exploration-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</th>\n",
       "      <th>g-0</th>\n",
       "      <th>g-1</th>\n",
       "      <th>g-2</th>\n",
       "      <th>g-3</th>\n",
       "      <th>g-4</th>\n",
       "      <th>...</th>\n",
       "      <th>c-90</th>\n",
       "      <th>c-91</th>\n",
       "      <th>c-92</th>\n",
       "      <th>c-93</th>\n",
       "      <th>c-94</th>\n",
       "      <th>c-95</th>\n",
       "      <th>c-96</th>\n",
       "      <th>c-97</th>\n",
       "      <th>c-98</th>\n",
       "      <th>c-99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>b68db1d53</td>\n",
       "      <td>1.0620</td>\n",
       "      <td>0.5577</td>\n",
       "      <td>-0.2479</td>\n",
       "      <td>-0.6208</td>\n",
       "      <td>-0.1944</td>\n",
       "      <td>...</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      droga     g-0     g-1     g-2  \\\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",
       "      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",
       "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_datas = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
    "\n",
    "datas = pd.read_csv(url_datas, compression = 'zip')\n",
    "datas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui mostra diferentes tipos de dados, como id, tratamento, dose, drogas entre outros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explorando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23814, 877)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           com_droga\n",
       "1           com_droga\n",
       "2           com_droga\n",
       "3           com_droga\n",
       "4           com_droga\n",
       "             ...     \n",
       "23809       com_droga\n",
       "23810       com_droga\n",
       "23811    com_controle\n",
       "23812       com_droga\n",
       "23813       com_droga\n",
       "Name: tratamento, Length: 23814, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['com_droga', 'com_controle'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24, 72, 48], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tempo'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['D1', 'D2'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['dose'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['b68db1d53', 'df89a8e5a', '18bb41b2c', ..., '573c787a2',\n",
       "       'b2fe3eca7', 'dd4a96d16'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['droga'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "com_droga       21948\n",
       "com_controle     1866\n",
       "Name: tratamento, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D1    12147\n",
       "D2    11667\n",
       "Name: dose, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['dose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "com_droga       0.921643\n",
       "com_controle    0.078357\n",
       "Name: tratamento, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D1    0.510078\n",
       "D2    0.489922\n",
       "Name: dose, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['dose'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aqui podemos perceber a discrepância entre os dados.\n",
    "\n",
    "Observando a proporção da coluna tratamento, é possível observar uma enorme desproporção no comparativo entre o tratamento feito com droga e o feito apenas com controle.\n",
    "\n",
    "Já nas proporções entre as doses vemos que a diferença é mínima, predominando um balenceamento maior entre os dados. Podemos visualizar isso melhor através de gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a75bf67c48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "datas['tratamento'].value_counts().plot.pie(figsize = (20,8),colors = ['#EE82EE', '#B22222'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a75d79b2c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas['tempo'].value_counts().plot.pie(figsize = (20,8),colors = ['#FFA07A', '#B22222', '#FA8072'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A proporção entre os gráficos dificultou o processo de visualização da diferença, dado a escolha do gráfico ter sido inadequada para o processo, para perceber a diferença de forma visual, podemos plotar um gráfico de barras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.countplot(x ='tempo', data = datas, palette = 'coolwarm_r')\n",
    "ax.xaxis.set_label_text('Horas',fontdict= {'size':14}, weight='bold')\n",
    "ax.yaxis.set_label_text('Observações',fontdict= {'size':14}, weight='bold')\n",
    "ax.set_title('Observações por Hora',fontdict= {'size':20}, weight = 'bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>droga</th>\n",
       "      <th>g-0</th>\n",
       "      <th>g-1</th>\n",
       "      <th>g-2</th>\n",
       "      <th>g-3</th>\n",
       "      <th>g-4</th>\n",
       "      <th>...</th>\n",
       "      <th>c-90</th>\n",
