       "      <td>9693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antagonist</td>\n",
       "      <td>3449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>agonist</td>\n",
       "      <td>2330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blocker</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>agent</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>activator</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>local</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>antioxidant</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>anti-inflammatory</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>immunosuppressant</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                acao  quantidade\n",
       "0          inhibitor        9693\n",
       "1         antagonist        3449\n",
       "2            agonist        2330\n",
       "3            blocker         323\n",
       "4              agent         150\n",
       "5          activator         115\n",
       "6              local          80\n",
       "7        antioxidant          73\n",
       "8  anti-inflammatory          73\n",
       "9  immunosuppressant          73"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_acoes = dados_moa.groupby('acao').agg({'quantidade':'sum'}).sort_values('quantidade', ascending=False).head(10)\n",
    "top_10_acoes = top_10_acoes.reset_index()\n",
    "top_10_acoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando a figura\n",
    "fig, ax = plt.subplots(figsize = (12,8))\n",
    "\n",
    "#Setando a cor\n",
    "sns.set_palette(sns.color_palette(\"winter\"),10)\n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#Criando o gráfico \n",
    "sns.barplot(data=top_10_acoes, x='acao', y='quantidade', ax=ax)\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "fig.suptitle('Top 10 Ações Do Moa', fontsize=20)\n",
    "ax.set_xlabel('Ação', fontsize=12)\n",
    "ax.set_ylabel('Quantidade De Ativações', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "#Adicionando os valores acima da barra\n",
    "for index, row in top_10_acoes.iterrows():\n",
    "    ax.text(index, row['quantidade'], row['quantidade'], color='black', ha=\"center\", fontsize=15)\n",
    "plt.xticks(rotation= 45)\n",
    "\n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 02: Criar a coluna eh_controle para quando na linha tratamento == com_controle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>composto</th>\n",
       "      <th>g0</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "      <th>g4</th>\n",
       "      <th>...</th>\n",
       "      <th>c91</th>\n",
       "      <th>c92</th>\n",
       "      <th>c93</th>\n",
       "      <th>c94</th>\n",
       "      <th>c95</th>\n",
       "      <th>c96</th>\n",
       "      <th>c97</th>\n",
       "      <th>c98</th>\n",
       "      <th>c99</th>\n",
       "      <th>eh_controle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23804</th>\n",
       "      <td>id_fff506720</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>74515bfd2</td>\n",
       "      <td>-1.0140</td>\n",
       "      <td>0.1709</td>\n",
       "      <td>-0.4291</td>\n",
       "      <td>1.8750</td>\n",
       "      <td>0.9859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>-1.5510</td>\n",
       "      <td>-1.2800</td>\n",
       "      <td>-0.7632</td>\n",
       "      <td>-0.6736</td>\n",
       "      <td>-0.4097</td>\n",
       "      <td>0.2282</td>\n",
       "      <td>-0.3213</td>\n",
       "      <td>-0.0836</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23805</th>\n",
       "      <td>id_fff6df1c5</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>5628cb3ee</td>\n",
       "      <td>1.7380</td>\n",
       "      <td>-1.2900</td>\n",
       "      <td>-0.4533</td>\n",
       "      <td>-1.1640</td>\n",
       "      <td>-0.4841</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5974</td>\n",
       "      <td>-0.2727</td>\n",
       "      <td>0.1188</td>\n",
       "      <td>1.0770</td>\n",
       "      <td>-1.0250</td>\n",
       "      <td>0.1516</td>\n",
       "      <td>0.4503</td>\n",
       "      <td>-0.6312</td>\n",
       "      <td>-0.8302</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23806</th>\n",
       "      <td>id_fff790a17</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>47dd8f190</td>\n",
       "      <td>-0.1150</td>\n",
       "      <td>-0.8037</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>-0.1301</td>\n",
       "      <td>0.2013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1086</td>\n",
       "      <td>-0.1686</td>\n",
       "      <td>-0.3482</td>\n",
       "      <td>0.4121</td>\n",
       "      <td>0.5728</td>\n",
       "      <td>1.2630</td>\n",
       "      <td>0.7823</td>\n",
       "      <td>-0.6947</td>\n",
       "      <td>0.7277</td>\n",
       "      <td>0</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>...</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",
       "      <td>0</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>...</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",
       "      <td>0</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>...</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>0</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>...</td>\n",
       "      <td>0.4426</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>-0.3195</td>\n",
       "      <td>-0.8086</td>\n",
       "      <td>-0.9798</td>\n",
       "      <td>-0.2084</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>-0.2715</td>\n",
       "      <td>0.3689</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>0.3942</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>-0.7389</td>\n",
       "      <td>0.5505</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.7343</td>\n",
       "      <td>0.2807</td>\n",
       "      <td>0.4116</td>\n",
       "      <td>0.6422</td>\n",
       "      <td>0.2256</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.3808</td>\n",
       "      <td>1</td>\n",
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       "    <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>...</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>-0.2012</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>1.5230</td>\n",
       "      <td>0.7101</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23813</th>\n",
       "      <td>id_ffffdd77b</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>972f41291</td>\n",
       "      <td>-0.8598</td>\n",
       "      <td>1.0240</td>\n",
       "      <td>-0.1361</td>\n",
       "      <td>0.7952</td>\n",
       "      <td>-0.3611</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.7450</td>\n",
       "      <td>-6.6300</td>\n",
       "      <td>-4.0950</td>\n",
       "      <td>-7.3860</td>\n",
       "      <td>-1.4160</td>\n",
       "      <td>-3.5770</td>\n",
       "      <td>-0.4775</td>\n",
       "      <td>-2.1500</td>\n",
       "      <td>-4.2520</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 878 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose   composto      g0      g1  \\\n",
       "23804  id_fff506720     com_droga     24   D1  74515bfd2 -1.0140  0.1709   \n",
       "23805  id_fff6df1c5     com_droga     48   D2  5628cb3ee  1.7380 -1.2900   \n",
       "23806  id_fff790a17     com_droga     48   D2  47dd8f190 -0.1150 -0.8037   \n",
       "23807  id_fff7e6992     com_droga     48   D1  a28556d51  0.1420 -0.3696   \n",
       "23808  id_fff8c2444     com_droga     72   D1  6c3a459be  0.1608 -1.0500   \n",
       "23809  id_fffb1ceed     com_droga     24   D2  df1d0a5a1  0.1394 -0.0636   \n",
       "23810  id_fffb70c0c     com_droga     24   D2  ecf3b6b74 -1.3260  0.3478   \n",
       "23811  id_fffc1c3f4  com_controle     48   D2  cacb2b860  0.3942  0.3756   \n",
       "23812  id_fffcb9e7c     com_droga     24   D1  8b87a7a83  0.6660  0.2324   \n",
       "23813  id_ffffdd77b     com_droga     72   D1  972f41291 -0.8598  1.0240   \n",
       "\n",
       "           g2      g3      g4  ...     c91     c92     c93     c94     c95  \\\n",
       "23804 -0.4291  1.8750  0.9859  ...  0.3175 -1.5510 -1.2800 -0.7632 -0.6736   \n",
       "23805 -0.4533 -1.1640 -0.4841  ...  0.5974 -0.2727  0.1188  1.0770 -1.0250   \n",
       "23806  0.0988 -0.1301  0.2013  ...  0.1086 -0.1686 -0.3482  0.4121  0.5728   \n",
       "23807 -0.0093 -0.2495 -0.0175  ...  0.5230  0.1589  0.7715  0.5497  0.6929   \n",
       "23808  0.2551 -0.2239 -0.2431  ...  0.3538  0.0558  0.3377 -0.4753 -0.2504   \n",
       "23809 -0.1112 -0.5080 -0.4713  ...  0.0262 -0.8121  0.3434  0.5372 -0.3246   \n",
       "23810 -0.3743  0.9905 -0.7178  ...  0.4426  0.0423 -0.3195 -0.8086 -0.9798   \n",
       "23811  0.3109 -0.7389  0.5505  ...  0.3755  0.7343  0.2807  0.4116  0.6422   \n",
       "23812  0.4392  0.2044  0.8531  ...  0.4258 -0.2012  0.1506  1.5230  0.7101   \n",
       "23813 -0.1361  0.7952 -0.3611  ... -1.7450 -6.6300 -4.0950 -7.3860 -1.4160   \n",
       "\n",
       "          c96     c97     c98     c99  eh_controle  \n",
       "23804 -0.4097  0.2282 -0.3213 -0.0836            0  \n",
       "23805  0.1516  0.4503 -0.6312 -0.8302            0  \n",
       "23806  1.2630  0.7823 -0.6947  0.7277            0  \n",
       "23807  0.3738  0.8636  0.4494  0.1218            0  \n",
       "23808 -0.7415  0.8413 -0.4259  0.2434            0  \n",
       "23809  0.0631  0.9171  0.5258  0.4680            0  \n",
       "23810 -0.2084 -0.1224 -0.2715  0.3689            0  \n",
       "23811  0.2256  0.7592  0.6656  0.3808            1  \n",
       "23812  0.1732  0.7015 -0.6290  0.0740            0  \n",
       "23813 -3.5770 -0.4775 -2.1500 -4.2520            0  \n",
       "\n",
       "[10 rows x 878 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapa = {'com_controle':1, 'com_droga':0}\n",
    "dados_exp['eh_controle'] = dados_exp['tratamento'].map(mapa)\n",
    "dados_exp.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 03: Criar três colunas para indicar o tempo 24, 48, 72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
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       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
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       "      <th>1</th>\n",
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       "      <td>D1</td>\n",
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       "      <td>0.0604</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-0.0764</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>-1.3240</td>\n",
       "      <td>-0.3174</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
       "      <td>0.6931</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_0015fd391</td>\n",
       "      <td>com_droga</td>\n",
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       "      <td>D1</td>\n",
       "      <td>8c7f86626</td>\n",
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       "      <td>-0.2491</td>\n",
       "      <td>-0.2656</td>\n",
       "      <td>0.5288</td>\n",
       "      <td>4.0620</td>\n",
       "      <td>...</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",
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       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
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       "      <td>72</td>\n",
       "      <td>D2</td>\n",
       "      <td>7cbed3131</td>\n",
       "      <td>-0.3254</td>\n",
       "      <td>-0.4009</td>\n",
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       "      <td>1.4180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6670</td>\n",
       "      <td>1.0690</td>\n",
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       "<p>5 rows × 879 columns</p>\n",
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      "text/plain": [
       "             id tratamento  tempo dose   composto      g0      g1      g2  \\\n",
       "0  id_000644bb2  com_droga     24   D1  b68db1d53  1.0620  0.5577 -0.2479   \n",
       "1  id_000779bfc  com_droga     72   D1  df89a8e5a  0.0743  0.4087  0.2991   \n",
       "2  id_000a6266a  com_droga     48   D1  18bb41b2c  0.6280  0.5817  1.5540   \n",
       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "       g3      g4  ...     c92     c93     c94     c95     c96     c97  \\\n",
       "0 -0.6208 -0.1944  ...  0.8076  0.5523 -0.1912  0.6584 -0.3981  0.2139   \n",
       "1  0.0604  1.0190  ...  0.4708  0.0230  0.2957  0.4899  0.1522  0.1241   \n",
       "2 -0.0764 -0.0323  ...  0.6103  0.0223 -1.3240 -0.3174 -0.6417 -0.2187   \n",
       "3  0.5288  4.0620  ... -5.6300 -1.3780 -0.8632 -1.2880 -1.6210 -0.8784   \n",
       "4  0.6919  1.4180  ...  0.6670  1.0690  0.5523 -0.3031  0.1094  0.2885   \n",
       "\n",
       "      c98     c99  eh_controle  24_h  \n",
       "0  0.3801  0.4176            0     1  \n",
       "1  0.6077  0.7371            0     0  \n",
       "2 -1.4080  0.6931            0     0  \n",
       "3 -0.3876 -0.8154            0     0  \n",
       "4 -0.3786  0.7125            0     0  \n",
       "\n",
       "[5 rows x 879 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapa = {24:1, 48:0, 72:0}\n",
    "dados_exp['24_h'] = dados_exp['tempo'].map(mapa)\n",
    "dados_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "             id tratamento  tempo dose   composto      g0      g1      g2  \\\n",
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       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "       g3      g4  ...     c93     c94     c95     c96     c97     c98  \\\n",
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       "4  0.6919  1.4180  ...  1.0690  0.5523 -0.3031  0.1094  0.2885 -0.3786   \n",
       "\n",
       "      c99  eh_controle  24_h  48_h  \n",
       "0  0.4176            0     1     0  \n",
       "1  0.7371            0     0     0  \n",
       "2  0.6931            0     0     1  \n",
       "3 -0.8154            0     0     1  \n",
       "4  0.7125            0     0     0  \n",
       "\n",
       "[5 rows x 880 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapa = {24:0, 48:1, 72:0}\n",
    "dados_exp['48_h'] = dados_exp['tempo'].map(mapa)\n",
    "dados_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "             id tratamento  tempo dose   composto      g0      g1      g2  \\\n",
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       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "       g3      g4  ...     c94     c95     c96     c97     c98     c99  \\\n",
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       "4  0.6919  1.4180  ...  0.5523 -0.3031  0.1094  0.2885 -0.3786  0.7125   \n",
       "\n",
       "   eh_controle  24_h  48_h  72_h  \n",
       "0            0     1     0     0  \n",
       "1            0     0     0     1  \n",
       "2            0     0     1     0  \n",
       "3            0     0     1     0  \n",
       "4            0     0     0     1  \n",
       "\n",
       "[5 rows x 881 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapa = {24:0, 48:0, 72:1}\n",
    "dados_exp['72_h'] = dados_exp['tempo'].map(mapa)\n",
    "dados_exp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando colunas com o números de mecanismos ativados e se teve ou não algum ativado "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "             id  5-alpha_reductase_inhibitor  11-beta-hsd1_inhibitor  \\\n",
       "0  id_000644bb2                            0                       0   \n",
       "1  id_000779bfc                            0                       0   \n",
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       "2               0                               0   \n",
       "3               0                               0   \n",
       "4               0                               0   \n",
       "\n",
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       "0                                  0                               0   \n",
       "1                                  0                               0   \n",
       "2                                  0                               0   \n",
       "3                                  0                               0   \n",
       "4                                  0                               0   \n",
       "\n",
       "   adenosine_receptor_agonist  adenosine_receptor_antagonist  \\\n",
       "0                           0                              0   \n",
       "1                           0                              0   \n",
       "2                           0                              0   \n",
       "3                           0                              0   \n",
       "4                           0                              0   \n",
       "\n",
       "   adenylyl_cyclase_activator  ...  trpv_agonist  trpv_antagonist  \\\n",
       "0                           0  ...             0                0   \n",
       "1                           0  ...             0                0   \n",
       "2                           0  ...             0                0   \n",
       "3                           0  ...             0                0   \n",
       "4                           0  ...             0                0   \n",
       "\n",
       "   tubulin_inhibitor  tyrosine_kinase_inhibitor  \\\n",
       "0                  0                          0   \n",
       "1                  0                          0   \n",
       "2                  0                          0   \n",
       "3                  0                          0   \n",
       "4                  0                          0   \n",
       "\n",
       "   ubiquitin_specific_protease_inhibitor  vegfr_inhibitor  vitamin_b  \\\n",
       "0                                      0                0          0   \n",
       "1                                      0                0          0   \n",
       "2                                      0                0          0   \n",
       "3                                      0                0          0   \n",
       "4                                      0                0          0   \n",
       "\n",
       "   vitamin_d_receptor_agonist  wnt_inhibitor  n_moa  \n",
       "0                           0              0      1  \n",
       "1                           0              0      0  \n",
       "2                           0              0      3  \n",
       "3                           0              0      0  \n",
       "4                           0              0      1  \n",
       "\n",
       "[5 rows x 208 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_result['n_moa'] = dados_result.drop('id', axis=1).sum(axis=1)\n",
    "dados_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "             id  5-alpha_reductase_inhibitor  11-beta-hsd1_inhibitor  \\\n",
       "0  id_000644bb2                            0                       0   \n",
       "1  id_000779bfc                            0                       0   \n",
       "2  id_000a6266a                            0                       0   \n",
       "3  id_0015fd391                            0                       0   \n",
       "4  id_001626bd3                            0                       0   \n",
       "\n",
       "   acat_inhibitor  acetylcholine_receptor_agonist  \\\n",
       "0               0                               0   \n",
       "1               0                               0   \n",
       "2               0                               0   \n",
       "3               0                               0   \n",
       "4               0                               0   \n",
       "\n",
       "   acetylcholine_receptor_antagonist  acetylcholinesterase_inhibitor  \\\n",
       "0                                  0                               0   \n",
       "1                                  0                               0   \n",
       "2                                  0                               0   \n",
       "3                                  0                               0   \n",
       "4                                  0                               0   \n",
       "\n",
       "   adenosine_receptor_agonist  adenosine_receptor_antagonist  \\\n",
       "0                           0                              0   \n",
       "1                           0                              0   \n",
       "2                           0                              0   \n",
       "3                           0                              0   \n",