       "      <th>c-91</th>\n",
       "      <th>c-92</th>\n",
       "      <th>c-93</th>\n",
       "      <th>c-94</th>\n",
       "      <th>c-95</th>\n",
       "      <th>c-96</th>\n",
       "      <th>c-97</th>\n",
       "      <th>c-98</th>\n",
       "      <th>c-99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_000644bb2</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>b68db1d53</td>\n",
       "      <td>1.0620</td>\n",
       "      <td>0.5577</td>\n",
       "      <td>-0.2479</td>\n",
       "      <td>-0.6208</td>\n",
       "      <td>-0.1944</td>\n",
       "      <td>...</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>6</th>\n",
       "      <td>id_001bd861f</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>746ca1f5a</td>\n",
       "      <td>2.0440</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>-1.5390</td>\n",
       "      <td>5.9440</td>\n",
       "      <td>-2.1670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>1.1720</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.6486</td>\n",
       "      <td>0.8090</td>\n",
       "      <td>1.5880</td>\n",
       "      <td>2.4670</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.1351</td>\n",
       "      <td>-0.3179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>id_0020d0484</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>0.2711</td>\n",
       "      <td>0.5133</td>\n",
       "      <td>-0.1327</td>\n",
       "      <td>2.5950</td>\n",
       "      <td>0.6980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>-0.4140</td>\n",
       "      <td>0.2532</td>\n",
       "      <td>0.0513</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>1.4250</td>\n",
       "      <td>0.6633</td>\n",
       "      <td>0.4562</td>\n",
       "      <td>-0.9622</td>\n",
       "      <td>0.0260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id tratamento  tempo dose      droga     g-0     g-1     g-2  \\\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",
       "6  id_001bd861f  com_droga     24   D2  746ca1f5a  2.0440  1.7000 -1.5390   \n",
       "7  id_0020d0484  com_droga     48   D1  8b87a7a83  0.2711  0.5133 -0.1327   \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",
       "6  5.9440 -2.1670  ...  0.1855  1.1720  0.8325  0.6486  0.8090  1.5880   \n",
       "7  2.5950  0.6980  ...  0.3230 -0.4140  0.2532  0.0513  0.8600  1.4250   \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",
       "6  2.4670  0.0357  0.1351 -0.3179  \n",
       "7  0.6633  0.4562 -0.9622  0.0260  \n",
       "\n",
       "[5 rows x 877 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_filtrados = datas[datas['g-0'] > 0]\n",
    "dados_filtrados.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigar por que a classe tratamento é tão desbalanceada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         23814\n",
       "unique            2\n",
       "top       com_droga\n",
       "freq          21948\n",
       "Name: tratamento, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por meio da função 'describe' em Python, temos como retorno a estatística descritiva das variáveis. Sendo esta, do tipo categórico. Fazendo uma rápida análise, selecionei apenas a Série tratamento e solicitei as estatístas da variável, onde tive como retorno os seguintes dados\n",
    "\n",
    "Count: retorna o número de itens da coluna\n",
    "\n",
    "Unique: retorna a quantidade de valores únicos (no caso, temos 2, sendo o tratamento com droga e o tratamento com controle)\n",
    "\n",
    "top: retorna o que tem maior valor de ocorrências, no caso, o tratamento com droga\n",
    "\n",
    "freq: retorna a frequência do tratamento com droga "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.value_counts of 0           com_droga\n",
       "1           com_droga\n",
       "2           com_droga\n",
       "3           com_droga\n",
       "4           com_droga\n",
       "             ...     \n",
       "23809       com_droga\n",
       "23810       com_droga\n",
       "23811    com_controle\n",
       "23812       com_droga\n",
       "23813       com_droga\n",
       "Name: tratamento, Length: 23814, dtype: object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "com_droga       92.164273\n",
       "com_controle     7.835727\n",
       "Name: tratamento, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui observamos que\n",
    "\n",
    "tratamento com droga = 92.16%\n",
    "\n",
    "tratamento com controle = 7.83%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cacb2b860'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count2= datas[datas['tratamento'] == 'com_controle']\n",
    "count2['droga'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui vemos que só há um tipo de droga no tratamento com controle, logo, podemos ver que a discrepância dos dados acontece devido a isso. Uma vez que temos aproximadamente 92% no tratamento com drogas e aproximadamente 8% no tratamento com controle (sendo droga única)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotar as 5 últimas linhas da tabela"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</th>\n",