       "4                           0                              0   \n",
       "\n",
       "   adenylyl_cyclase_activator  ...  trpv_antagonist  tubulin_inhibitor  \\\n",
       "0                           0  ...                0                  0   \n",
       "1                           0  ...                0                  0   \n",
       "2                           0  ...                0                  0   \n",
       "3                           0  ...                0                  0   \n",
       "4                           0  ...                0                  0   \n",
       "\n",
       "   tyrosine_kinase_inhibitor  ubiquitin_specific_protease_inhibitor  \\\n",
       "0                          0                                      0   \n",
       "1                          0                                      0   \n",
       "2                          0                                      0   \n",
       "3                          0                                      0   \n",
       "4                          0                                      0   \n",
       "\n",
       "   vegfr_inhibitor  vitamin_b  vitamin_d_receptor_agonist  wnt_inhibitor  \\\n",
       "0                0          0                           0              0   \n",
       "1                0          0                           0              0   \n",
       "2                0          0                           0              0   \n",
       "3                0          0                           0              0   \n",
       "4                0          0                           0              0   \n",
       "\n",
       "   n_moa  ativo_moa  \n",
       "0      1       True  \n",
       "1      0      False  \n",
       "2      3       True  \n",
       "3      0      False  \n",
       "4      1       True  \n",
       "\n",
       "[5 rows x 209 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_result['ativo_moa'] = dados_result['n_moa'] != 0\n",
    "dados_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 04: Estudar sobre combinações de DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para fazer uma concatenação com um dataframe do pandas temos diversas opções, tanto horizontalmente e verticalmente, temos a função pandas.merge com diversos parâmetros para principalmente concatenação horizontal, ela também pode ser usada diretamente no dataframe sem criar um novo objeto(pandas.DataFrame.merge), para o mesmo fim temos a pd.DataFrame.join que apresenta menos flexibilidade."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Além disso pandas oferece o pd.concat, que serve tanto para horizaltal e vertical, e para uma concatenação rápida verticalmente oferece o pandas.DataFrame.append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_comb = dados_exp.merge(dados_result[['id', 'n_moa', 'ativo_moa']], on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "    </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>...</td>\n",
       "      <td>-0.3981</td>\n",
       "      <td>0.2139</td>\n",
       "      <td>0.3801</td>\n",
       "      <td>0.4176</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0.1522</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.7371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>...</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
       "      <td>0.6931</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>-1.6210</td>\n",
       "      <td>-0.8784</td>\n",
       "      <td>-0.3876</td>\n",
       "      <td>-0.8154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>-0.3786</td>\n",
       "      <td>0.7125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 883 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id tratamento  tempo dose   composto      g0      g1      g2  \\\n",
       "0  id_000644bb2  com_droga     24   D1  b68db1d53  1.0620  0.5577 -0.2479   \n",
       "1  id_000779bfc  com_droga     72   D1  df89a8e5a  0.0743  0.4087  0.2991   \n",
       "2  id_000a6266a  com_droga     48   D1  18bb41b2c  0.6280  0.5817  1.5540   \n",
       "3  id_0015fd391  com_droga     48   D1  8c7f86626 -0.5138 -0.2491 -0.2656   \n",
       "4  id_001626bd3  com_droga     72   D2  7cbed3131 -0.3254 -0.4009  0.9700   \n",
       "\n",
       "       g3      g4  ...     c96     c97     c98     c99  eh_controle  24_h  \\\n",
       "0 -0.6208 -0.1944  ... -0.3981  0.2139  0.3801  0.4176            0     1   \n",
       "1  0.0604  1.0190  ...  0.1522  0.1241  0.6077  0.7371            0     0   \n",
       "2 -0.0764 -0.0323  ... -0.6417 -0.2187 -1.4080  0.6931            0     0   \n",
       "3  0.5288  4.0620  ... -1.6210 -0.8784 -0.3876 -0.8154            0     0   \n",
       "4  0.6919  1.4180  ...  0.1094  0.2885 -0.3786  0.7125            0     0   \n",
       "\n",
       "   48_h  72_h  n_moa  ativo_moa  \n",
       "0     0     0      1       True  \n",
       "1     0     1      0      False  \n",
       "2     1     0      3       True  \n",
       "3     1     0      0      False  \n",
       "4     0     1      1       True  \n",
       "\n",
       "[5 rows x 883 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_comb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 05: Fazer análise mais detalhada considerando tempo e dose, para comparar as distribuições (Escolher uma droga e comparar com controle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n_moa</th>\n",
       "      <th>ativo_moa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dose</th>\n",
       "      <th>tempo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
       "      <th>24</th>\n",
       "      <td>2754</td>\n",
       "      <td>2364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3072</td>\n",
       "      <td>2632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2775</td>\n",
       "      <td>2376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
       "      <th>24</th>\n",
       "      <td>2741</td>\n",
       "      <td>2354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2757</td>\n",
       "      <td>2365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2745</td>\n",
       "      <td>2356.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            n_moa  ativo_moa\n",
       "dose tempo                  \n",
       "D1   24      2754     2364.0\n",
       "     48      3072     2632.0\n",
       "     72      2775     2376.0\n",
       "D2   24      2741     2354.0\n",
       "     48      2757     2365.0\n",
       "     72      2745     2356.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_comb.groupby(['dose', 'tempo']).agg({'n_moa':'sum', 'ativo_moa':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KEqcnJSUJnp6egkwmEy5evChOl8lkgkwmE9asWSNOUygUQr9+/QSZTCZERUWJ0ydMmCDIZDLhzp074rRVq1YJMplMCAkJUanj6dOngoWFhdC6dWtx2uHDhwWZTCYMHTpUyM7OFqdv2bJFrOPx48eCIAhCXFycYGFhIXTo0EFITk4W583MzBR8fX2FBg0aCNHR0XnWY/369eK0mJgYcfpPP/0kTk9LSxNsbGwEOzs7jdtTuc7nz58vcL4uXboIMplMuHv3riAIgnDjxg3BzMxM6NGjh5CRkSHOl5ycLLi4uAhWVlYq+0ed3bt3CzKZTOjRo4ewdOnSfP9du3ZN5Xnz588XZDKZMHfuXCEnJ0f45ptvhAYNGgiXL18W5zl//ry4bcLDw8Xpz58/F9q1ayc0bNhQePDggSAIRdsXjx8/FmQymWBmZib88ccfatfn7X3k6OgoyGQyISgoSJz28uVLwcrKSpDJZMKIESMEhUIhCIIg5ObmCi4uLoJMJhO36fu2k/dt7z///LMgk8mEiRMnCjk5OeL0W7duCZaWlkKzZs2EtLQ0QRAEYfjw4YJMJhMePXokzpednS106tRJMDc3F1JTU4WCaGp7x44dE2QymdCmTRshKyuryPUp94e9vb1KG83PwoULBZlMJmzYsEHjvIIgCJaWloKZmZlYm7rtqSSTyYSOHTuKfxfluKJsy+bm5sLVq1fF6ampqULLli2Fxo0bC69evRKnOzo6Ck2bNlV53XdrO3funCCTyYR+/fqJ9QuC+mNVfHy80KBBAyEgIEDIzc1Ved2BAwcKMplMOHPmjCAIb9p206ZNBXt7eyEuLk6c7+bNm4KlpaUgk8mE3bt3qzzfzMxMOHHihMrrbty4UXyPayKTyQRHR8cC5/nrr78EmUwmdO7cWZw2ffp0QSaTCVu2bFGZNyIiQnxvavL2th42bJhgYWEhtj9BEAS5XC60bt1amDJlivDy5UvxeKcUFhYmyGQyYcyYMSrt+dGjR4Ktra3Qtm1bcf9ERUUJMplM6N69u8oyrly5IpiZmQmDBg0SBEEQDh48KMhkMmHhwoUqtaalpQmtWrUSzM3NxfeD8ngmk8mEsLAwcd7s7GzBw8NDkMlkwsOHD8Xp77ZjW1tbwdnZWaV25THL29tbnLZ06VJxP718+VKcPmbMGEEmkwk2NjZCfHy8OD04OFiQyWTC1q1bBUF4c2x0dXUVGjZsKJw6dUplvZSfB5MmTRLXU9le33bz5k1BJpMJw4cPFwpjw4YNgkwmE1avXi1OW7dunSCTyYR58+blmV95/Hi7LZ89e1bl8zkjI0No0qSJyjYUBEFISUkRGjVqJPTp00ec5uzsLLRs2TLP5+iKFSsEmUwmLF68WJzm6+srNGzYULhw4YI4LTExUWjfvr0gk8mE77//Xpz+3XffCTKZTAgODlZ5XWW7d3NzE+RyuSAIgtCxY0fBxsZG5fjy8uVLoVWrVirHqE8Vz4hroLxkUqZMmSI9T3lm+bvvvlM5S25sbIyxY8cCeHPp7m2lSpVSuYlGS0tLPBMfGxtb4PLatGmDGTNm4JtvvlGZXqNGDZiYmKicIVZe6p8wYQL09PTE6f7+/jA1NVV5fmhoKLKzszFixAiVbjelSpXCiBEjoFAoxNdT0tfXR/fu3cW/v/jiC/G5b4+QULZsWdSvXx9JSUkqZ6o+hHK0AeXl4l27dkEQBHz33XcqN9FVrFgRAwcORGZmptpv/epcvHgRP//8c77/rl+/rjL/iBEjUL9+fWzatAlTp07FnTt3MGDAADRt2jTPa1tbW8PNzU38u1KlShg0aBByc3PF+t5nX9StW1c8o1wYffr0Ef9fvnx51K9fHwDQt29fsR+yjo4OGjVqBODNmcL3re1D2vvevXtRunRpTJ48Gbq6/9xzbmFhge7duyM1NVXsvqA8u/52/209PT2sWbMGFy5cQLly5TRvmP9fZnBwsPjvf//7H7799luMGDECurq6mD59utj+ilKfkoODQ6Fu9Hzx4gUAwNDQsFB1ly9fHoIgvNdoGEU5rigpLw0rlStXDtbW1sjKyipw6Dx1lN3MRo0apTKSSEBAAL744guVefX19TFv3jxMnjwZOjo6eWoC/rlB9NSpU0hLS0OvXr1QvXp1cb7GjRvnWddnz57h9OnTcHBwgKOjo8pjPXr0QI0aNT7aUIfvHr9yc3Oxb98+fPXVVwgICFCZt127drCxscGxY8eK1D3G1dUV2dnZKkNuXrlyBYmJiejQoYPa5yi7Obzbnk1MTODn54eEhARERUUB+GefjR07FmXLlhXntbGxwZgxY8Rt2LBhQwQFBakcc4A3nwsNGzaEXC7Hy5cvVR4zMTFRqVFPTw92dnYACj5mCIKA5ORklTPE1atXR3h4OH799dc883t7e6t0RbWxsQEAeHh4qNwnZmlpCQD4+++/AQDXrl1DbGwsPDw8YG9vr/KaI0aMQLVq1XDgwAFkZ2dDoVBAEAQ8ffpU5X3RuHFjREREqL3apM7+/fuhpaUFDw8PcZq7uzu0tbWxb9++9xpauXTp0nB2dsbdu3dx//59cfrRo0eRk5MDLy8vAG+6jY0fPx5z585VyTkAxBHGlO+5p0+f4tq1a3BwcFAZfaxy5cp5zsxnZWUhLCwMJiYmGDZsmMpj7dq1g7OzM+7fvy9eOVIoFMjMzFTpJlW+fHns2rWrwG5snwqOmqKBkZEREhMTkZqamqehFeTu3bvQ1tZWG7qU097tL1ezZs08w1YpQ4KmvqwNGzZEw4YN8erVK9y4cQMPHz5EbGwsbt26hYcPH0Iul4vz/v777zAyMkKdOnVUXkNbWxvW1tYqByvlWKPnzp3DX3/9pTK/8nLsu+tRo0aNPOuhvBO/SpUqKtMNDAzE9fsYQz++evUKwD9fnG7fvg3gzQHk5MmTKvPGx8cDQKHHWw4MDCzSneXKLir+/v7YvXs3GjRokO/z1Y3GoTzIK7fv++yL2rVrF7pePT09leH5gH/C3ruv8/Z+e9/a3re9p6en4/Hjx7CxsVH5oFdq2rQp1q1bJy7Px8cHERERmDBhAlasWIG2bdvC3t4eLVu2LNIwceq+cFauXBlubm7o06ePuL+KWp/Su9s+P8ovOsq2rolyPnW1aFKU44pSvXr18kxT7tecnJwiLf/u3bvQ0dFRO8qPtbW1SkioWLEivLy8oFAo8OeffyImJgaPHz9GdHS0GBKVX8qU297CwkLt677dt/TOnTviF5l3u4oBb943cXFxSEhIyHMzf1Ep95XyfffgwQNkZGRALperXXZWVhbkcjmio6PVftao4+TkBD09PRw9elQMb0eOHEHFihVha2urtl3dvn0bBgYG2Lp1a57HlJ8Xf/zxB77++mtxn6kbRvbbb78V/29qagpTU1NkZWXhxo0bePDgAR49eoTbt2+LfYvfbV8Fta2Cjhm+vr5YvXo1OnbsKN634ODgkO9Qt+9+Niq/IGs6Dio/S9R179HX1xdD9v3799GgQQO4u7vj0KFDcHFxgbW1tTg08pdffpnvurzt3r17uH37NmxsbFCzZk1xerVq1WBra4vz58/jt99+g4uLS6Fe720dO3ZEaGgowsLCEBgYCOBNt5RSpUqJXd50dHTE/z958gR//fUXHj16hJiYGLFrTWHfc2+LiYlBdnY2mjVrpnbo1aZNmyIiIgLR0dFo1qwZ/Pz8MHPmTAQEBKBBgwbi/rWxsdHYfe9TwCCugYmJCRITE/Hw4cMCg3haWhoyMzNRtWpVAG8+jA0MDNR+0JcrVw6lS5fOc5OiunmVjVAQhALrzMrKwsKFC7Fjxw7xdatVq4bmzZujYsWKSExMFOd98eJFnjPfSsr6314vAAXe9PDuWYv8zuoV59i4AMSzC1paWmKoUda/evXqfJ/3bv0fU6NGjVCrVi08fvwYFhYW+W6Dd7c7APFLi/Js1/vsC+UHRWEU9EVI0757n9ret70rg0J+Z7KV21J5lcXBwQGbNm3C2rVrERUVhc2bN2Pz5s0wMjJCYGBgoX+4ZNOmTWq/MH1ofUqF3VfKMPB2CM1PQkICXr16hcqVKxf6DPrbinJcUfqQ49i7UlNTYWBgoHIWVkndEKVHjx7F//73P/HsqKGhISwsLNCgQQNERUWJyy/oSqeRkVGeGgDg+vXrea56vS0lJeWDg7jyzKry/gDlsu/fv4+ff/453+cV5RhWtmxZtGrVCqdPn0ZWVhb09fVx9OhRuLi45LmSoJSWlibeWKipBuU+e/tqqzoKhQKrVq3C+vXrxedWqlQJ1tbWqFWrFmJiYvK0l4LaVkHGjBmDunXrYvv27bh58yZu3LiB4OBgmJqaYtq0aeJZdaX83iuajoPKY3V+X3qV733le2nu3LmwsLDAnj17cPHiRVy8eBELFiyAhYUFgoKCNA4zq+zffvXq1Xx/UG7nzp3vFcRbtWqFKlWqIDw8HIGBgUhKSsKFCxfg6uqqsn53797F7NmzxS9Penp6+PLLL2FhYYHY2Nj3es8VdTsGBASgcuXK2LJlC65cuYK7d+9i9erVqFGjBiZOnKhytflTxCCuQdu2bXH16lWcPXu2wMHud+zYgfnz52PIkCEYNWoUypQpg8zMTKSlpeX5QM7KysLr16/zjLDyIX766Sf8+uuvaN++PQICAmBmZiY27g4dOqh8YJYtWzbfS5nvTlcekCIiIvK9eexT8eeffyI1NRUymUzc5oaGhtDR0cGNGzc0fjAUh5UrV+Lx48cwMjLC7t274eXlhZYtW+aZLysrK8805YFLuR8/5X1RkrUpD+T53aT27nYD/vkxpoyMDFy+fBknT57E3r17ERQUhDp16sDBwUHS+orC0dEROjo6OHXqFORyuUp4ys7Ohp6enhhOTpw4AeDNh6pSfqFY3ehFRTmuFIfy5cvj0aNHyMnJyfP+ffcGyRs3bmDkyJGoXr06Fi5cKN7wqqWlhdWrV4tnxZWvCyDPKBbqXlfZtocOHYqRI0d+lPXKz+XLlwH8c4ZQ2ZY6deqEefPmfbTluLq64tSpUzh79iwqVqyIhISEAsOKoaEhypQpk+eqYn7zZmVlITc3N88XqMzMTPFEzbp167B48WLY2tpi4MCBMDc3F08+DBgw4KMOBaylpYWuXbuia9euSEpKQlRUFI4dO4ajR49iyJAhOHHiRJGueOenqO99PT099OvXD/369cPTp09x9uxZ8QfCBg0ahOPHj+f7uaVQKHDgwAHo6Oiga9euauc5cOAAIiMjER8fr9IFqzB0dHTg7u6OjRs34q+//sLFixchl8vFbinAm/dP3759kZGRgUmTJsHOzg6mpqbQ19fHlStXcPDgQXHeorznNG1H5Re3t4+h7du3R/v27fHy5UtcuHABx48fx6FDhzBmzBh89dVXYjfLT9Gnf85eYl5eXtDT08OWLVvUNiDgzcFFOTxY69atAUDsl6s8sL7typUrEASh0Jef3qXuDMDBgwdRqVIlLFmyBC1atBAb6OvXr8V+vMoP3kaNGiE+Pl5tI393JALlt2x1Q8LFxsZi7ty54oe91JSXTd8+UJiZmUEul6vtfnLt2jUsWLBA7T76GO7evYtVq1ZBJpPh119/hb6+PiZPnqx2hAV121d59s3KygrAp70virO2d9t72bJlUbt2bTx48EBtH+VLly4BgPj+2rhxo/hDW4aGhrC3t8fUqVPFX5772GN/F7W+oqpcuTJcXFwQHx+PLVu2qDy2detWuLq6IiQkBC9fvsSaNWsAQKUvvvKD/d12qG6osqIcV4pDo0aNoFAo1I6Q8vZPdANv+iYrFApMmzYNHh4eqFOnjth2lFcP3j4GAqqjkyi924aVbfvd5SktXboUq1ev/uChMNPT07F//37o6uqK/aCVoeb27dtqt/OGDRuwfPly8b6BwmrXrh10dXXFMGpkZFTg1R4zMzPEx8er/eL122+/YdGiRWLXA5lMBrlcrnZYw6FDh6JZs2bIzMzEwYMHoaOjgxUrVsDe3l4M4YIg5NlfH+LFixcIDg4Wu5ZVqlQJXl5eWLp0Kby9vfP0Lf4QyjPY6o4pCoUCV65cgaGhoXiVdOHChfjtt98AvOmq5+Pjg7Vr16Jly5ZISEjIM4zw286fPwTY3QMAACAASURBVI/4+HjY2dlh5syZav+1b98ecrlc5X60ovzKrnL0lBMnTuDw4cMwMjJS6fseFRWF5ORk9OrVC3369IGZmZl41UDde05LSwvXrl3Ls5x333P169eHnp4ebt68qfZ9pfzM/uqrr5CVlYUVK1aIvx9SoUIFuLq6Yu7cufj2228hl8vVLvNTwiCugYmJCfr06YMXL15gwIABecJrWloaxo0bh9jYWDg6Oop9w7y9vQEACxcuVPkwTk5OFs9svDvOaWEpzzK83d/SwMAAWVlZKuNxyuVyzJ49W7wErpzf29sbgiBg3rx5Kn3w9u/fn+cN0bFjR+jo6GDx4sUqB+Hc3FzMmjUL69at+yR+Ejk0NBQhISGoWrWqyo2inTt3BgD8+OOPKmf709PTMX36dKxZs0ZtP9cPlZubi0mTJiE3NxczZsxA/fr1MWjQIDx58kTtTTjHjh1T+UKQmJiIFStWwNDQUPxQ/pT3RXHWpq69d+7cGa9fv8aPP/6ocjPS7du3sWXLFpQvX14cczwyMhIrV67M061A2Q3g7b6VH0tR6nsfU6ZMQcWKFTFv3jyEhISI0xs1aoTatWtjypQpcHV1xd9//y0Oh6ikvMlRGQCANyFB3RCTRTmuFJWenp7GG8k6d+4MLS0tLFiwQOX9e+jQoTzBWNm1591fDjx37px4Zk65PAcHBxgbG2Pz5s0q98TExMTkGX/ZxMQEzZs3x+nTp/OM771v3z4sW7YMZ86c+aCud5mZmZgwYQKSk5Ph5+eHGjVqiOvk7u6Oe/fuYf369SrPuXDhAubNm4fdu3cX+ZeEK1asiObNm+PkyZM4duwYXFxc1Hb/UercuTMEQcCsWbNUgtGzZ88wffp0rF69WrxyoAxvixcvVul+de3aNVy8eBHW1tYoXbo0DAwMIJfL83xZXb58ufjefJ8bDd9VpkwZbNq0CYsWLcpzDFJ+mfxYx4CmTZuibt26OHr0qMrNsMCbL2xxcXHo0KED9PX1UapUKaxZswZLlixR2abZ2dlITEyEvr5+nnuq3qbslvL2iad3KXPI7t27xUCs7niaHwsLC9SvXx8HDx7ElStX4ObmpnKGPr/33JMnT7B8+XIA/+zDatWqoVWrVjh79qzKcKppaWnivEqlSpWCu7s74uLisGzZMpXHTp48iSNHjsDU1BSWlpYwMDDAvn37sGTJkjxfXJTtqLD330iFXVMKYfTo0UhKSsKePXvQrl07fP3116hTpw4SEhJw9uxZJCcnw8bGRuXSYfPmzdG3b1+sX78eHTt2FO8U/+2335CYmIiBAwfmO16rJsp+iD/99BNatWqFwMBAeHl5Yd26dejSpQucnZ2Rm5uLyMhIPHjwAMbGxkhOTkZKSgqqVq0KDw8P7N+/HwcOHMC9e/fQokULPHz4ECdPnkTFihXx4sUL8XJ3vXr1MH78ePHnu52cnFChQgWcPn0aMTExcHR0FA+8JWHv3r0qN/Kkpqbi+vXruH37NoyMjLBs2TKVfmUtW7ZEz549sXnzZnFMWH19fURERCAuLg5+fn6F6vcLvBk1Rd0NU29zd3dH/fr1sXLlSty5cwe+vr7iHfcDBw7EoUOHsHXrVrRv317lzvEyZcqgT58+cHNzQ9myZREREYHnz59j1qxZ4sH4U9sXbyvO2tS194EDByIyMhIHDhxAdHQ0WrZsiaSkJEREREAQBCxatEhsB8OHD8eFCxfQq1cvuLm5oVq1arh37x5+++031K9fv1i2WVHqex+VK1fG1q1bMWTIEEyZMgUbNmyAnZ0dDA0Nxe5YytBRu3ZtKBQK8aYlT09PLFmyBGvXrsXjx49Ru3ZtnD17FqmpqXkCSVGOK0WlHPN/3LhxaNOmTZ7RSoA3V4P69euHtWvX4ptvvsHXX3+N+Ph4REREoE6dOipn8d3d3bF+/XrMmDEDly5dQpUqVRAdHY3IyEhUrFgRSUlJ4jYpU6YMZs2ahZEjR8LHx0f8EZvDhw/D2Ng4zw+MKG8EGzlyJOzt7fHVV1/hwYMHOHnyJIyMjMSrK5qkpqaqHEOys7MRHx+Ps2fPIikpCW3atMkzpvKECRNw7do1zJ07F8ePH4elpSUSEhJw9OhR6Orq4scff3yvG9JcXV1x7tw5JCcnaxxP39vbGydOnMCRI0cQHR2Ntm3biiM6paSkYOzYseINjm3atEGXLl2we/dudOrUCW3btsWrV69w6NAhlClTBlOnTgXwJrBfv34d/v7+6NChA/T09HDhwgXcvn0blSpVUtlfH0JfXx8jRoxAUFAQPD094eLiglKlSuHSpUu4desWOnXqlGcEnvelra2Nn376Cf3798fgwYPh6OiIOnXq4Nq1a7h+/Trq16+P7777DsCbe4B69+6N9evXw9PTEw4ODtDW1saZM2cQExODoUOH5nuMyMjIwLFjx1C6dOkC+383b94cJiYmePz4MaKiotC6dWvxeLps2TLcunVL4wAEXl5e4hXFd0N/s2bNULNmTezZswfJycmQyWR4+vQpTpw4Id5z9PY+nDp1Krp3747hw4fDxcUFVatWxYkTJ9TemzBx4kRcv34dK1euxIULF9CkSRMxp5QtWxbz5s0Tz+6PGzcOw4cPh7e3N9q3b4/y5cvj1q1buHDhAuzs7NR2B/2UMIgXgo6ODubMmQMPDw9s374dd+/exalTp6CrqwszMzPxYP5uY5o4cSIaNmyIrVu34sCBA9DV1YW5uTmmTp0q3mn8Prp3746rV6/i8uXLiImJQd++fTF69GiUKVMGoaGh+PXXX2FsbIz69evjhx9+QExMDH788UecOnUKPj4+0NLSQnBwMFasWIHQ0FBs3boVdevWxbx583DixAmEh4er3LjXt29ffPHFF1i3bh2OHj0KhUIh/ihNQEBAgWdSPra3R67Q0tJC6dKlUbduXQwcOBB9+vRB5cqV8zznhx9+QOPGjbFt2zaEhoZCR0cHpqamGD58uHjGvDCUN9MUxNzcHLm5uVi5ciWqVKmCcePGiY/p6+tj5syZ6NGjByZPnozQ0FDxsY4dO6JOnTpYu3YtUlJSYG5ujtmzZ+fpu/wp7Yt3FVdt6tp7mTJlsGHDBqxduxYHDhzAtm3bUL58eTg6OmLQoEHiL8YBb0af2bJlC1asWIHz588jOTkZVatWRa9evTBkyJD3uolREwMDg0LX977q16+Pffv2Yd++fThw4ADCwsKQkZGBatWqoVOnTmjfvj127tyJOXPmYOfOnVi8eDG++uorVK5cGZs2bcLChQtx+vRp6OnpoW3btvjuu+8waNAglWUU5bhSVOPHj8f333+Pw4cPIykpSW0QB94MAWtqaopNmzYhJCQE1atXR1BQEKKjo7Fp0yZxPnNzc6xevRpLly5FREQEdHR0UKtWLYwYMQJdu3aFvb09Tp06Ja6js7MzNmzYgODgYISFhaF06dLo1q0bGjdujNGjR6vU8MUXX2DPnj1Yvnw5Tp06hXPnzqFq1aro1KkThg0bVuj7ItLS0lRueNTV1UWFChXQoEEDeHp64ptvvskTqo2NjRESEoJVq1bh2LFj2Lx5M4yNjeHk5IShQ4cWaXjSt7m4uGDWrFkoX768xqCipaWFpUuXYuvWrdizZw927tyJUqVK4csvv0Tfvn3z/CDT7NmzYWFhgR07dmDHjh0wMDCAvb09Ro8eLW6r7t27i7/WuXPnTpQrVw6mpqbiD80NGzYMp06dKvDerMLq2bMnKlWqhE2bNiEsLAyZmZmoV68eJk2ahB49enzw67/NxsZG/JXqqKgonDlzBjVr1sSQIUMwcOBAlZsVx48fj7p162Lnzp3Yu3cv5HI5vvzyS/z0008FfjYdPXoUGRkZ8PT0LHBoZS0tLXTu3BlLly7Fzp070bp1a3h6euLMmTM4ffo0tm3blm//cqWOHTtiyZIlqFmzZp6RecqWLYt169ZhwYIF4hWPGjVqiO+Lfv364dKlS+K9AfXq1cOOHTuwaNEinD17FtnZ2Wjbtq14MvFtyna/cuVKHD16FFu2bIGxsTG8vb0xePBglfeci4sL1qxZg19++QUnTpxAamoqatWqheHDh2PAgAFF6o4jBS2hODv40ScpLi4O5cqVU/ttu0ePHvj9999x7dq1T77xfg6UZ2p79eqFyZMnS10OfYZOnTqFzZs3Y9GiRYUeM52IiEoG+4j/B61ZswZNmzbNc3b3+vXruHLlCmxtbRnCiT4TDg4O+OWXXxjCiYg+Qeya8h/UpUsXhISEYNCgQXB1dUW1atXw5MkTREREoEyZMnn6KBIRERHRx8cg/h/UqFEjsc/h+fPnkZSUBGNjY7i7u2Po0KF5flWMiIiIiD4+9hEnIiIiIpIA+4gTEREREUmAQZyIiIiISAIM4kREREREEmAQJyIiIiKSAIM4EREREZEEGMSJiIiIiCTAIE5EREREJAEGcSIiIiIiCTCIExERERFJgEGciIiIiEgCDOJERERERBJgECciIiIikgCDOBERERGRBBjEiYiIiIgkwCBORERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJMIgTEREREUmAQZyIiIiISAIM4kREREREEmAQJyIiIiKSAIM4EREREZEEGMSJiIiIiCTAIE5EREREJAEGcSIiIiIiCTCIExERERFJgEGciIiIiEgCDOJERERERBJgECciIiIikgCDOBERERGRBHSlLkAKCoUCcrkgdRlERERE9JnT09PJ97H/ZBCXywWkpGRIXQYRERERfeaqVCmX72PsmkJEREREJAEGcSIiIiIiCTCIExERERFJgEGciIiIiEgCDOJERERERBJgECciIiIikgCDOBERERGRBBjEiYiIiIgkwCBORERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJMIgTEREREUlAV+oCiPJTtqwhSpfWkbqMf43MTDnS0zOkLoOIiIgKiUGcPlmlS+ughWms1GX8a1x4UA/p6VJXQURERIXFrilERERERBJgECciIiIikgCDOBERERGRBBjEiYiIiIgkwCBORERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJFDsQVwQBEyYMAFr164FALx+/RqTJk2Cp6cnPDw8MGnSJLx+/RoAkJycjAEDBsDd3R2enp64evWq+DonT56El5cX2rdvjxEjRiA9PR0AIJfLMXv2bLi5ucHFxQXbtm0r7lUiIiIiIvpgxRrEY2Ji0Lt3bxw5ckSctmLFCsjlcoSGhiI0NBRZWVlYtWoVAGDGjBlo1qwZwsLCMH/+fIwcORKZmZlITk7GpEmTEBwcjCNHjsDExAQLFiwAAGzfvh2xsbE4ePAgdu3ahY0bN+LmzZvFuVpERERERB+sWIP41q1b4ePjAzc3N3Fa8+bNMWTIEGhra0NHRwfm5uZ4+vQpcnNzcfLkSXTr1g0AYG5ujnr16uHMmTOIjIxE48aNUa9ePQCAv78/Dhw4AEEQEBERAW9vb+jq6qJChQrw8PBAaGhoca4WEREREdEH0y3OF586dSoA4OzZs+K0Nm3aiP//+++/sXHjRsyaNQsvXryAQqGAsbGx+Hi1atUQHx+P169fo3r16uL06tWrIz09Ha9evUJcXBxq1Kih8lh0dHSBdenoaMHIyPCD14/oU8N2TURE9O9RrEG8IL///jsCAwPRo0cPODo6IiEhAVpaWirzCIIAHR0dKBSKPI8BgLa2NgRBUHlMEARoaxd8ol8uF5CSkvFxVoSKTZUq5aQu4V+H7ZqIiOjTUlCekWTUlEOHDqFfv34YO3YsBg8eDACoVKkSBEFASkqKON+zZ89QrVo11KhRA8+ePROnJyQkoEKFCjA0NMzz2LNnz1TOnhMRERERfYpKPIifOHECQUFBWLt2Lby8vMTpurq6+PrrrxESEgIAuHv3LmJiYtCiRQu0adMGN27cQGxsLIA3N2i2a9cOANCuXTvs3r0bubm5SE1NxaFDh+Ds7FzSq0VEREREVCQl3jVl7ty5EAQBP/zwgzjNxsYG06ZNw7Rp0/DDDz/A09MTWlpamDdvHsqVe3M6f86cORgxYgRycnJQp04dzJ07F8CbGzcfPXqETp06IScnB76+vrC1tS3p1SIiIiIiKhItQRAEqYsoaTk5cval/ReoUqUcWpjGSl3Gv8aFB/WQmJgmdRlERET0lk+ujzgRERER0X8dgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJMIgTEREREUmAQZyIiIiISAIM4kREREREEmAQJyIiIiKSAIM4EREREZEEGMSJiIiIiCTAIE5EREREJAEGcSIiIiIiCTCIExERERFJgEGciIiIiEgCDOJERERERBJgECciIiIikgCDOBERERGRBBjEiYiIiIgkwCBORERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJMIgTEREREUmAQZyIiIiISAIM4kREREREEmAQJyIiIiKSAIM4EREREZEEGMSJiIiIiCTAIE5EREREJAEGcSIiIiIiCTCIExERERFJgEGciIiIiEgCDOJERERERBJgECciIiIikgCDOBERERGRBBjEiYiIiIgkwCBORERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJMIgTEREREUmAQZyIiIiISAIM4kREREREEmAQJyIiIiKSQLEHcUEQMGHCBKxduxYAIJfLMXv2bLi5ucHFxQXbtm0T542NjUVAQADc3d3RtWtXxMTEiI/t2rUL7u7ucHV1xbRp05CTkwMAyMzMxNixY9GhQwe0b98eERERxb1KREREREQfrFiDeExMDHr37o0jR46I07Zv347Y2FgcPHgQu3btwsaNG3Hz5k0AwLhx4+Dn54ewsDAMHz4cI0eOhCAI+PPPPxEcHIwtW7bg8OHDSEtLw4YNGwAAwcHBMDQ0RHh4ONavX48ZM2YgPj6+OFeLiIiIiOiDFWsQ37p1K3x8fODm5iZOi4iIgLe3N3R1dVGhQgV4eHggNDQUCQkJuH//Pjw8PAAADg4OyMjIwJ07d3D8+HE4OTnB2NgY2tra8PX1RWhoqPh6Pj4+AICaNWuidevWCA8PL87VIiIiIiL6YLrF+eJTp04FAJw9e1acFhcXhxo1aoh/V69eHdHR0YiLi0PVqlWhrf3Pd4Nq1aohPj4ecXFxqF27tspzEhIS1L6e8jkF0dHRgpGR4YetHNEniO2aiIjo36NYg7g6giBAS0tL5W9tbW0oFAqV6crHdHR0IAhCnunKwP7u6wFQCfPqyOUCUlIyPmQ1qARUqVJO6hL+ddiuiYiIPi0F5ZkSHzWlRo0aePbsmfj3s2fPUL16ddSsWROJiYkqoVv5WH7PKej1iIiIiIg+ZSUexNu1a4fdu3cjNzcXqampOHToEJydnVG9enXUqVMHYWFhAIAzZ85AW1sbMpkMTk5OOHHiBJKSkiAIAnbs2AFnZ2fx9Xbs2AEAiI+Px5kzZ+Do6FjSq0VEREREVCQl3jXF398fjx49QqdOnZCTkwNfX1/Y2toCABYuXIgpU6ZgxYoV0NfXx5IlS6CtrY0GDRpg2LBh6N27N3JycmBlZYWBAwcCAIYPH47p06fDw8MDcrkc48ePR506dUp6tYiIiIiIikRLeLcD9n9ATo6cfWn/BapUKYcWprFSl/GvceFBPSQmpkldBhEREb3lk+ojTkREREREDOJERERERJJgECciIiIikgCDOBERERGRBBjEiYiIiIgkwCBORERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJ6EpdwKesTBkDGBrqS13Gv0pGRjZevcqSugwiIiKiTx6DeAEMDfVRqdI0qcv4V0lKmsEgTkRERFQI7JpCRERERCQBBnEiIiIiIgkwiBMRERERSYBBnIiIiIhIAgziREREREQSYBAnIiIiIpIAgzgRERERkQQYxImIiIiIJMAgTkREREQkAQZxIiIiIiIJMIgTEREREUmAQZyIiIiISAIM4kREREREEihyEM/JySmOOoiIiIiI/lM0BvHLly9j+fLlyM7Oho+PD5o1a4awsLCSqI2IiIiI6LOlMYjPnz8fTZo0QUREBIyMjHDo0CGsW7euJGojIiIiIvpsaQzicrkcrVq1QlRUFJydnVG7dm0oFIqSqI2IiIiI6LOlMYgrFArcvHkTJ0+eRKtWrfDnn3+ynzgRERER0QfS1TTD4MGDMXbsWHTt2hUmJiZwcnLC5MmTS6I2IiIiIqLPlsYg7urqCldXV+Tm5iInJwfHjh2Djo5OSdRGRERERPTZ0tg1JSkpCQMHDkSTJk1gaWmJvn37IiEhoSRqIyIiIiL6bGkM4jNnzoSVlRWioqIQFRWFZs2aYfr06SVQGhERERHR50tjEI+NjUVgYCDKly+PihUrYsSIEXj06FFJ1EZERERE9NnSGMRzc3ORlZUl/p2ZmQktLa1iLYqIiIiI6HOn8WZNd3d39OnTB97e3tDS0sLu3bvh6upaErUREREREX22NAbxYcOGoXr16jhz5gwUCgW8vb3h4+NTErUREREREX22NAbxxYsXY9SoUejSpYs4LSgoCD/88EOxFkZERERE9DnLN4gvXboUqampCAsLQ3p6ujg9JycHkZGRDOJERERERB8g3yBuZWWFW7duQVtbG0ZGRuJ0HR0dLFiwoESKIyIiIiL6XOUbxB0cHODg4AB7e3tYWlqWZE1ERERERJ89jX3Ey5Yti0mTJiElJQWCIIjTV65cWayFERERERF9zjQG8YkTJ8LS0hLNmzfn+OFERERERB+JxiCemZnJGzOJiIiIiD4yjb+sWbduXTx79qwkaiEiIiIi+s/QeEZcoVDA09MTjRo1goGBgTidfcSJiIiIiN6fxiDu4uICFxeXkqiFiIiIiOg/Q2MQ79y5M+Lj4xEdHY02bdogISEBNWvWLInaiIiIiIg+Wxr7iJ86dQp+fn6YMWMGkpKS4OHhgYiIiJKojYiIiIjos6UxiP/8888ICQlB+fLlUbVqVfz6669YunRpSdRGRERERPTZ0hjE5XI5qlatKv5tbm7O8cSJiIiIiD6QxiBeunRpPH36VAzfly9fVhk95X0cO3YMXl5e6NSpE3r16oVHjx5BLpdj9uzZcHNzg4uLC7Zt2ybOHxsbi4CAALi7u6Nr166IiYkRH9u1axfc3d3h6uqKadOmIScn54NqIyIiIiIqCRpv1hw3bhz69euHxMRE+Pr6IjY2FsHBwe+9wNevX2P8+PHYv38/6tatiw0bNiAoKAgODg6IjY3FwYMH8erVK/j6+qJRo0awtLTEuHHj0Lt3b3h5eeHUqVMYOXIkDhw4gL/++gvBwcHYu3cvjIyMMG7cOGzYsAEDBw587/qIiIiIiEqCxjPi1tbWCAkJwcKFCzF48GCEh4fD1tb2vRcol8shCALS0tIAAK9evYKBgQEiIiLg7e0NXV1dVKhQAR4eHggNDUVCQgLu378PDw8PAICDgwMyMjJw584dHD9+HE5OTjA2Noa2tjZ8fX0RGhr63rUREREREZUUjWfEAeD+/fsAAG1tbdy6dQvAm0D8PsqUKYMZM2bAz88PRkZGUCgU2LZtGwYNGoQaNWqI81WvXh3R0dGIi4tD1apVoa39z3eGatWqIT4+HnFxcahdu7bKcxISEjTWoKOjBSMjw/eqnzTjtpUOtz0REdG/h8YgPnr0aFy+fFnlhk0tLa33DuLR0dFYtmwZwsLCUKdOHWzatAnDhw+HQqFQuQlUEARoa2vnma58TEdHB4Ig5Jn+dmDPj1wuICUlQ+N8VaqUK+Ra0dsKs20Lg9u/6D7WticiIqKPo6A8ozGI//777zh+/Dj09fU/SjGRkZGwsbFBnTp1AAABAQGYM2cOWrRogWfPnonzPXv2DNWrV0fNmjWRmJgIQRDEQK58rEaNGmqfQ0RERET0qdN4+viLL75Abm7uR1tgw4YNcenSJTx//hwAEBERgdq1a6Ndu3bYvXs3cnNzkZqaikOHDsHZ2RnVq1dHnTp1EBYWBgA4c+YMtLW1IZPJ4OTkhBMnTiApKQmCIGDHjh1wdnb+aLUSERERERUXjWfEfX190bFjR1hbW0NX95/Z58yZ814LtLOzQ//+/dGzZ0/o6emhQoUKWL58OUxNTfHo0SN06tQJOTk58PX1FW8KXbhwIaZMmYIVK1ZAX18fS5Ysgba2Nho0aIBhw4ahd+/eyMnJgZWVFUdMISIiIqJ/BS3h3Y7W7+jcuTOsrKzEriRK/fr1K9bCilNOjrzQfcQrVZpWAhV9PpKSZiAxMe2jvFaVKuXQwjT2o7zWf8GFB/U+2rYnIiKij+OD+ohra2tj+vTpH7MeIiIiIqL/PI19xBs1aoSTJ0+WQClERERERP8dGs+IR0VFISQkBHp6etDT0xNHL7l69WpJ1EdERERE9FnSGMQ3btxYEnUQEREREf2n5BvEz507Bzs7O9y+fVvt47Vq1Sq2ooiIiIiIPnf5BvFDhw7Bzs4OmzdvzvOYlpYWXF1di7UwIiIiIqLPWb5BPCgoCADg5uaGgICAEiuIiIiIbzNmuQAAIABJREFUiOi/QOOoKdu2bSuJOoiIiIiI/lM03qxpamqKH374Ac2aNYOhoaE4nV1TiIiIiIjen8YgnpKSgpSUFDx8+FCcxj7iREREREQfRmMQV3ezJhERERERfRiNfcQTExPx7bffon379khKSkL//v2RmJhYErUREREREX22NAbxGTNmwNnZGQYGBihfvjwaNGiAyZMnl0RtRERERESfLY1B/O+//0a3bt2gra0NPT09jB8/HnFxcSVRGxERERHRZ0tjENfS0oJCoRD/Tk9PV/mbiIiIiIiKTuPNmq6urhg3bhzS0tKwfft27Ny5Ex06dCiJ2oiIiIiIPlsag/jgwYOxb98+KBQKREVFwdfXFz4+PiVRGxERERHRZ0tjEAcALy8vyGQyaGtrw8zMDFpaWsVdFxERERHRZ01jEL98+TJGjx4NXV1dyOVy6OnpYdmyZWjQoEFJ1EdERERE9FnSGMSDgoIwe/Zs2NvbAwBOnDiB6dOnY/v27cVeHBERERHR50rjqCkAxBAOAE5OTsjMzCy2goiIiIiI/gs0BnFLS0uEhYWJf0dGRkImkxVrUUREREREnzuNXVMiIyMREhKCGTNmQFdXF0lJSTAwMEBERAS0tLRw9erVkqiTiIiIiP6vvTuPjqo+3D/+TCYbITGRfLMgkm8oSqolVXpiKRWEEhDFhIRgJLRi7Y9FVAiUL7RAIyFAQRYLJSIVLLKIRAqRLLJIiomE2lQBA1SBCqYUyYYhQDayzPz+6HFOUwkDksw1k/frHM+Zez9zJ8+9eDhPPnzmXjgVu0V88+bNjsgBAAAAdCh2l6ZkZmaqW7dutv98fX310ksv2bYBAAAA3Dy7RfyDDz7QhAkTVFlZqY8//lixsbHy9fV1RDYAAADAadldmrJx40a98sorioqKkouLixYvXqwHH3zQEdkAAAAAp2V3Rry8vFyFhYXq1q2bXF1ddejQITU1NTkiGwAAAOC07BbxESNG6N5779Wbb76p9PR0nTx5UvHx8Y7IBgAAADgtu0tTfv/736tv376SJD8/P61evVpbtmxp82AAAACAM2txRvzjjz+WJFsJ/08eHh5tlwgAAADoAFos4ikpKbbXo0ePbjbGjDgAAABwa1os4lar1fb66tWrLY4BAAAAuHktFnGTyXTN19faBgAAAHBz7N41BQAAAEDra/GuKXV1dfrkk09ktVqbvf5qDAAAAMA312IRv3r1qiZPnmzb/s/XLE0BAAAAbk2LRXz//v2OzAEAAAB0KKwRBwAAAAxAEQcAAAAMQBEHAAAADHBDRbyurk4nT56U1WpVbW1tW2cCAAAAnJ7dIv7xxx9ryJAheuaZZ1RaWqpBgwbp8OHDjsgGAAAAOC27RXzp0qXasGGD/Pz8FBwcrKVLl+q3v/2tI7IBAAAATstuEa+rq9Ndd91l2x44cKCampraNBQAAADg7OwWcVdXV126dMn2EJ8zZ860eSgAAADA2bX4QJ+vTJo0SU8++aQuXLig6dOn6+DBg5o/f74jsgEAAABOy24RHzx4sHr27KmDBw/KYrHo+eefV8+ePR2RDQAAAHBadov4559/rlOnTqlbt266++67dccddzgiFwAAAODUWizi1dXV+r//+z999NFHCg0Nlclk0pkzZzRw4EC9+OKLcnd3d2ROAAAAwKm0+GXNNWvWKDg4WAcPHtT27dv1pz/9Sfn5+fL29taKFSscmREAAABwOi3OiOfl5Wnnzp0ym822fZ06ddKcOXM0evRoh4QDAAAAnFWLM+Jms7lZCf+Kp6fnNfcDAAAAuHEtFvGv7ht+LVartU3CAAAAAB1Fi0tTLl++rH379l2zdF+5cqVNQwEAAADOrsUifscdd2jTpk3XHOvatWubBQIAAAA6ghaL+ObNmx2ZAwAAAOhQWlwj3pZOnjypsWPHKjY2VnFxcTp+/Lgk6dVXX9UjjzyioUOHKjU11bYspqKiQuPHj9fw4cMVFRWlw4cP2z4rNzdX0dHRGjZsmBITE1VVVWXEKQEAAAA3xeFFvLa2VuPGjdP48eO1c+dOPffcc5oxY4by8vK0e/dupaenKzs7WwUFBdq9e7ckKSUlRREREdq1a5eWLVumqVOnqra2VhUVFZo9e7ZSU1O1d+9ede/eXcuXL3f0KQEAAAA3zeFF/ODBg+revbsGDhwoSYqMjNTKlSu1b98+RUVFycvLSx4eHoqLi1NmZqYaGxuVm5urJ554QpJ0zz33KDQ0VAcOHFB+fr7Cw8MVGhoqSRozZoyysrK4qwsAAAC+9W6oiB89elRpaWmqr6/XkSNHbukHfv755woICNCcOXMUFxenX/ziF2pqalJxcXGzL4EGBwertLRUFy9elMViUZcuXWxjQUFBKikpUUlJiYKDg5sdU1VVperq6lvKCAAAALS1Fr+s+ZX09HT98Y9/1NWrVzV06FA999xz+uUvf2mbob5ZjY2NysvL06ZNm3TfffcpJydHEydO1He+851m9y63Wq1ycXGRxWL52j3NrVarzGbzNcckycXl+r9fmM0m+fl5faP8sI9raxyuPQAA7YfdIr5582a99dZbevLJJ+Xv76/09HSNHz/+GxfxwMBA9ezZU/fdd58kaciQIUpKSpKLi4vKysps7ysrK1NwcLD8/f1ltVpVWVkpPz8/21hQUJC8vb1VWFhoO6a0tFS+vr7y8rp+GWlqsqqyssZu1oAAn29yih3ejVzbG8H1v3mtde0BAEDruF6fsbs0xcXFRd7e3rbtrl273tIj7h966CGdO3fOdqeUDz/8UCaTST//+c+VmZmpmpoa1dfXKz09XUOGDJGrq6sGDRqkbdu2SZJOnDih06dPq2/fvurfv78KCwtVVFQkSUpLS1NkZOQ3zgYAAAA4it0ZcT8/P3366ae2JSCZmZny9fX9xj8wICBAq1evVkpKimpra+Xu7q7U1FRFRETo1KlTio+PV0NDgyIjIxUbGytJSk5OVlJSkqKiomQymbR06VL5+Pz7t4vFixcrMTFRDQ0NCgkJ0ZIlS75xNgAAAMBRTFY7txg5ffq0pk6dqrNnz+q2226Th4eHXnnlFYWFhTkqY6traGi64aUp/v7JDkjkPL78MkXl5Vda5bMCAnzUt0dRq3xWR1DweWirXXsAANA6rrc0xe6MeM+ePZWRkaGioiI1NTWpR48ecnNza9WAAAAAQEfTYhHfuXPnNfd/8sknkmRbNgIAAADg5rVYxPfs2SNJKi8v15kzZ/SjH/1Irq6uKigo0D333EMRBwAAAG5Bi0X8D3/4gyRp4sSJWrFihUJCQiRJ58+f1wsvvOCYdAAAAICTsnv7wuLiYlsJl6Q77rhDJSUlbRoKAAAAcHZ2v6wZEBCgVatWaeTIkZKkt956S927d2/zYAAAAIAzszsj/uKLL+rUqVOKiYlRbGysvvjiCy1atMgR2QAAAACnZXdGPDAwUC+//LIjsgAAAAAdht0ifuTIEa1du1Y1NTWyWq2yWCw6d+6ccnNzHRAPAAAAcE52l6YkJSWpT58+qqqqUnR0tLy9vfXwww87IhsAAADgtOzOiJtMJk2cOFEXL17Ud77zHUVHR2vUqFGOyAYAAAA4Lbsz4p07d5YkhYSE6B//+Ic8PT3l4mL3MAAAAADXYXdGPDw8XNOmTdPUqVP1zDPPqKioSK6udg8DAAAAcB12p7Z/85vf6Omnn1aPHj00Z84cWSwWvfTSS47IBgAAADitFqe2z58/b3sdGBio8+fPq1evXurVq5dDggEAAADOrMUi/thjj8lkMslqtaqurk5eXl5ydXXV5cuX5e/vr/z8fEfmBAAAAJxKi0X8yJEjkqS5c+eqb9++euyxxyRJf/7zn5WTk+OYdAAAAICTsrtG/Pjx47YSLkmRkZE6ceJEm4YCAAAAnJ3dIm6xWFRQUGDbfv/992Uymdo0FAAAAODs7N6HMCkpSdOmTZObm5ssFosk6eWXX27zYAAAAIAzs1vEIyIi9N577+nUqVOSpLCwMO4jDgAAANyiFhv1unXrNGHCBC1YsOCaS1GSkpLaNBgAAADgzFos4j4+PpKk22+/3WFhAAAAgI6ixSKekJAgSerSpYt++tOfNhtbu3Zt26YCAAAAnFyLRXzr1q2qq6vThg0bdPXqVdv+hoYGpaWlaeLEiQ4JCAAAADijFou4q6urTp06pbq6OtsXNSXJbDZr1qxZDgkHAAAAOKsWi3h8fLzi4+OVk5OjIUOGODITAAAA4PTs3ofw/vvv18svv6zKyspm+7lrCgAAAPDN2S3iM2fOlKenp+69916eqAkAAAC0ErtFvKSkRLt373ZEFgAAAKDDcLH3hjvuuEM1NTWOyAIAAAB0GHZnxAMDAxUbG6sf/vCH8vT0tO1njTgAAADwzdkt4t26dVO3bt0ckQUAAADoMOwW8cmTJ39tH0tVAAAAgFtjt4jn5ORo1apVqqmpkdVqlcViUWVlpY4cOeKIfAAAAIBTslvEly5dqmnTpmnr1q2aMGGCcnJy1LlzZ0dkAwAAAJyW3bumdOrUScOHD9f9998vDw8PzZs3T7m5uQ6IBgAAADgvu0Xcw8ND9fX1CgkJ0aeffioXFxce7AMAAADcIrtLUwYPHqyJEydqyZIlGj16tA4dOqTbb7/dEdkAAAAAp2W3iE+aNEkjRoxQUFCQXnnlFX344YeKiopyRDYAAADAadkt4n//+98lSRcvXpQkRUREqKSkRP7+/m2bDAAAAHBidov4lClTbK8bGhp04cIFfe9739P27dvbNBgAAADgzOwW8f379zfbLigoUFZWVpsFAgAAADoCu3dN+W99+/a1LVcBAAAA8M3c8BpxSbJarTp+/Ljq6uraNBQAAADg7G5qjbjJZJK/v7/mzZvXlpkAAAAAp3fTa8QBAAAA3LrrFvHq6mqlpaXp8OHDslgs6tOnj376058qJydHQUFB6tevn6NyAgAAAE6lxSJeWVmp0aNHq2fPnnrwwQclSX/96181atQode7cWZs2bXJYSAAAAMDZtFjEU1NTFR8fr/Hjx9v2/exnP1NiYqJcXV3l7e3tkIAAAACAM2qxiP/tb3/Tzp07m+2rrKzU6dOn1djY2ObBAAAAAGfW4n3ETSaTzGZzs32dO3fWqlWr5OHh0ebBAAAAAGd23Qf6VFVVNdt2c3NTQEBAmwYCAAAAOoIWi3hUVJReeOEF1dfX2/ZdvXpVycnJGjFihEPCAQAAAM6qxTXi48aN04wZMxQZGanevXtLko4dO6a+fftq3LhxDgsIAAAAOKMWi7jZbNaKFSt07NgxHTp0SJL07LPP6vvf/77DwgEAAADOyu6TNcPDwxUeHu6ILAAAAECHcd0va7a1nJwc9enTx7b96quv6pFHHtHQoUOVmpoqq9UqSaqoqND48eM1fPhwRUVF6fDhw7ZjcnNzFR0drWHDhikxMfFrXzAFAAAAvo0MK+JFRUVasmSJbTsvL0+7d+9Wenq6srOzVVBQoN27d0uSUlJSFBERoV27dmnZsmWaOnWqamtrVVFRodmzZys1NVV79+5V9+7dtXz5cqNOCQAAALhhhhTx2tpazZw5U7NmzbLt27dvn6KiouTl5SUPDw/FxcUpMzNTjY2Nys3N1RNPPCFJuueeexQaGqoDBw4oPz9f4eHhCg0NlSSNGTNGWVlZtpl0AAAA4NvKkCI+d+5cjR49WmFhYbZ9xcXF6tq1q207ODhYpaWlunjxoiwWi7p06WIbCwoKUklJiUpKShQcHNzsmKqqKlVXVzvmRAAAAIBvyO6XNVvbli1b5Orqqscff1znzp2z7bdarTKZTM22XVxcZLFYmu3/asxsNl9zTJJcXK7/+4XZbJKfn9ctnglawrU1DtceAID2w+FF/O2331ZdXZ1iYmLU0NBge33vvfeqrKzM9r6ysjIFBwfL399fVqtVlZWV8vPzs40FBQXJ29tbhYWFtmNKS0vl6+srL6/rl5GmJqsqK2vsZg0I8PmGZ9mx3ci1vRFc/5vXWtceAAC0juv1GYcvTdm+fbuys7OVkZGhtWvXytPTUxkZGRo6dKgyMzNVU1Oj+vp6paena8iQIXJ1ddWgQYO0bds2SdKJEyd0+vRp9e3bV/3791dhYaGKiookSWlpaYqMjHT0KQEAAAA3zeEz4i0ZPHiwTp06pfj4eDU0NCgyMlKxsbGSpOTkZCUlJSkqKkomk0lLly6Vj8+/f7tYvHixEhMT1dDQoJCQkGZ3YgEAAAC+rUzWDniLkYaGphtemuLvn+yARM7jyy9TVF5+pVU+KyDAR317FLXKZ3UEBZ+Httq1BwAAreNbtTQFAAAAAEUcAAAAMARFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwgCFFPCMjQyNGjFBMTIwSEhJ07NgxSdKrr76qRx55REOHDlVqaqqsVqskqaKiQuPHj9fw4cMVFRWlw4cP2z4rNzdX0dHRGjZsmBITE1VVVWXEKQEAAAA3xeFF/MyZM1q2bJlee+01ZWRk6Nlnn9WUKVOUl5en3bt3Kz09XdnZ2SooKNDu3bslSSkpKYqIiNCuXbu0bNkyTZ06VbW1taqoqNDs2bOVmpqqvXv3qnv37lq+fLmjTwkAAAC4aQ4v4u7u7lq4cKECAwMlSb1799aFCxe0Z88eRUVFycvLSx4eHoqLi1NmZqYaGxuVm5urJ554QpJ0zz33KDQ0VAcOHFB+fr7Cw8MVGhoqSRozZoyysrJsM+kAAADAt5Wro3/gnXfeqTvvvFOSZLVatXjxYg0ePFhlZWXq37+/7X3BwcEqLS3VxYsXZbFY1KVLF9tYUFCQSkpKVFdXp+Dg4GbHVFVVqbq6Wt7e3i1mMJtN8vPzaoOzgySurYG49gAAtB8OL+Jfqamp0axZs1RSUqLXXntN06ZNk8lkso1brVa5uLjIYrE02//VmNlsvuaYJLm4XH+iv6nJqsrKGrsZAwJ8bvBs8J9u5NreCK7/zWutaw8AAFrH9fqMIV/WPH/+vBISEmQ2m7Vp0ybddttt6tq1q8rKymzvKSsrU3BwsPz9/WW1WlVZWdlsLCgo6GvHlJaWytfXV15ezAoCAADg283hRbyqqkpjx47Vww8/rBUrVsjT01OSFBkZqczMTNXU1Ki+vl7p6ekaMmSIXF1dNWjQIG3btk2SdOLECZ0+fVp9+/ZV//79VVhYqKKiIklSWlqaIiMjHX1KAAAAwE1z+NKULVu26Pz589q3b5/27dtn279hwwY9/PDDio+PV0NDgyIjIxUbGytJSk5OVlJSkqKiomQymbR06VL5+Px7mn/x4sVKTExUQ0ODQkJCtGTJEkefEgAAAHDTTNYOeIuRhoamG14j7u+f7IBEzuPLL1NUXn6lVT4rIMBHfXsUtcpndQQFn4e22rUHAACt41u3RhwAAADo6CjiAAAAgAEo4gAAAIABKOIAAACAASjiAAAAgAEo4gAAAIABKOIAAACAASjiAAAAgAEo4gAAAIABHP6IewDffr7eXnLvZDY6RrtSX9ukS1X2n9gLAMBXKOIAvsa9k1nL7jptdIx2ZeZnPaUqo1MAANoTlqYAAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGcDU6AACguS63dZLZg7+eb0bT1UZVXK41OgYA3BT+pgeAbxmzh6vKJuw0Oka7Ergu1ugIAHDTWJoCAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABjAKYp4bm6uoqOjNWzYMCUmJqqqqsroSAAAAMB1tfvbF1ZUVGj27NnaunWrQkNDtWzZMi1fvlzz5s0zOhoAoB3q4ushs7u70THajab6elVcump0DKBdavdFPD8/X+Hh4QoNDZUkjRkzRjExMUpOTpbJZDI2HACg3TG7u+vL9S8YHaPd8P9/CyS1ThHvcrubzK6erfJZHUVTY50qLjYYHQPfkMlqtVqNDnEr1q5dq3Pnzmn+/PmSpMbGRn3ve9/ToUOH5O3tbXA6AAAA4Nra/Rpxi8VyzZlvF5d2f2oAAABwYu2+rXbt2lVlZWW27dLSUvn6+srLy8vAVAAAAMD1tfsi3r9/fxUWFqqoqEiSlJaWpsjISGNDAQAAAHa0+zXikpSXl6eXXnpJDQ0NCgkJ0ZIlS+Tn52d0LAAAAKBFTlHEAQAAgPam3S9NAQAAANojijgAAABggHb/QJ+OJjc3Vy+99JLq6+sVFhamRYsWcb90B7JarZo1a5Z69eqlcePGGR2nQ8nIyNAf//hHmUwmderUSb/5zW8UHh5udKwO4Y033tDWrVtlMpnUvXt3LVy4UP7+/kbH6nBycnI0c+ZMHTlyxOgoHcqLL76oPXv2yNfXV5LUo0cPrVy50uBUHcPOnTv1+uuv27avXLmi0tJS5eXl6X/+538MTNZ6WCPejlRUVOixxx7T1q1bFRoaqmXLlqm6ulrz5s0zOlqHcPr0aaWkpOjo0aOaMmUKRdyBzpw5o6eeekrp6ekKDAxUXl6ekpOTlZuba3Q0p3f8+HElJiYqIyNDPj4+WrJkiaqrq20PUYNjFBUVacKECbpw4QJF3MFGjx6tX//61/rBD35gdJQOraGhQU8++aRGjhyphIQEo+O0GpamtCP5+fkKDw9XaGioJGnMmDHKysoSv0s5xpYtWxQfH69HHnnE6Cgdjru7uxYuXKjAwEBJUu/evXXhwgXV19cbnMz59e7dW3v37pWPj4+uXr2q0tJS7krlYLW1tZo5c6ZmzZpldJQOp76+Xp988olee+01RUdHa8qUKTp//rzRsTqkdevWqUuXLk5VwiWKeLtSUlKi4OBg23ZwcLCqqqpUXV1tYKqOY+7cuYqOjjY6Rod05513atCgQZL+vTxo8eLFGjx4sNzd3Y0N1kG4ubkpJydHDz30kD788EPFxcUZHalDmTt3rkaPHq2wsDCjo3Q4paWl+tGPfqRp06YpMzNT9913n5577jkmwBysoqJCr7/+uubMmWN0lFZHEW9HLBaLTCbT1/a7uPDHiI6hpqZGU6dO1dmzZ7Vw4UKj43QoQ4YMUUFBgW1ZlsViMTpSh7Blyxa5urrq8ccfNzpKh9S9e3etW7dOvXr1kslk0rhx43T27FmdO3fO6GgdyrZt2xQZGanu3bsbHaXV0eDaka5du6qsrMy2XVpaKl9fX3l5eRmYCnCM8+fPKyEhQWazWZs2bdJtt91mdKQO4Z///Kc++ugj2/aoUaN0/vx5Xbp0ycBUHcfbb7+tY8eOKSYmRhMnTlRdXZ1iYmJUWlpqdLQO4cSJE9q5c2ezfVarVW5ubgYl6ph27drltP8SRxFvR/r376/CwkIVFRVJktLS0hQZGWlsKMABqqqqNHbsWD388MNasWKFPD09jY7UYZSXl2v69OmqqKiQJGVlZenuu+/W7bffbnCyjmH79u3Kzs5WRkaG1q5dK09PT2VkZCgoKMjoaB2Ci4uLfvvb3+pf//qXJOnNN99UWFhYs2WiaFuXLl3S2bNn1adPH6OjtAluX9iO+Pv7a/HixUpMTFRDQ4NCQkK0ZMkSo2MBbW7Lli06f/689u3bp3379tn2b9iwgULYxiIiIjRp0iQ99dRTMpvNCgwM1OrVq42OBThEr169lJSUpGeffVZNTU0KDg7W7373O6NjdSj//Oc/FRAQ4LT/CsHtCwEAAAADsDQFAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAbtG5c+cUFhamJ5988mtjs2bNUlhYmO0+3I5w7NgxJSYmOuzn3Yj9+/crLCxMu3btarb/6NGjmjt3rqTWzV1RUcEj4QF861HEAaAVeHh46PPPP9cXX3xh21dTU6PDhw87PEt4eLhWrVrl8J97PW+++aaio6O1YcOGZvs/++wz21Miv425AaAt8UAfAGgFZrNZjz76qLKysjRp0iRJ0rvvvqvIyEitX7/e9r79+/drzZo1amhokKenp37961+rT58+amxs1LJly5Sbmyuz2aw+ffooOTlZly9f1ty5c/Xll1+qvLxc3bp108qVK+Xv76/Bgwdr5MiR+uCDD1RcXKyYmBhNmzZNBQUFWrBggbKzs/XRRx/pxRdflMVikSQ988wzGjZsmGbNmiVPT0+dOnVKX375pQYPHiw/Pz9KIZCwAAAHOklEQVS99957Ki8v18KFC9WvXz9duXJFKSkpOnHihEwmkwYMGKDp06fL1dVVq1at0r59++Tm5qbbb79dixcvVmBg4Neuzb/+9S/97W9/03vvvafhw4fr448/1v3336/i4mKtWrVKV65c0ezZsxUbG6sFCxZo69atGjhwoPbu3auAgABJUnx8vCZPnqyQkBDNnz9f1dXVKi8v13e/+12tXLlSHh4eevfdd7VixQp16tRJvXv3bpZh9erVeuedd2Q2m9WjRw+98MILCggI0Lvvvqs1a9bIZDLJbDbrV7/6lR544IG2+t8EAJphRhwAWklsbKwyMjJs2zt37tTIkSNt20VFRVqxYoXWrl2rnTt3asGCBZoyZYpqamr05ptv6u9//7syMjKUnZ2t6upq7dq1S++8847uv/9+vfXWW/rzn/9se8T5V746Ni0tTevXr7c9ivsrqamp+sUvfqH09HQtWrRIf/3rX21jn3zyiTZu3Kg33nhD69evl5eXl9LS0vTUU09p3bp1kqSFCxfKz89PWVlZ2rFjh06ePKn169eruLhYGzdu1I4dO5Senq4HH3xQR48eveZ12bp1qwYNGiR/f38NHz7cNivetWtXJSYmKiIiQosXL7a938fHR0OHDlVmZqYk6fTp07pw4YIGDBigbdu2KTY2Vtu2bdO7776rc+fOKTc3VxcuXNCcOXOUmpqq9PR0devWzfZ5O3bs0IEDB7R9+3ZlZWXp7rvv1qxZsyRJS5cuVXJystLT0zV16lQVFBTc1J85ANwKZsQBoJX07t1bZrNZx48fl7+/v6qrq9WrVy/b+MGDB1VWVqann37ats9kMuns2bP6y1/+opiYGHl6ekqSVq5caXvPRx99pNdff11FRUX6xz/+ofvuu882FhkZKUkKCgqSv7+/Ll261CzTo48+qvnz52v//v368Y9/rOnTp9vGfvKTn8jNzU0BAQHy8vLSgAEDJEkhISGqrKyUJL3//vvaunWrTCaT3N3dlZCQoI0bN2r8+PH67ne/q5EjR+qhhx7SQw89pH79+n3tmtTX19t+CZCkkSNHasyYMSouLlbXrl1bvJbx8fFKSUnRuHHjtGPHDo0aNUouLi6aOXOmDh48qHXr1qmoqEhlZWWqqanRoUOH1KtXL911112SpNGjR9seRf7+++8rLi5OXl5ekqSnnnpKf/jDH1RfX6/HHntMkydP1sCBA/Xggw9qwoQJLWYCgNZGEQeAVjRixAhlZmaqS5cuiomJaTZmsVjUr1+/ZiW7uLhYgYGBcnVt/tfxhQsXZLFYtHHjRh09elSjRo1S37591djYKKvVanufh4eH7bXJZGo2JkkJCQn6yU9+ooMHD+rAgQN6+eWXtWfPHkmSu7t7s/f+d4avMptMpmbbjY2NcnFx0RtvvKFjx47pgw8+0KJFizRgwAD96le/anb8rl27dPnyZS1YsEALFy605dy8efPX3vufIiIi1NjYqKNHjyo7O1tvvfWWJGn69OlqamrSo48+qkGDBqm4uNh2zv957v95Li2dgyT98pe/1KhRo3Tw4EGlp6dr/fr12r59e4u5AKA1sTQFAFpRTEyM9uzZo127dikqKqrZWL9+/XTw4EGdPn1akpSXl6cRI0aorq5O/fr1U3Z2turr62WxWDRv3jy98847ys/P189//nPFxsbK399ff/nLX9TU1HTDeRISEvTpp58qLi5OCxYs0OXLl1VeXn7Dx/fv319vvPGGrFar6uvrtW3bNv34xz/WiRMnFBUVpZ49e+qZZ57R008/rWPHjn3t+LS0NE2aNEnvvfee9u/fr/3792vevHn605/+pJqaGpnNZlsp/m/x8fFasGCBwsLCbLPn+fn5ev755zV8+HBJUmFhoZqamvTAAw/os88+04kTJyRJ6enpts8ZMGCAduzYoZqaGknS5s2b9cADD8jFxUWDBw9WbW2txowZo+TkZJ08eVL19fU3fH0A4FYwIw4ArSgoKEg9e/aUj4+P/Pz8mo3dddddmj9/vqZPny6r1SpXV1etWbNGnTt3VkJCgr744gvFxcXJarXqhz/8ocaOHatu3bpp6dKl+v3vfy83Nzf94Ac/0NmzZ284z4wZM7Ro0SKtXLlSJpNJkydP1p133nnDxyclJWnhwoWKjo5WQ0ODBgwYoEmTJsnd3V2PPvqoRo0aJS8vL3l6eiopKanZsSdOnNCnn36qV155pdn+2NhYrVmzRm+//bb69++v1atXa/LkyRo7duzX3ve73/3OtsRE+vcM9vPPPy8vLy95e3vrgQce0NmzZ9WlSxctX75cM2bMkJubW7MvXD7++OMqLi5WfHy8LBaL/vd//1fLly+Xq6ur5syZoxkzZsjV1VUmk0mLFi362r8UAEBbMVn/+98xAQAAALQ5lqYAAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABqCIAwAAAAagiAMAAAAGoIgDAAAABvj/R+CwwdEZMyAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando a figura\n",
    "fig, ax = plt.subplots(figsize = (12,8))\n",
    "\n",
    "#Setando a cor\n",
    "sns.set_palette(sns.color_palette(\"gnuplot2\"),7)\n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#Criando o gráfico \n",
    "sns.countplot(dados_comb['n_moa'], ax=ax)\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "fig.suptitle('Contagem De Experimentos Por Quantidade De Mecanismos Ativados', fontsize=20)\n",
    "ax.set_xlabel('Mecanismos Ativados', fontsize=12)\n",
    "ax.set_ylabel('Quantidade De Experimentos', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparando os dados por composto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mais_moa = dados_comb[dados_comb['n_moa'] == 7]\n",
    "mais_moa = mais_moa['composto'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados_instavelxcontrole = dados_comb[(dados_exp['composto'] == mais_moa) | (dados_exp['eh_controle'] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando a figura\n",
    "fig, ax = plt.subplots(3,3,figsize = (20,10), sharex=True)\n",
    "\n",
    "#Setando a cor\n",
    "sns.set_palette(sns.color_palette('Set1'),10)\n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#Criando o gráfico \n",
    "sns.lineplot(x='tempo', y='g0', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[0,0])\n",
    "sns.lineplot(x='tempo', y='g1', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[0,1])\n",
    "sns.lineplot(x='tempo', y='g2', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[0,2])\n",
    "sns.lineplot(x='tempo', y='g3', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[1,0])\n",
    "sns.lineplot(x='tempo', y='g4', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[1,1])\n",
    "sns.lineplot(x='tempo', y='g5', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[1,2])\n",
    "sns.lineplot(x='tempo', y='g6', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[2,0])\n",
    "sns.lineplot(x='tempo', y='g7', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[2,1])\n",
    "sns.lineplot(x='tempo', y='g8', data=dados_instavelxcontrole.loc[:, 'tempo':], hue='composto', ax=ax[2,2])\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "fig.suptitle('Alterações Gênicas Ao Longo Do Tempo Do Composto Com Mais Ativações vs Controle', fontsize=20)\n",
    "\n",
    "ax[0,0].set_ylabel('G-0', fontsize=12)\n",
    "ax[0,1].set_ylabel('G-1', fontsize=12)\n",
    "ax[0,2].set_ylabel('G-2', fontsize=12)\n",
    "ax[1,0].set_ylabel('G-3', fontsize=12)\n",
    "ax[1,1].set_ylabel('G-4', fontsize=12)\n",
    "ax[1,2].set_ylabel('G-5', fontsize=12)\n",
    "ax[2,0].set_ylabel('G-6', fontsize=12)\n",
    "ax[2,1].set_ylabel('G-7', fontsize=12)\n",
    "ax[2,2].set_ylabel('G-8', fontsize=12)\n",
    "\n",
    "\n",
    "ax[2,0].set_xlabel('Tempo', fontsize=12)\n",
    "ax[2,1].set_xlabel('Tempo', fontsize=12)\n",
    "ax[2,2].set_xlabel('Tempo', fontsize=12)\n",
    "\n",
    "ax[2,0].set_xticks([24,48,72])\n",
    "ax[2,1].set_xticks([24,48,72])\n",
    "ax[2,2].set_xticks([24,48,72])\n",
    "\n",
    "ax[2,0].set_xticklabels([24,48,72], fontsize=12)\n",
    "ax[2,1].set_xticklabels([24,48,72], fontsize=12)\n",
    "ax[2,2].set_xticklabels([24,48,72], fontsize=12)\n",
    "\n",
    "handles, labels = ax[0,0].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc='upper right', fontsize=20)\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        ax[i,j] = ax[i,j].get_legend().remove()\n",
    "\n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 06: Descobrir se tem algum composto que dependendo da configuração do experimento, ativa ou não ativa algum MOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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>index</th>\n",
       "      <th>composto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87d714366</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9f80f3f77</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5628cb3ee</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3284</th>\n",
       "      <td>c7e535136</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3285</th>\n",
       "      <td>861a693e3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>828099693</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>6e2b418c3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3288</th>\n",
       "      <td>a8ec8311e</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index  composto\n",
       "0     cacb2b860     False\n",
       "1     87d714366     False\n",
       "2     9f80f3f77     False\n",
       "3     8b87a7a83     False\n",
       "4     5628cb3ee     False\n",
       "...         ...       ...\n",
       "3284  c7e535136      True\n",
       "3285  861a693e3      True\n",
       "3286  828099693      True\n",
       "3287  6e2b418c3      True\n",
       "3288  a8ec8311e      True\n",
       "\n",
       "[3289 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compostos_drop = dados_comb['composto'].value_counts() == 1\n",
    "compostos_drop = compostos_drop.reset_index()\n",
    "compostos_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "compostos_drop = compostos_drop.loc[compostos_drop['composto'] == True,'index']\n",
    "compostos_drop = compostos_drop.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>composto</th>\n",