       "      <th>g-0</th>\n",
       "      <th>g-1</th>\n",
       "      <th>g-2</th>\n",
       "      <th>g-3</th>\n",
       "      <th>g-4</th>\n",
       "      <th>...</th>\n",
       "      <th>c-90</th>\n",
       "      <th>c-91</th>\n",
       "      <th>c-92</th>\n",
       "      <th>c-93</th>\n",
       "      <th>c-94</th>\n",
       "      <th>c-95</th>\n",
       "      <th>c-96</th>\n",
       "      <th>c-97</th>\n",
       "      <th>c-98</th>\n",
       "      <th>c-99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>0.1969</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>-0.8121</td>\n",
       "      <td>0.3434</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>-0.3246</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>0.5258</td>\n",
       "      <td>0.4680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>0.4286</td>\n",
       "      <td>0.4426</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>-0.3195</td>\n",
       "      <td>-0.8086</td>\n",
       "      <td>-0.9798</td>\n",
       "      <td>-0.2084</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>-0.2715</td>\n",
       "      <td>0.3689</td>\n",
       "    </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>0.5409</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.7343</td>\n",
       "      <td>0.2807</td>\n",
       "      <td>0.4116</td>\n",
       "      <td>0.6422</td>\n",
       "      <td>0.2256</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.3808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>0.2324</td>\n",
       "      <td>0.4392</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.8531</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1105</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>-0.2012</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>1.5230</td>\n",
       "      <td>0.7101</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "    <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>-3.3890</td>\n",
       "      <td>-1.7450</td>\n",
       "      <td>-6.6300</td>\n",
       "      <td>-4.0950</td>\n",
       "      <td>-7.3860</td>\n",
       "      <td>-1.4160</td>\n",
       "      <td>-3.5770</td>\n",
       "      <td>-0.4775</td>\n",
       "      <td>-2.1500</td>\n",
       "      <td>-4.2520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose      droga     g-0     g-1  \\\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",
       "          g-2     g-3     g-4  ...    c-90    c-91    c-92    c-93    c-94  \\\n",
       "23809 -0.1112 -0.5080 -0.4713  ...  0.1969  0.0262 -0.8121  0.3434  0.5372   \n",
       "23810 -0.3743  0.9905 -0.7178  ...  0.4286  0.4426  0.0423 -0.3195 -0.8086   \n",
       "23811  0.3109 -0.7389  0.5505  ...  0.5409  0.3755  0.7343  0.2807  0.4116   \n",
       "23812  0.4392  0.2044  0.8531  ... -0.1105  0.4258 -0.2012  0.1506  1.5230   \n",
       "23813 -0.1361  0.7952 -0.3611  ... -3.3890 -1.7450 -6.6300 -4.0950 -7.3860   \n",
       "\n",
       "         c-95    c-96    c-97    c-98    c-99  \n",
       "23809 -0.3246  0.0631  0.9171  0.5258  0.4680  \n",
       "23810 -0.9798 -0.2084 -0.1224 -0.2715  0.3689  \n",
       "23811  0.6422  0.2256  0.7592  0.6656  0.3808  \n",
       "23812  0.7101  0.1732  0.7015 -0.6290  0.0740  \n",
       "23813 -1.4160 -3.5770 -0.4775 -2.1500 -4.2520  \n",
       "\n",
       "[5 rows x 877 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proporção das classes tratamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "com_droga       0.921643\n",
       "com_controle    0.078357\n",
       "Name: tratamento, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['tratamento'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a75db8f308>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5aUb1AFB6vKSwzMAnboR9LbiSv9SPuwoAiJ3/V19GJu4ymh5hXwOV1ypsOQEASQql8rNlOurFjLCPWFgOVX6GKXwA+JD3jEdHvZgR9hEzjpG/jCl8APhQ8HYgW2RkHyfCPkLWWvkv+byDBYBPKC8sy/oEflwI+wjZsmUKHwA2wnvWE+v04kPYR8gYo8pyhvUA8EnhmlDBu+y5jwthHxEbWnkveBLbSQFgo8pPcThOXAj7qPhSeRFT+ACwKf5SX3LjrqI5EfYRsYFV8DpTVACwKbZkFbzHz8k4EPYRsIGV9xeOsgWAzfGX+pyEF4O6DftzzjlHt99+e9xlbJlAhD0AbAH/JV82IOx7W92GfSOxZcvJTgCwBYK3AhnDHrzeltrcF1hrddlll2nevHlyXVcnnniiJk+erPPPP18dHR1qaWnReeedp7333lvnnHOO8vm8lixZorVr1+qss87SXXfdpaVLl2ratGk655xzPvVxfvzjH+vhhx/W4MGDFQSBJkyYoJUrV+q0005T//79lcvl9Ktf/UoXX3yxFixYIGOMjjnmGH3961+XJP3sZz/TnDlz1L9/fw0aNEiHHXaYTjjhBF1++eVasGCB1qxZo8GDB+vyyy/XwIEDI/kG2tDSMQ8AtpSV/Fd9ZcZm4q6kqWw27B944AEtXLhQs2fPlu/7OvnkkzVr1iydffbZmjFjhhYtWqQzzjhDc+bMkSS98847uvXWW3XHHXfo3HPP1Zw5c5TNZjV58mSdfvrpam9v3+jjzJkzR0uWLNE999yjzs5OHXPMMes/t3z5cv3yl7/UiBEjdNNNN+mtt97S3XffLc/z9KUvfUljx45VGIZ6+umndc8996hYLOr444/XYYcdphUrVuiVV17RLbfcIsdx9L3vfU933323vvrVr0byDbSelf8iYQ8AW8p/wVd6p7RMlhF+b9ls2D/55JP6/Oc/r0wmo0wmo1mzZmnq1KmaMWOGJGn8+PHq27evXnnlFUnS5MmTJUnDhg3TmDFjNGDAAElSv379tGbNmk2G/RNPPKEZM2YonU5ru+22W38dSRowYIBGjBghSfrzn/+s448/Xq7rKp/P6+ijj9aCBQsUBMEGdU6bNk2StNNOO+lf//Vf9fvf/17Lly/XokWLtOOOO27r9+tvmLSRv4KwB4At5b/CFrzettl79qlUaoP7K6+//rqs3XBxhbVWQVC9Z51Opzf4u1vKGLPBdT/+d3O53Po/h+GGXWs+fGzHcf7mc5L03HPP6Wtf+5rCMNQRRxyhadOm/U39PRGsDiTW5gHAFrMFq7CTDmS9abNhf8ABB2ju3LnyfV/FYlFnnnmmjDGaO3euJGnRokVavXq1xowZ06NCDjzwQN1///3yPE9r1qzRY489ttGvmzRpku68804FQaBisajZs2dr4sSJOuiggzR37lx5nqdCoaCHH35Yxhg9+eSTmjBhgk466SSNHDlSDz/88Po3Jj1lAyv/r4zqAWBr+X/1OeO+F2126D19+nQ999xzOuGEExSGoU499VRNnDhRF1xwgWbOnKl0Oq2ZM2cqk+nZYotp06bp2Wef1VFHHaWBAwdq55133ujXnXjiiXr11Vd17LHHyvd9HX300Zo+fbok6ZlnntHxxx+vvn37avDgwcpmszryyCP1rW99S0cffbQkac8999TKlSt7VOt6vuS/TNgDwNbyX/SVGZ+RyXHfvjcYG+WcdoyeeeYZvfrqqzr++OPl+75OPPFEXXzxxdptt91q9pjWt+r4aQf98AFga7lSv+/1k0kR9r1hy2+qR+Cpp57ShRdeuNHPXXfddRoyZMg2X3vUqFG68sordf3118taq+OOO66mQS9JlbcqBD0AbItAqrxRUXqn9Oa/Fj2WmJF9b7MVq9KjJZXml+IuBQAaUvaArPKH5WUyjO5rjQ5628hWrCqvcXY9AGwr/2VfIud7BWG/jUzaqPImYQ8A2yp8P5T1mVzuDYT9NgrfCyXa4QNAjwTv8IO0NxD228CGli13ABCByusV9tv3AsJ+G1jPqrKCKXwA6Kng7UDWI+xrjbDfBiZtVHmdsAeAngreCWQcVunVGmG/DaxnZUu8EwWAngrfD0miXsC3eBuE79NJBwCiEn7Az9RaI+y3QbCK1aMAEBW2MdceYb+VrG8JewCIUOWNCov0aoyw30o2sNUz7AEAkQhWBWy/qzHCfisZ1xD2ABCh4J1AJs2K/Foi7LeWkWyBd6AAEBlPskV+rtYSYb+VwjWsGgWAqNE2t7YiC/vu7m59//vf12GHHabJkyfr3HPPVaFQiOrydYMnJABEr7KStrm1FFnYX3LJJfJ9X1dddZWuvvpqGWN04YUXRnX5umADq+Atwh4Aokbb3NpKRXWhxYsX6+67717/3z/60Y/0d3/3d1Fdvi5Yn5X4AFALwSra5tZSZCP7IAgUhh/dzw7DUK7rRnX5umCMUfAeYQ8AUQvXhBEOP/FJkX1rJ02apDPPPFMnnXSSJOnmm2/WhAkTorp8fUjTKhcAasJK8iVl4y4kmYy1NpKbJJVKRVdffbUee+wxBUGgQw45RKeffroymUwUl68LYWeoNf+5Ju4yACCR+ny7j9x+yZoRrheRhf2sWbN08sknb/Cx6667Tl//+tejuHxd8F/1VbgxeTsMAKAetH+1XanhzOXXQo+/qzfffLNKpZJuuOEGlcvl9R/3fV+33HJLosI+XM0UPgDUSljgZ2yt9DjsU6mUli1bplKppGXLlq3/uOu6Ouecc3p6+bphreWJCAA1RNOy2olsGn/evHmaNm1aFJeqS9a3Kv5PUeUny5v/YgDAVssemFV+al7GZQte1CK7OTJ+/HhdeeWV6ujo2ODj//Zv/xbVQ8TKhla2RMMHAKgV22VlK5awr4HIwv673/2ucrmcxo0bJ2MS+A9lRdgDQA2FhbC6BQ+Riyzs3377bd1///1RXa4uhUXuJwFArdgukr5WIuugN2zYMHV3d0d1ubpjjGFkDwA1FBZCpvBrJLKR/eDBg3XcccdpwoQJyuVy6z+elHv2cpjGB4Bast1WoqdOTUQW9sOHD9fw4cOjulz9cQl7AKgpWubWTGRb7ySpVCppxYoVGjNmjMrlsvL5fFSXjp0NrDou7tj8FwIAthktc2sjsnv2ixcv1rRp0/SNb3xD77zzjqZMmaKFCxdGdfnYWZ9RPQDUGov0aiOysP/JT36iG264Qf369dPQoUP105/+VBdddFFUl4+fF3cBAJB8YRe7nmohsrAvlUraZZdd1v/3oYceqiBIztnvtsy7TQCouUrcBSRTZGGfSqW0Zs2a9Q11XnnllaguXRfCEu82AaDWbMjAqhYiW43/zW9+U6eccopWr16ts846S/Pnz9cPf/jDqC4fO9vNExAAai45E8J1JdLV+CtWrND8+fMVhqEOPPBA7bzzzlFdOnblZ8rqvie5TYMAoB60HNWi7GfYexe1yEb2ktTS0qIJEyZIqh4J+9JLL21wH7+hMYsPALXHyL4mIgv7Sy65RDfddJPa29v14WSBMUYLFiyI6iHiFenbIgDAxnDPvjYii7AHH3xQjz32mPr37x/VJetLOu4CgK2XGsm7VDQW00pv/FqI7CfByJEj1adPn6guV3c4nAGNxhniqP1L7ZIk74MPFFbY04T657bSPa8WIgv7L33pSzrllFM0ceJEpVIfXfZb3/pWVA8RK5Mi7NFYwlWhCv9dUHaqFAYVPX/RRXrznnuk6NbkApEbd+652vm00+IuI3Ei22d/3XXXqa2tTZ2dnfrggw/W/0oMZkPRgPwlvgpX+QoXtmrvH16kqQ8+qEGHHBJ3WcAmGZeRfS1EFmHFYlE333xzVJerO0zjo5GVnyir/ISUmzZU+195lTpfelHPXnCB1jz7bNylARswKUZWtRDZyH7UqFFaunRpVJerP7zZRAKU5pXUOdNT3uymg2bdrAm//KVaR46MuyxgPUb2tRHZW6i33npLX/ziFzV8+HBlMpn1H589e3ZUDxErRvZIjIpUvLso5aT+xx2oQ++9V2/ee69euPRSld99N+7q0OQY2ddGZN/Vs846K6pL1SfebCJpSlLXLUU5/R0NOebzGvbQkVp+44168aqrVCkU4q4OTSrd3h53CYkUWdhPmDBBHR0dKhaLstYqCAK99tprUV0+foQ9Eir8IFTXb8pyhjva8Zj/q5Enn6xlV12l5TfcoNDjbGf0rky/fnGXkEiR9cb/+c9/ruuuu06S5LqufN/XLrvskphp/LAz1Jr/XBN3GUDNpcamlJtmFBpPS378Y6284w4ppF80esfk2bPVd9y4uMtInMgW6N1111166KGHdMQRR2ju3Lm65JJLktMXX4rwOwXUt8qyigpX+6osyGrP/3e+Dv/jQxpy+OFxl4UmkWIavyYii7DttttOgwcP1ujRo7V06VIdd9xxWrZsWVSXjx/T+Ggy3kJPhZm+zPIB2vdnl2vy7Nnqv99+cZeFhEu1tsZdQiJFFvapVEqvvfaaRo8eraeeekqVSkXlcjmqy8fOOKzGR3MqPVJS5xWessXRmnT9DZr029+qbcyYuMtCQqXy+bhLSKTIwv4b3/iG/v3f/11TpkzRgw8+qClTpmjSpElRXT5+TOOjmYVS8b6iOq8sq8+AfTX5jju1789/rvz228ddGZLEGDlZzrKvhcgW6K1atUpDhgyRVO2mt2LFCjmOo7Fjx0Zx+djZwKrj0g7Jj7sSoA60S63HZeUOcbTitlu17Ior5Hd0xF0VGlyqrU1HPPmknI/1akE0ejxe7ejoUEdHh/7pn/5Ja9asUUdHh8rlsgYOHKjvfOc7UdRYHyqS08bwHpAkdUpdN5ZVuLGsEdP/XtMee0xjvvUtuUzBogfS7e2czlgjPd5nf/bZZ2v+/PmSpIkTJ3504VRKRxxxRE8vXzestXLaHYUfsAUJ+FC4KlTXr8pKjUpp9Cn/pNFf/aqWXnaZXrvtNll+aGMrpfr0kQ2CuMtIpMim8c8991xdcsklUVyqLoWlUN33dct/nnl8YFPSe6WVPVSqlAt6/sIL9dYDD8RdEhrIgEmTdMA11yjdp0/cpSROZGEvaaMd9A4++OCoLh8rW7Eq/rGo8p+Ts8MAqJXsQVmlD7AqrXpTz/7gB3pvwYK4S0IDGPGFL2iv73+f7Xc1EFm73CuuuELXXnutpGR20DMpI6cv9+yBLVH+U1nlP0n5I3bQhGuv05olz+u5H/5Qa5csibs01LHWHXZg3UeNRJZed955Z7I76Ely+hP2wNYozimq84qy2vJ76eDbbtP+11yjlh12iLss1Km2nXeWcfg5Wwt00NsKTh+ehMBWq0jddxTVeXVJ2+18iKbc/4D2vugiZQYMiLsy1JnWnXaKu4TEooPeViDsgR7olrpnlVS4vqztP3uMDn/kEe169tlyuT+LdXJDh8ZdQmJFll7//M//nOwOepJMztBJD+ih8L1QXdeX1X2br5Ff+LKmz5+vUf/4jzRSaXLGdZXu2zfuMhIrstX48+bN07Rp0yR91EFv1113lTHJ6Slvy1Zrf7lW4fvstQeikt49rexhUhCUtOTii/TG7NlSdJuE0CDy22+vKXPnKtXSEncpiRTZOPXyyy9f/+d8Pq/ddtstUUEvrWus04+hPRAl/wVfhat8hU+3aK8LLtTUefM0+NBD4y4LvSw/fDiNmGoosq13Y8eO1TXXXKP9999fLR97Z7bHHntE9RCxM44h7IEaKT9ZVvlJKXfYEO13xUwVXn5Zz/7gAnUsXhx3aegFLSNGyLicJV4rkU3jjxs3TkM/sbiiWCxqQcKaaZQeL6n4YDHuMoBkc6T83+WVGmv1/pNP6LkLL1TX8uVxV4UaGvPtb2vXb3+bwK+RHo/sO9addLXLLrvot7/9ray1MsbI932dcsopPS6w3riDeCICNRdKxdlFKSf1PXaCDp19j9584H4tvfRSlVatirs61EC/Pfck6GsosoNwjDE68MAD13/cdd1EHYTzIWcg0/hArylJ3beW5PR1NOTYz2nY/3xOr950k5ZdeaUqnZ1xV4cI9R03Lu4SEo2DcLaSDaw6LuuQvLgrAZqPs72j/FEpOW3Ssquv0vIbblCYsH4ezcjJZvX5xYvlpNNxlwtdyA8AABl2SURBVJJYkR6E0wzCUqiuW7tUeY1Vo0BcUruklJtuZB1fS376E628/XaORm1gfffaSwfeeKPS7e1xl5JYzElvJZMycodxXwmIU+WligrX+PLnZ7THOf+mwx56SEOnT4+7LGyjvuPGcb++xgj7rWRSRqlRke1YBNAD3iJPhZm+9GJ/jb/0Z5p8773abv/94y4LW6nfPvvQTKfGCPttkBpG2AP1pPxYWYUrPGULIzXx19frwN/dpPaxY+MuC1uo//jxcZeQeIT9NjAZI9OSrO6AQMMLpeL9RXVeWVZ73/E65Pbbtd8VM5UfNizuyvBpjFHryJFxV5F4hP02sBXLfXugXnlS938XtfbakgaOm6qpcx/UnhdcoEz//nFXho1o2XFHFlf2AsJ+G5iMUWoEU/lAXeuUun5XUuE3ZQ0//As6/NFHNfY735Gbz8ddGT6m7+67E/a9gLDfBsYxSu1E2AONIHw3VNevyir+d6BRJ39N0+f/STudcopMitdwPWBxXu8g7LdRaig/KIBGUnm1oq5f+Cr/0dHuZ3xX0x59VNsfeaSUsNM5G82gQw5h210voKnONrKe1dpr1yrs4Gx7oBFlD8wqPSFU6d1Veu6CC7T6T3+Ku6Sm42Qy1c55mUzcpSQeI/ttZcUiPaCBlReUVfi5r/S7w3XANdfq4NtuU98EHcndCPrtvbcC2h33CsJ+W2Wk1A5M5QONrvhgUZ0zy2pJ76GDbrlVB1x7rVp22inusprCgIkT5WazcZfRFAj7bWQMi/SAxKhIxbuK6rympP4jD9aUe+/T3pdcouzAgXFXlmiDp05lCr+XEPY94A5w+Q4CSdItdd9cUuHXZW1/0FE67OGHtdt3v6tUW1vclSWOSaeb9ljbv/zlL7r00ku36u/MnDlTM2fO3ObHJKp6wFYso3sggcL3Q3VdX1b3Lb5GHv8lTfvf+Rr9ta8xCo1Q//HjFXrNeVb4Sy+9pPfee69XH5Ok6gGTMcqMy6iynONugSQKVgYqXBcotVtKY7/xHe3yjW9oycUXa+Xdd0shO3F6YtAhh8jN5SK5lrVWl112mebNmyfXdXXiiSdq8uTJOv/889XR0aGWlhadd9552nvvvXXOOecon89ryZIlWrt2rc466yzdddddWrp0qaZNm6Zzzjlnqx7ny1/+spYvX77Jx2pra9Pzzz+vVatW6fTTT9f06dN1xRVXqLu7W9dcc42GDBmiO+64Qx0dHZo6dapOPfVUnXfeeXrzzTeVSqX0L//yL5o8efIGdTz66KO64oorVKlUNGLECF144YXqv5kOkYR9DxjHKL1bWro37koA1FJlaUWFpVJ2v7z2PP8HGnvGGXruBz/QOw8/HHdpDWvotGly0ulIrvXAAw9o4cKFmj17tnzf18knn6xZs2bp7LPP1owZM7Ro0SKdccYZmjNnjiTpnXfe0a233qo77rhD5557rubMmaNsNqvJkyfr9NNPV3t7+xY/zpFHHqnvfve7+vrXv77Rx3r77bc1a9YsLVu2TKeeeqq+8IUv6Dvf+Y6eeOIJffOb39Ttt9+uVatW6b777lMqldIZZ5yhSZMm6Stf+Ypef/11nXTSSbrzzjvX1/D+++/rZz/7mX7729+qb9++uuWWW3TZZZfpoosu+tTvEWHfQ8Y1coe4ClbR7hFIuvLTZZWflnJTBmnf/7xCXa++omcvuEAdixbFXVpDcVtb1TZqVGTXe/LJJ/X5z39emUxGmUxGs2bN0tSpUzVjxgxJ0vjx49W3b1+98sorkrR+pDxs2DCNGTNGAwYMkCT169dPa9as2WTYf/Jx7rrrLnV1dem1117b5GMdfPDBMsZo7Nix6ujo2Oh1x40bp9S6jo6PP/64fvSjH0mSdthhB+2zzz5avHjx+q9dvHix3nrrLZ166qmSpDAM1bdv381+jwj7nnKl9G5pwh5oIqWHS9KjUv7IMTrwxhv1/tNP6/kLL1Th5ZfjLq0hDD7kEAXlcmRrIFKplMzHOiG+/vrr+mS/OGutgnU9+NMfm1FIbUXb5E8+zsqVKzcatB9/rOy6rYXmUzo15j52O+PT6pakIAi077776he/+IUkqVwuq6ura7O1s0Cvh0zKKLMHi3aAphNKxXuK6ryqrL6D99fku+7SZ/7jP5QbOjTuyureiOOOU3oTo+dtccABB2ju3LnyfV/FYlFnnnmmjDGaO3euJGnRokVavXq1xowZE+njnHbaaVq9erVGjBixVY/luq4qlY2v9Zo0aZL+8Ic/SKq+aVm4cKHGjx+//vP77LOPFi1apOXLl0uSrr76av30pz/dbO2M7CPg9HVk2o1sJ52HgaZTkrpvK8np62jQsTO0/bwZWnHzzVo2c6b8tWvjrq7umHRagz772UivOX36dD333HM64YQTFIahTj31VE2cOFEXXHCBZs6cqXQ6rZkzZyrTw5mEjT3OqFGjdOmll27VY+2999668sorddlll2n06NEbfO68887T+eefr9tvv12S9KMf/UiDBw9e//lBgwbp4osv1plnnqkwDDVkyJAt2sZHb/wIWM+qe163vKebcxsJgI84Qx3lj0rL6WP14jXX6JVf/1ohLWHXGzR5sva74opIR/bYPMI+IpXXKur8TWfcZQCoE6mdU8rNcGRdTy9ceqle/8MfOLdd0vjLLtOIY4+VcerzLvJTTz2lCy+8cKOfu+666zRkyJBerigahH1EbMWq42cdEoN7AB+T2SejzGSrSvdaPXfhhXp73b3dpuQ4+tzChYzqY1Cfb60akK1YpXeOZs8ogOTwFnsqzPRl/9pP4396mQ697z4NmDAh7rJisd2++0qfsiodtUPYR8TJOazKB7BJ5f8tq/CfnjJrd9KEX/5KB86apfZdd427rF417KijIuuah63DNH6ErGfV8dMOie8ogE+TllqOzik1Wlr18CNacsnFKr7xRtxV1dyMJ55Qdl0DG/QuRvZRCjnjHsAW8KXu20ta+4uSBu52qKbOnau9fvhDZbbbLu7KaqbPuHGM6mNE2EcpI6X34L49gC1UkLp+V1LhhrKGTTlehz/yiMaecYbclpa4K4vcDl/8IqcGxohp/IjZ8rpV+eywAbCV3J1c5T7nyuQCLb38cq24+WZZ34+7rB4z6bSOePJJVuHHiJF9xKysMuN49wpg6wUrAnVd66k8z9Fu3z5b0x59VMOOOqrhV7APmTq14f8fGh0j+xqorKqo8zoa7ADomeykrNITQpXfe1fPXvB9rZ4/P+6StslBN9/ctNsN6wVhXwPWs+q8vlPBO8zlA+i53LSc0nuF6nxxmZ79wQ+05tln4y5pi+WGDNFhDz0kd93pb4gH0/i1kJKyE3liA4hGaV5JnTM95d3dddCsmzXhv/5LrSNHxl3WFtnxH/5BYkwZO0b2NWJ9q47/oH0ugIjlpdZj83JHWL1x771aetllKr/7btxVbZwxmr5ggXKDBsVdSdNjZF8rVnTUAxC9otR1S1GFX3kaOvFIHfbQQ9r9e99Tqq0t7sr+xoCJE5XK5+MuAyLsa8ZkjHIH0UACQG2EH4TquqGsrps97XjcKZo2f75Gn3ZaXe1lH3XqqYnsGdCImMavIetZdd7YqeBNFuoBqK3U2JRy04xClbXkxz/WyjvvlMIwtnrSffpo+uOPszCvTjCyr6VUdesMANRaZVlFhat9Vf6c057nfV+H//GPGjx1amz17HjSSbIxvtnAhhjZ15itWK25fI1siW8zgN6Tm5JTanyo7tde1bMXXKAPFi7stcc26bRm/PnPyvTt22uPiU/HyL7WQimzT/3cQwPQHEoPl1S4wlO2tLMm3fAbTfrNb9S2yy698tjDjzlGTopDweoJI/teEK4Ntebna+IuA0Czykgtx+aU2kl6e948LfnJT1R6663aPJYxOvyRR9QyfHhtro9twsi+F5isUWon3uUCiIkndf++pM5ryxq01+E67MEHtce//7vSNZhmHzxlijL9+kV+XfQMI/teYK1VZUVFhRsLcZcCAHKGOMofnZbTx+qlX/xCr/z61wpKpUiufchdd6nfnntGci1Eh5F9LzDGKDUsJXd7N+5SAEDhqlBdvyyreEeo0ad+Q9Pmz9eOJ50k4/bsZ1S/8ePVNnp0RFUiSozse4kN143uf8foHkB9Se+VVvZQqVIq6PkLf6i35szZputMvP56DfrsZ2UcxpH1hrDvRdaz6vxNp4K3abIDoP5kD8oqfYBV8e039NwPfqD3Hn98i/9u68iROvTee+Xm6Bxaj3j71ZtSUn4afaIB1Kfyn8oq/NxTpmNHTbjuv3TQzbeoz+67b9HfHXP66TJst6tbjOx7mfWtOm9gdA+gzqWklmPyckdbvfvoo3r+4otVXLlyo1/assMOmvLAA4zq6xhh38u4dw+gobSu26M/TFp5551aevnl8t57b4Mv2e/KKzV02jQ56XRMRWJzCPsYWN+q87cckAOgcTgDHOWPScsZYPTKr36ll667TkFXl9rGjNHkO+6Qy1G2dY2wj4G1VsGbgTp/3Rl3KQCwVdwdXeU+58rkA/315z/XkKlTNeDAA+X0cNseaouwj4n1rAq/L6jySiXuUgBgq6V3Tyt7uOTkXEb1DYDV+DExGaOWz7fEXQYAbBP/BV+mnJPDoryGQNjHyGlzlN6TBS0AGk96t7Tc7VwZY+IuBVuAsI+RyRi1TG/hXwFAY3Gkls+1yGQI+kZBzMTMZIyy+2XjLgMAtlj2gKxMlqBvJIR9zEzGKD81L9PCCwdA/TM5o/yheUb