       "      <th>g0</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "      <th>g4</th>\n",
       "      <th>...</th>\n",
       "      <th>c96</th>\n",
       "      <th>c97</th>\n",
       "      <th>c98</th>\n",
       "      <th>c99</th>\n",
       "      <th>eh_controle</th>\n",
       "      <th>24_h</th>\n",
       "      <th>48_h</th>\n",
       "      <th>72_h</th>\n",
       "      <th>n_moa</th>\n",
       "      <th>ativo_moa</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>...</td>\n",
       "      <td>-0.3981</td>\n",
       "      <td>0.2139</td>\n",
       "      <td>0.3801</td>\n",
       "      <td>0.4176</td>\n",
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       "      <td>0</td>\n",
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       "      <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>...</td>\n",
       "      <td>0.1522</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.7371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>...</td>\n",
       "      <td>-0.6417</td>\n",
       "      <td>-0.2187</td>\n",
       "      <td>-1.4080</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>-1.6210</td>\n",
       "      <td>-0.8784</td>\n",
       "      <td>-0.3876</td>\n",
       "      <td>-0.8154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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>...</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.2885</td>\n",
       "      <td>-0.3786</td>\n",
       "      <td>0.7125</td>\n",
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       "    <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",
       "    </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>...</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>0.5258</td>\n",
       "      <td>0.4680</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</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>...</td>\n",
       "      <td>-0.2084</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>-0.2715</td>\n",
       "      <td>0.3689</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</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>...</td>\n",
       "      <td>0.2256</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.3808</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>0.2324</td>\n",
       "      <td>0.4392</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.8531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23685 rows × 883 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose   composto      g0      g1  \\\n",
       "0      id_000644bb2     com_droga     24   D1  b68db1d53  1.0620  0.5577   \n",
       "1      id_000779bfc     com_droga     72   D1  df89a8e5a  0.0743  0.4087   \n",
       "2      id_000a6266a     com_droga     48   D1  18bb41b2c  0.6280  0.5817   \n",
       "3      id_0015fd391     com_droga     48   D1  8c7f86626 -0.5138 -0.2491   \n",
       "4      id_001626bd3     com_droga     72   D2  7cbed3131 -0.3254 -0.4009   \n",
       "...             ...           ...    ...  ...        ...     ...     ...   \n",
       "23809  id_fffb1ceed     com_droga     24   D2  df1d0a5a1  0.1394 -0.0636   \n",
       "23810  id_fffb70c0c     com_droga     24   D2  ecf3b6b74 -1.3260  0.3478   \n",
       "23811  id_fffc1c3f4  com_controle     48   D2  cacb2b860  0.3942  0.3756   \n",
       "23812  id_fffcb9e7c     com_droga     24   D1  8b87a7a83  0.6660  0.2324   \n",
       "23813  id_ffffdd77b     com_droga     72   D1  972f41291 -0.8598  1.0240   \n",
       "\n",
       "           g2      g3      g4  ...     c96     c97     c98     c99  \\\n",
       "0     -0.2479 -0.6208 -0.1944  ... -0.3981  0.2139  0.3801  0.4176   \n",
       "1      0.2991  0.0604  1.0190  ...  0.1522  0.1241  0.6077  0.7371   \n",
       "2      1.5540 -0.0764 -0.0323  ... -0.6417 -0.2187 -1.4080  0.6931   \n",
       "3     -0.2656  0.5288  4.0620  ... -1.6210 -0.8784 -0.3876 -0.8154   \n",
       "4      0.9700  0.6919  1.4180  ...  0.1094  0.2885 -0.3786  0.7125   \n",
       "...       ...     ...     ...  ...     ...     ...     ...     ...   \n",
       "23809 -0.1112 -0.5080 -0.4713  ...  0.0631  0.9171  0.5258  0.4680   \n",
       "23810 -0.3743  0.9905 -0.7178  ... -0.2084 -0.1224 -0.2715  0.3689   \n",
       "23811  0.3109 -0.7389  0.5505  ...  0.2256  0.7592  0.6656  0.3808   \n",
       "23812  0.4392  0.2044  0.8531  ...  0.1732  0.7015 -0.6290  0.0740   \n",
       "23813 -0.1361  0.7952 -0.3611  ... -3.5770 -0.4775 -2.1500 -4.2520   \n",
       "\n",
       "       eh_controle  24_h  48_h  72_h  n_moa  ativo_moa  \n",
       "0                0     1     0     0      1       True  \n",
       "1                0     0     0     1      0      False  \n",
       "2                0     0     1     0      3       True  \n",
       "3                0     0     1     0      0      False  \n",
       "4                0     0     0     1      1       True  \n",
       "...            ...   ...   ...   ...    ...        ...  \n",
       "23809            0     1     0     0      1       True  \n",
       "23810            0     1     0     0      1       True  \n",
       "23811            1     0     1     0      0      False  \n",
       "23812            0     1     0     0      1       True  \n",
       "23813            0     0     0     1      0      False  \n",
       "\n",
       "[23685 rows x 883 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_filtrados = dados_comb.drop(index=dados_exp[dados_exp['composto'].isin(compostos_drop)].index)\n",
    "dados_filtrados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tratamento</th>\n",
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       "      <th>dose</th>\n",
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       "      <th>48_h</th>\n",
       "      <th>72_h</th>\n",
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       "      <th>ativo_moa</th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 883 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, tratamento, tempo, dose, composto, g0, g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, g11, g12, g13, g14, g15, g16, g17, g18, g19, g20, g21, g22, g23, g24, g25, g26, g27, g28, g29, g30, g31, g32, g33, g34, g35, g36, g37, g38, g39, g40, g41, g42, g43, g44, g45, g46, g47, g48, g49, g50, g51, g52, g53, g54, g55, g56, g57, g58, g59, g60, g61, g62, g63, g64, g65, g66, g67, g68, g69, g70, g71, g72, g73, g74, g75, g76, g77, g78, g79, g80, g81, g82, g83, g84, g85, g86, g87, g88, g89, g90, g91, g92, g93, g94, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 883 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_filtrados.drop_duplicates(['composto', 'ativo_moa'], keep=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observamos que eliminando primeiramente todas as drogas que participaram de apenas um experimento e após isso todos os experimentos que tiveram o mesmo composto e que a ativação desse composto não mudou percebemos que não achamos nenhum composto que dependendo da configuração do experimento, ativa ou não ativa algum MOA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 7 : Descobrir se tem algum composto que dependendo da configuração do experimento, ativa MOAs diferentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>5-alpha_reductase_inhibitor</th>\n",
       "      <th>11-beta-hsd1_inhibitor</th>\n",
       "      <th>acat_inhibitor</th>\n",
       "      <th>acetylcholine_receptor_agonist</th>\n",
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       "      <td>False</td>\n",
       "      <td>df89a8e5a</td>\n",
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       "    <tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>23680</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23681</th>\n",
       "      <td>id_fffb70c0c</td>\n",
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       "    <tr>\n",
       "      <th>23682</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
       "      <td>cacb2b860</td>\n",
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       "    <tr>\n",
       "      <th>23683</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23684</th>\n",
       "      <td>id_ffffdd77b</td>\n",
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       "      <td>False</td>\n",
       "      <td>972f41291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23685 rows × 209 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  5-alpha_reductase_inhibitor  11-beta-hsd1_inhibitor  \\\n",
       "0      id_000644bb2                            0                       0   \n",
       "1      id_000779bfc                            0                       0   \n",
       "2      id_000a6266a                            0                       0   \n",
       "3      id_0015fd391                            0                       0   \n",
       "4      id_001626bd3                            0                       0   \n",
       "...             ...                          ...                     ...   \n",
       "23680  id_fffb1ceed                            0                       0   \n",
       "23681  id_fffb70c0c                            0                       0   \n",
       "23682  id_fffc1c3f4                            0                       0   \n",
       "23683  id_fffcb9e7c                            0                       0   \n",
       "23684  id_ffffdd77b                            0                       0   \n",
       "\n",
       "       acat_inhibitor  acetylcholine_receptor_agonist  \\\n",
       "0                   0                               0   \n",
       "1                   0                               0   \n",
       "2                   0                               0   \n",
       "3                   0                               0   \n",
       "4                   0                               0   \n",
       "...               ...                             ...   \n",
       "23680               0                               0   \n",
       "23681               0                               0   \n",
       "23682               0                               0   \n",
       "23683               0                               0   \n",
       "23684               0                               0   \n",