1DYawrweu1HIUi/UA1L/coTmSowHxT1YHTMooPSqt1C70lQZQv5yBjrKfycqkGdU3GsK+TpiMUesxrVIm7koAYCOM1HpCq0TvnIZE2NcRkzFqmcZ0PoD6k52UldvflXEY1Tciwr6OmLRRZu+M3GG8dQZQP5ztHBblNTjCvs6YtKlOlfEvA6BOMH3f+IiUOuS0Osp9lhaUAOKXnZCVO4Dp+0ZH2NchkzHKHZSTM4B/HgDxcfo51T4gTN83PNKkXrlS63GtcVcBoIm1ntAqsSM4EQj7OmUcI3egq8y+7MUD0Psy+2XkDmL6PikI+zr24UE5po0XG4DeY/pUtwEzfZ8chH29c6XWo5nOB9B7Wo9n9X3SEPZ1zrhGqR1TnHsPoFdkxmeUGpqScRnVJwlh3wBMxqj171rlDOKfC0DtmHajliOYvk8i0qNRpKW2k9ronQ+gNuh9n2iEfYMwxshpcaovRgCIWO7wHNP3CUbYNxCTNkrvlFZ2UjbuUgAkSHr3tHL755i+TzDCvsGYjFF+Sl6pHeh0AaDnnEGOWo9p5Yz6hCPsG5BJG7We2CrTyosTwLYzWaP2/9susdkn8Qj7BmUyRm3/p00i7wFsCyO1/kOrTN7IGH6QJB1h36CMW22nm5+ej7sUAA0oNzWn1LCUTIqgbwaEfQMzGaPsZ7JK78YcHIAtl941rdwEFuQ1E8K+wZmMUeuxrXK2458SwOY5Ax21HseCvGZDQiRBSmo7uY1FNgA+XVYsyGtShH0CGMfIaXPUeiwNdwBsWtvft8m0sCCvGRH2CWHSRumd08ofwYI9AH8rNzWn1HAW5DUrwj5BTMYoOz6r7GfpsAfgI+nd0spNZEFeMyPsE8ZkjPIH55XZjxNzAEip0SkW5IGwTyKTMWqZ3qL0OFbhAM0stWOqep+eoG96hH1CmbRR6zGtSu1MD32gGbnDXLWd1MbUPSQR9olm0kZtX2yTuwMHVAPNxB3iqv2UdoIe6xH2CWcyRu0nt8sdQuADzcAZ6Kjt1DaJZTv4GMK+GaSltlPb6LIHJJzT31H7l9tlsuylx4b46d8EjDHVEf6X22Xa+QEAJJHpY9T+j+0yOYIef4uwbxLGMTL5dT8M8vwgAJLEtBr1+Uqfanc8h9c3/hZh30SMa+S0V6f5uJ8HJIPJG7V/pV2mlaDHphH2Tca4Rk6/j+7rAWhcJludrXP6ODIur2dsGmHfhEzayB3oqv206mgAQAP6cOFtP4Iem2estTbuIhAPG1jZbqvOGzoVdoRxlwNgC5kWo/Yvtcvp79AdD1uEsG9yNrSyZavO33YqfIfAB+rd+ttwrYYRPbYYYQ9ZayVP6rypU8EbQdzlANgEd6irtlPaqvvoWYyHrUDYYz3rWRX+UFDl5UrcpQD4hNSolNr+oU1Ki3302GqEPTZgfavuB7vlPe3FXQqAddJ7ptV6FMfUYtsR9vgb1rMqLyyr+GAx7lKAppf9bFb5z+YJevQIYY+Nsp6Vv9xX1393SdzGB3qfI7Uc26LM2Ayn16HHCHtskvWtgtWBCjcVZIs8TYDeYvJGbSe3yR3oEvSIBGGPT2Ur6/bi39ip8H225gG15gxw1P6l6hkWJkXQIxqEPTbLhlaqSF33dMl/3o+7HCCxUqNSavv7dSvu2VqHCBH22GLWs/KWeuq+t1tidx4QqexBWeUnsxAPtUHYY6tY3yoshCrcUlC4mml9oKdMm1HrF1uVGpLi/jxqhrDHVvtwWr/7/m55f2E/PrCtUruk1Hp8df88rW9RS4Q9tpn1rLxlnrrv6Za4lQ9sOVdqmdGizN5sq0PvIOzRI9a3CrvWTeu/y7Q+sDnOQEdtJ7bJaXMIevQawh49Zq2VfKl7bre8Z5jWBzYls29GLdNbpBSr7dG7CHtExnpW/su+uu7uksh8YD2TM2o9vlWpHVmEh3gQ9oiU9a1s0apwS0HBKvrsAqkdUmr9+9bqsbQ0yUFMCHtEztrqav3SEyWVHi2xJx/NyUi5KTnlJubYO4/YEfaoGetZ2bJV1+wuVV4m8dE8nEGOWo9rlbsdve1RHwh71Jz1rCorKuq6t0u2k6cbEiwr5afmlR2flVwW4aF+EPboFTawUiAVHymq/OeyxLMOCZPZO6P8EXkZ1zBtj7pD2KNXWa/abrfrzi4Fb7CAD43PHeqq5ZgWuf2Zskf9IuwRC+tbeS94Ks4pypZ4CqLxmLxRflpemT0y1X3zhqBH/SLsERtbsbIVq+KcIj320TjMuuY4h7dU78uznQ4NgLBH7KxnFawO1HV3Fy13Udfc4a5aj2mV04dWt2gshD3qgg2rC/i85z0VHynKruVpifphWo3yR+SVGcuUPRoTYY+6YitWspK3xFPpkZLCNYz0EaOMlD0gq/xn85LDlD0aF2GPumQDK4WS98K60O8g9NF7TN4oe2BWuQNy1f9myh4NjrBHXVsf+n/1VHqI0EdtmXaj3MG5alMcif3ySAzCHg1hfegvWxf6HxD6iI7T31Fuck6Z3TOSYboeyUPYo6F8GPr+i76KDxUVvk/oY9s5gx3lD80rvXO6ek/eJeSRTIQ9GtL60H/JV/HhosLVhD62nDvMVf6wvFIjUvSwR1Mg7NHQPgz9yusVlR4vcboePlVqZEr5w/JyB7lSmi10aB6EPRLBWit51Ta8pSdK8hZ5sl08tSEpK2X2yCg3MUczHDQtwh6JY30rGclf7qu8oKzKCkb7zSi1Y0rZA7JKj0lLlu1zaG6EPRJr/WjfsyovLMtb7NGkJ+FMm1Fmn4xyB+Sq4Z7mfjwgEfZoEtavPs2DdwOVnyzLW+pJnL2TDBkps2tG2f2ycrd3q6N49scDGyDs0XRs2UpOdSV/+el10/wM+BuLI6V3SSvzmYzSo9NSIJksAQ9sCmGPpmWtlfWsjGNUWVGRt8ST/7IvW+AlUZdS1fvwmT0z1eY3oWRyBDywJQh7YJ3QC2Uco7AzlPeCp8qyiiorKxKvkNi4Q12lRqeU2S0jd4grW7EyGcN9eGArEfbARtjQSr4kR4z6e5FpN0qPTiu9W1rpndLVD7q0rwV6irAHtkBYDmXcj0b9/jJfwcqAUX9PZaT0Tmmlx1R/mbyRDa2crBN3ZUCiEPbAVrKBlSqSHClYFajyekWVNysKVgUK32Ol36cxWSN3sKvUqJTSu6blDmRqHugNhD3QQzas7ueXU/0Vvh+qsrKiyhsVBW8HCt4NpCDuKnuZK7kDXbmDXblDXLkjXLkDXJmskfWtTMowNQ/0IsIeqJHQC6srxtPV6f/gzeosQPB2oMqqilSOu8JoOP2caqgPXhfqg105bY5UkazWjdrpQQ/EirAHepH1rWxgZdJG1rOyXVZhZ6hwTaiwI1RYCGUL6z5WCKv9/eN6hRrJ5I1Mi5HT6lR/b3Fk2oyc/o5S26fk9HOksDq7YdKGI2KBOkXYA3XChtW1ADasviSNayS32u7XdluFhVDh2lDhB6FsZ7VHgA2sFGj97+v/HKr6JsFs+MsYU/2zUw1yp8WRaTXVA2LaPxbqOUfK6KNrWiujaj1KcVoc0GgIe6DBWPuxNwU9ePUS3kDzIOwBAEg4NrMCAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBwhD0AAAlH2AMAkHCEPQAACUfYAwCQcIQ9AAAJR9gDAJBw/x+EJjeGllf2egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas['tratamento'].value_counts().plot.pie(figsize = (20,8),colors = ['#EE82EE', '#B22222'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proporção das classes Dose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D1    0.510078\n",
       "D2    0.489922\n",
       "Name: dose, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas['dose'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.countplot(x ='dose', data = datas, palette = 'coolwarm_r')\n",
    "ax.xaxis.set_label_text('Doses',fontdict= {'size':14}, weight = 'bold')\n",
    "ax.yaxis.set_label_text('Contagem',fontdict= {'size':14}, weight = 'bold')\n",
    "ax.set_title('Quantidade de Doses',fontdict= {'size':20}, weight = 'bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantas tipos de drogas foram investigados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Foram encontrados 3289 tipos de drogas'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_drug = len(datas['droga'].value_counts())\n",
    "print = 'Foram encontrados ' + str(count_drug) + ' tipos de drogas'\n",
    "print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Procurar na documentação o método query(pandas).\n",
    "\n",
    "# Renomear as colunas tirando o hífen."
   ]
  },
  {
   "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>id</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>tempo</th>\n",
       "      <th>dose</th>\n",
       "      <th>droga</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>c90</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",
       "    </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      droga      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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Documentação: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html?highlight=columns#pandas.DataFrame.columns\n",
    "\n",
    "for coluna in datas.columns:\n",
    "      if '-' in coluna:\n",
    "        new = coluna.replace('-','')\n",
    "        datas.rename(columns={coluna:new}, inplace=True)\n",
    "datas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A base de dados que usamos até o momento tem uma variável chamada ```droga```mas entendemos, que essa não é a melhor nomenclatura para representa-la. \n",
    "Por isso, vamos usar a função ```map``` da biblioteca Pandas para renomear essa coluna.\n",
    "É importante destacar que passamos o parâmetro ```inplace = True```, esse parâmetro faz com que os dados sejam modificados no local e o dataframe será atualizado. \n",
    "Caso esse parâmetro não seja declarado, o default é ```inplace = False``` e o retorno será uma cópia do objeto e caso você queira, precisa salva-lo com um outro nome. Agora, iremos chamá-la de ```composto```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapa = {'droga': 'composto'}\n",
    "datas.rename(columns=mapa, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_000779bfc</td>\n",
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       "      <td>72</td>\n",
       "      <td>D1</td>\n",
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       "      <td>0.2991</td>\n",
       "      <td>0.0604</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.7543</td>\n",
       "      <td>0.4708</td>\n",
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       "      <td>0.6077</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_000a6266a</td>\n",
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       "      <td>D1</td>\n",
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       "      <td>-0.0323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7250</td>\n",
       "      <td>-0.6297</td>\n",
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       "    </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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_001626bd3</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D2</td>\n",
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       "      <td>-0.4009</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.7125</td>\n",
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       "  </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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Queremos melhorar a visualização do nosso histograma de compostos e, como existem mais de 3.000 variações na nossa base de dados, decidimos elencar os 5 compostos que mais aparecem. Para isso, vamos usar a função value_counts (função presente na biblioteca Pandas e que conta a ocorrência dos diferentes valores) e, como queremos saber somente os 5 elementos mais frequentes, também declaramos o index[0:5]. Essa parte final, faz com que o value_countsse atenha à contagem dos maiores valores e apresente como resultado apenas o index do intervalo [0, 5[, ou seja, o nome dos 5 maiores valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cacb2b860', '87d714366', '9f80f3f77', '8b87a7a83', '5628cb3ee'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cod_compostos = datas['composto'].value_counts().index[0:5]\n",
    "cod_compostos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exitem algumas maneiras de filtrar uma base de dados e optamos em usar a função query do Pandas e, somente a título de curiosidade, essa função é bastante análoga ao SQL (linguagem de programação para bancos de dados). A estrutura dela é bastante simplificada, precisamos apenas definir o dataframe, chamar a função e passar como parâmetro a condição que deve ser filtrada no nosso conjunto de dados.\n",
    "\n",
    "Nesta parte do projeto, queremos realizar um filtro em nossos dados, selecionando apenas as linhas nas quais o composto esteja dentro da nossa lista cod_composto (lista que representa os 5 compostos mais testados no experimento) e vamos utilizar o método query para resolver este problema.\n",
    "\n",
    "Como parâmetro da função, passamos uma string contendo a lógica para realização da seleção dos dados. O que queremos é o seguinte: o queryprecisa retornar para nós todas as linhas contendo os 5 compostos mais utilizados. Logo, a string necessária para isso é: composto in @cod_compostos.\n",
    "\n",
    "Usamos composto porque essa é a coluna a ser verificada no dataframe e cod_compostos por ser a lista com os top 5 compostos, o detalhe aqui é que o @ é necessário para informar o query que cod_composto é uma variável que já foi definida fora da função."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>id_0054388ec</td>\n",
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       "      <td>D1</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>-0.6696</td>\n",
       "      <td>-0.2718</td>\n",
       "      <td>-1.2230</td>\n",
       "      <td>-0.6226</td>\n",
       "      <td>-0.7220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6171</td>\n",
       "      <td>0.6405</td>\n",
       "      <td>0.5429</td>\n",
       "      <td>0.3562</td>\n",
       "      <td>1.3290</td>\n",
       "      <td>0.5573</td>\n",
       "      <td>0.8837</td>\n",
       "      <td>0.5534</td>\n",
       "      <td>0.8976</td>\n",
       "      <td>1.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>id_0079af0fb</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>-0.1636</td>\n",
       "      <td>-1.8230</td>\n",
       "      <td>-0.5211</td>\n",
       "      <td>0.3054</td>\n",
       "      <td>-1.1280</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3880</td>\n",
       "      <td>-0.6828</td>\n",
       "      <td>-0.6594</td>\n",
       "      <td>-0.2043</td>\n",
       "      <td>0.3571</td>\n",
       "      <td>-0.1319</td>\n",
       "      <td>0.2187</td>\n",
       "      <td>0.0737</td>\n",
       "      <td>0.6498</td>\n",
       "      <td>-1.4820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>id_007bfbb91</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>-1.3200</td>\n",
       "      <td>-1.7340</td>\n",
       "      <td>-0.0741</td>\n",
       "      <td>1.5440</td>\n",
       "      <td>-1.8290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7412</td>\n",
       "      <td>1.0340</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>1.1020</td>\n",
       "      <td>0.3786</td>\n",
       "      <td>0.2636</td>\n",
       "      <td>-0.5751</td>\n",
       "      <td>0.3362</td>\n",
       "      <td>0.8543</td>\n",
       "      <td>0.9180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23793</th>\n",
       "      <td>id_ffd26f361</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>0.6008</td>\n",
       "      <td>0.2781</td>\n",
       "      <td>-0.3319</td>\n",
       "      <td>-0.8782</td>\n",
       "      <td>0.9281</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2470</td>\n",
       "      <td>0.9569</td>\n",
       "      <td>-0.2065</td>\n",
       "      <td>-0.4918</td>\n",
       "      <td>0.7863</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.8813</td>\n",
       "      <td>0.7757</td>\n",
       "      <td>-0.5272</td>\n",
       "      <td>0.7082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23802</th>\n",
       "      <td>id_fff3976bd</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>87d714366</td>\n",
       "      <td>3.2890</td>\n",
       "      <td>2.1270</td>\n",
       "      <td>0.9770</td>\n",
       "      <td>2.0490</td>\n",
       "      <td>2.3170</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.6300</td>\n",
       "      <td>-8.1490</td>\n",
       "      <td>-6.2900</td>\n",
       "      <td>-8.0970</td>\n",
       "      <td>-8.7330</td>\n",
       "      <td>-2.3780</td>\n",
       "      <td>-8.4480</td>\n",
       "      <td>-4.4550</td>\n",
       "      <td>-5.7930</td>\n",
       "      <td>-3.7000</td>\n",
       "    </tr>\n",
       "    <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>0.2865</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",
       "    </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>0.5409</td>\n",