       "\n",
       "       acetylcholine_receptor_antagonist  acetylcholinesterase_inhibitor  \\\n",
       "0                                      0                               0   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               0   \n",
       "...                                  ...                             ...   \n",
       "23680                                  0                               0   \n",
       "23681                                  0                               0   \n",
       "23682                                  0                               0   \n",
       "23683                                  0                               0   \n",
       "23684                                  0                               0   \n",
       "\n",
       "       adenosine_receptor_agonist  adenosine_receptor_antagonist  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "2                               0                              0   \n",
       "3                               0                              0   \n",
       "4                               0                              0   \n",
       "...                           ...                            ...   \n",
       "23680                           0                              0   \n",
       "23681                           0                              0   \n",
       "23682                           0                              0   \n",
       "23683                           0                              0   \n",
       "23684                           0                              0   \n",
       "\n",
       "       adenylyl_cyclase_activator  ...  trpv_antagonist  tubulin_inhibitor  \\\n",
       "0                               0  ...                0                  0   \n",
       "1                               0  ...                0                  0   \n",
       "2                               0  ...                0                  0   \n",
       "3                               0  ...                0                  0   \n",
       "4                               0  ...                0                  0   \n",
       "...                           ...  ...              ...                ...   \n",
       "23680                           0  ...                0                  0   \n",
       "23681                           0  ...                0                  0   \n",
       "23682                           0  ...                0                  0   \n",
       "23683                           0  ...                0                  0   \n",
       "23684                           0  ...                0                  0   \n",
       "\n",
       "       tyrosine_kinase_inhibitor  ubiquitin_specific_protease_inhibitor  \\\n",
       "0                              0                                      0   \n",
       "1                              0                                      0   \n",
       "2                              0                                      0   \n",
       "3                              0                                      0   \n",
       "4                              0                                      0   \n",
       "...                          ...                                    ...   \n",
       "23680                          0                                      0   \n",
       "23681                          0                                      0   \n",
       "23682                          0                                      0   \n",
       "23683                          0                                      0   \n",
       "23684                          0                                      0   \n",
       "\n",
       "       vegfr_inhibitor  vitamin_b  vitamin_d_receptor_agonist  wnt_inhibitor  \\\n",
       "0                    0          0                           0              0   \n",
       "1                    0          0                           0              0   \n",
       "2                    0          0                           0              0   \n",
       "3                    0          0                           0              0   \n",
       "4                    0          0                           0              0   \n",
       "...                ...        ...                         ...            ...   \n",
       "23680                0          0                           0              0   \n",
       "23681                0          0                           0              0   \n",
       "23682                0          0                           0              0   \n",
       "23683                0          0                           0              0   \n",
       "23684                0          0                           0              0   \n",
       "\n",
       "       ativo_moa   composto  \n",
       "0           True  b68db1d53  \n",
       "1          False  df89a8e5a  \n",
       "2           True  18bb41b2c  \n",
       "3          False  8c7f86626  \n",
       "4           True  7cbed3131  \n",
       "...          ...        ...  \n",
       "23680       True  df1d0a5a1  \n",
       "23681       True  ecf3b6b74  \n",
       "23682      False  cacb2b860  \n",
       "23683       True  8b87a7a83  \n",
       "23684      False  972f41291  \n",
       "\n",
       "[23685 rows x 209 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_result_comb = pd.merge(dados_result, dados_filtrados[['id', 'composto']], on= 'id')\n",
    "dados_result_comb.drop(columns='n_moa', inplace=True)\n",
    "dados_result_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>5-alpha_reductase_inhibitor</th>\n",
       "      <th>11-beta-hsd1_inhibitor</th>\n",
       "      <th>acat_inhibitor</th>\n",
       "      <th>acetylcholine_receptor_agonist</th>\n",
       "      <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>...</th>\n",
       "      <th>trpv_antagonist</th>\n",
       "      <th>tubulin_inhibitor</th>\n",
       "      <th>tyrosine_kinase_inhibitor</th>\n",
       "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
       "      <th>vegfr_inhibitor</th>\n",
       "      <th>vitamin_b</th>\n",
       "      <th>vitamin_d_receptor_agonist</th>\n",
       "      <th>wnt_inhibitor</th>\n",
       "      <th>ativo_moa</th>\n",
       "      <th>composto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "</table>\n",
       "<p>0 rows × 209 columns</p>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, 5-alpha_reductase_inhibitor, 11-beta-hsd1_inhibitor, acat_inhibitor, acetylcholine_receptor_agonist, acetylcholine_receptor_antagonist, acetylcholinesterase_inhibitor, adenosine_receptor_agonist, adenosine_receptor_antagonist, adenylyl_cyclase_activator, adrenergic_receptor_agonist, adrenergic_receptor_antagonist, akt_inhibitor, aldehyde_dehydrogenase_inhibitor, alk_inhibitor, ampk_activator, analgesic, androgen_receptor_agonist, androgen_receptor_antagonist, anesthetic_-_local, angiogenesis_inhibitor, angiotensin_receptor_antagonist, anti-inflammatory, antiarrhythmic, antibiotic, anticonvulsant, antifungal, antihistamine, antimalarial, antioxidant, antiprotozoal, antiviral, apoptosis_stimulant, aromatase_inhibitor, atm_kinase_inhibitor, atp-sensitive_potassium_channel_antagonist, atp_synthase_inhibitor, atpase_inhibitor, atr_kinase_inhibitor, aurora_kinase_inhibitor, autotaxin_inhibitor, bacterial_30s_ribosomal_subunit_inhibitor, bacterial_50s_ribosomal_subunit_inhibitor, bacterial_antifolate, bacterial_cell_wall_synthesis_inhibitor, bacterial_dna_gyrase_inhibitor, bacterial_dna_inhibitor, bacterial_membrane_integrity_inhibitor, bcl_inhibitor, bcr-abl_inhibitor, benzodiazepine_receptor_agonist, beta_amyloid_inhibitor, bromodomain_inhibitor, btk_inhibitor, calcineurin_inhibitor, calcium_channel_blocker, cannabinoid_receptor_agonist, cannabinoid_receptor_antagonist, carbonic_anhydrase_inhibitor, casein_kinase_inhibitor, caspase_activator, catechol_o_methyltransferase_inhibitor, cc_chemokine_receptor_antagonist, cck_receptor_antagonist, cdk_inhibitor, chelating_agent, chk_inhibitor, chloride_channel_blocker, cholesterol_inhibitor, cholinergic_receptor_antagonist, coagulation_factor_inhibitor, corticosteroid_agonist, cyclooxygenase_inhibitor, cytochrome_p450_inhibitor, dihydrofolate_reductase_inhibitor, dipeptidyl_peptidase_inhibitor, diuretic, dna_alkylating_agent, dna_inhibitor, dopamine_receptor_agonist, dopamine_receptor_antagonist, egfr_inhibitor, elastase_inhibitor, erbb2_inhibitor, estrogen_receptor_agonist, estrogen_receptor_antagonist, faah_inhibitor, farnesyltransferase_inhibitor, fatty_acid_receptor_agonist, fgfr_inhibitor, flt3_inhibitor, focal_adhesion_kinase_inhibitor, free_radical_scavenger, fungal_squalene_epoxidase_inhibitor, gaba_receptor_agonist, gaba_receptor_antagonist, gamma_secretase_inhibitor, glucocorticoid_receptor_agonist, glutamate_inhibitor, glutamate_receptor_agonist, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 209 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_result_comb = dados_result_comb.drop_duplicates(dados_result_comb.columns[1:], keep=False)\n",
    "dados_result_comb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminando os compostos que só foram utilizadas em um experimento e depois eliminando os experimento que tinham todas as colunas(mesmos composto e mesmos MOAs ativados) não foi achado nenhum composto que dependendo da configuração do experimento, ativa MOAs diferentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resumo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nessa aula vimos a importância de concatenar dados de dois arquivos diferentes e como isso pode ser poderoso e ajudar a retirar informações concretas dos dados, vimos a real ação dos compostos a partir dos dados do resultado e que a maioria dos compostos possui uma ação inibidora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vimos como criar novas colunas e como isso pode ser útil na hora de filtrar as informações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aprendemos a função de um experimento controle, que não pode ativar nenhum mecanismo para ser possível comparar com os demais experimentos"
   ]
  }
 ],
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