       "      <td>0.3755</td>\n",
       "      <td>0.7343</td>\n",
       "      <td>0.2807</td>\n",
       "      <td>0.4116</td>\n",
       "      <td>0.6422</td>\n",
       "      <td>0.2256</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.3808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23812</th>\n",
       "      <td>id_fffcb9e7c</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D1</td>\n",
       "      <td>8b87a7a83</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>0.2324</td>\n",
       "      <td>0.4392</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.8531</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1105</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>-0.2012</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>1.5230</td>\n",
       "      <td>0.7101</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.7015</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3235 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose   composto      g0      g1  \\\n",
       "7      id_0020d0484     com_droga     48   D1  8b87a7a83  0.2711  0.5133   \n",
       "16     id_002fb9c19     com_droga     48   D1  87d714366  8.7380  0.1914   \n",
       "25     id_0054388ec  com_controle     48   D1  cacb2b860 -0.6696 -0.2718   \n",
       "38     id_0079af0fb  com_controle     24   D1  cacb2b860 -0.1636 -1.8230   \n",
       "40     id_007bfbb91  com_controle     24   D2  cacb2b860 -1.3200 -1.7340   \n",
       "...             ...           ...    ...  ...        ...     ...     ...   \n",
       "23793  id_ffd26f361  com_controle     48   D2  cacb2b860  0.6008  0.2781   \n",
       "23802  id_fff3976bd     com_droga     24   D1  87d714366  3.2890  2.1270   \n",
       "23805  id_fff6df1c5     com_droga     48   D2  5628cb3ee  1.7380 -1.2900   \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",
       "\n",
       "           g2      g3      g4  ...     c90     c91     c92     c93     c94  \\\n",
       "7     -0.1327  2.5950  0.6980  ...  0.3230 -0.4140  0.2532  0.0513  0.8600   \n",
       "16     2.4380 -0.2930  3.2010  ... -9.9840 -9.9840 -9.9840 -9.9840 -9.9840   \n",
       "25    -1.2230 -0.6226 -0.7220  ...  0.6171  0.6405  0.5429  0.3562  1.3290   \n",
       "38    -0.5211  0.3054 -1.1280  ... -0.3880 -0.6828 -0.6594 -0.2043  0.3571   \n",
       "40    -0.0741  1.5440 -1.8290  ...  0.7412  1.0340  0.7393  1.1020  0.3786   \n",
       "...       ...     ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "23793 -0.3319 -0.8782  0.9281  ...  1.2470  0.9569 -0.2065 -0.4918  0.7863   \n",
       "23802  0.9770  2.0490  2.3170  ... -8.6300 -8.1490 -6.2900 -8.0970 -8.7330   \n",
       "23805 -0.4533 -1.1640 -0.4841  ...  0.2865  0.5974 -0.2727  0.1188  1.0770   \n",
       "23811  0.3109 -0.7389  0.5505  ...  0.5409  0.3755  0.7343  0.2807  0.4116   \n",
       "23812  0.4392  0.2044  0.8531  ... -0.1105  0.4258 -0.2012  0.1506  1.5230   \n",
       "\n",
       "          c95     c96     c97     c98     c99  \n",
       "7      1.4250  0.6633  0.4562 -0.9622  0.0260  \n",
       "16    -9.9840 -9.9840 -9.9840 -9.9840 -6.7840  \n",
       "25     0.5573  0.8837  0.5534  0.8976  1.0050  \n",
       "38    -0.1319  0.2187  0.0737  0.6498 -1.4820  \n",
       "40     0.2636 -0.5751  0.3362  0.8543  0.9180  \n",
       "...       ...     ...     ...     ...     ...  \n",
       "23793  0.0504  0.8813  0.7757 -0.5272  0.7082  \n",
       "23802 -2.3780 -8.4480 -4.4550 -5.7930 -3.7000  \n",
       "23805 -1.0250  0.1516  0.4503 -0.6312 -0.8302  \n",
       "23811  0.6422  0.2256  0.7592  0.6656  0.3808  \n",
       "23812  0.7101  0.1732  0.7015 -0.6290  0.0740  \n",
       "\n",
       "[3235 rows x 877 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.query('composto in @cod_compostos')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizando os Top 5 Compostos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig,ax = plt.subplots(figsize = (8, 6))\n",
    "sns.countplot(x ='composto', data = datas.query('composto in @cod_compostos'), order=cod_compostos,  palette = 'magma')\n",
    "ax.xaxis.set_label_text('Compostos',fontdict= {'size':14}, weight='bold')\n",
    "ax.yaxis.set_label_text('Quantidade',fontdict= {'size':14}, weight='bold')\n",
    "ax.set_title('Top 5 Compostos',fontdict= {'size':25}, weight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui podemos observart que a droga mais utlizada é a ```cacb2b860``` e a menos utilizada é a ```5628cb3ee```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Até o momento analisamos os dados de tempo, dose, compostos e afins. Entretanto, não analisamos os dados de expressões gênicas (G's) e viabilidade celular (C's). Será que podemos criar um gráfico de barras para esses dados?\n",
    "Vamos pensar que a nossa base de dados apresenta mais de 3.000 compostos. Mas quantos desses compostos aparecem na coluna ```g0```? \n",
    "Para responder essa questão, vamos usar a função ```unique()``` do Pandas que conta os valores únicos presentes na coluna em questão. Como resposta padrão, o retorno será uma lista com arrays (os nomes dos compostos) mas, nosso objetivo é saber o tamanho dessa lista e, por isso, usamos o ```len```, pois assim, ele contará o tamanho desta lista de arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14367"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datas['g0'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.513"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculando o valor mínimo\n",
    "datas['g0'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculando o valor máximo\n",
    "datas['g0'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depois que reconhecemos que o nosso intervalo vai de ~5,5 a 10,0, podemos partir para o histograma e a função que usaremos para plotar é do Pandas (```dataframe['variável'].hist()```).\n",
    "Assim que rodamos essa função, percebemos que a visualização destes dados ainda não está boa pois, a divisão padrão das barras do histograma, representam intervalos muito grandes que atrapalham o entendimento dos dados.\n",
    "Por isso, acresentamos um parâmetro dentro da função ```(bins = número de quebras)``` para melhor dividir e, consequentemente visualizar os dados.\n",
    "Quando definimos os bins em 100, podemos perceber que a forma se aproxima bastante de uma curva bastante conhecida: a curva normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a75d92d148>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sn2XLljWbvnjxIlar1T9ts9morq5uMW61Wqmurubbb78lKiqKsLCwZuMiIhI8HT7V0+fzYbFY/NOGYWCxWNocv/VvU7dPt8eQIVGtjt86zzsUKVvP6ql1CuXnTtkCF8r5uiNbh8s/JiYGl8vln3a5XNhsthbjly5dwmazMXjwYK5du0ZjYyP9+/f3z99RNTVufD6j2ZjVGo3LFZpnd5s1WzBfQD3xfJv159pZoZwNQjtfoNn69bO0udEMAZzqmZCQwKlTpzh9+jSNjY3s3bsXu91ObGwskZGRlJeXA1BSUoLdbic8PJyRI0eyb98+AHbv3o3dbu/wioiISNfp8JZ/ZGQkhYWFzJkzB4/HQ1JSEikpKQCsWrWK/Px83G43w4cPZ+rUqQAUFBSwcOFCNm7cyNChQ1m9enXXroWIiHRIu8v/4MGD/seJiYns2bOnxTzx8fEUFxe3GI+NjWXLli0BRhQRka6mT/iKiJiQyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFRExId/KSTtNN20V6H235S6fppu0ivY/KX0TEhPS3uvQJ9Q2N/ovK1Xm8XLt6I8iJREKbyl/6hIjw/v7dTh/8JY3QvD6jSOjQbh8RERNS+YuImJDKX0TEhFT+IiImpPIXETGhgM/22blzJ1u3bvVPnzt3jrS0NG7cuEF5eTkDBw4EYPbs2YwdO5aysjKWL1+Ox+Nh3Lhx5Obmdj69iIgEJODyf/7553n++ecBOHnyJK+88gqzZ89m2rRpbN26tdlN2uvq6sjLy2PLli0MHTqU7OxsDh06RFJSUufXQEREOqxLdvv86U9/Ijc3l4EDB1JVVUVeXh5Op5O1a9fi8/moqKhg2LBhxMXFERYWhtPppLS0tCsWLSIiAej0h7zKysqoq6tj3LhxnD17ltGjR1NQUEB0dDTZ2dkUFxczaNAgrFar/3tsNhvV1dWdXbSIiASo0+X/7rvv8uKLLwIQFxfH+vXr/V+bMmUKu3fvxuFwYLFY/OOGYTSbbo8hQ6JaHb/1kf5QpGzB053rF8rPnbIFLpTzdUe2TpV/fX09X375JYWFhQCcOHGCyspKHA4HcLPkw8LCiImJweVy+b/P5XI1OybQHjU1bnw+o9mY1RqNyxWaH+Q3U7ZQfNF013Nvpp9rVwrlbBDa+QLN1q+fpc2NZujkPv8TJ05w7733MmjQIOBm2b/xxhtcuXKFhoYGduzYwdixY0lISODUqVOcPn2axsZG9u7di91u78yiRUSkEzq15X/27FliYmL80/Hx8cyaNYvMzEy8Xi/JycmkpqYCUFhYyJw5c/B4PCQlJZGSktK55CIiErBOlf/48eMZP358s7GsrCyysrJazJuYmMiePXs6szgREeki+oSviIgJqfxFRExI5S8iYkIqfxERE9JtHKXP0f18Re5M5S99ju7nK3Jn2u0jImJCKn8RERNS+YuImJDKX0TEhFT+IiImpPIXETEhneopAYm+eyADIvXrI9JbactfAjIgMgznvBL/+fQi0ruo/EVETEjlLyJiQp3aaTtlyhQuX75MWNjN/2bp0qWcOXOGjRs34vV6mTZtmv/GLmVlZSxfvhyPx8O4cePIzc3tfHoREQlIwOVvGAaVlZV88skn/vKvrq4mNzeX999/n4iICDIyMhg1ahQ/+clPyMvLY8uWLQwdOpTs7GwOHTpEUlJSl62IiIi0X8Dl/5///AeAGTNm8N133/HCCy9w1113MXr0aO655x4AHA4HpaWl/OIXv2DYsGHExcUB4HQ6KS0tVfmLiARJwPv8r169SmJiIuvXr+edd97h3XffpaqqCqvV6p/HZrNRXV3NxYsXWx0XEZHgCHjL/7HHHuOxxx7zT0+aNInly5eTk5PjHzMMA4vFgs/nw2KxtBjviCFDolodv3Xd9lCkbMHX9Nr+9Q2NRIT37/T/GcrPnbIFLpTzdUe2gMv/8OHDNDQ0kJiYCNws9NjYWFwul38el8uFzWYjJiam1fGOqKlx4/MZzcas1mhcrtC8WntfzxbKL5Smbr+2f1esd1/+uXaXUM4GoZ0v0Gz9+lna3GiGTuz2uXbtGitWrMDj8eB2u9m1axcrV67k888/5/Lly9y4cYMPP/wQu91OQkICp06d4vTp0zQ2NrJ3717sdnugixYRkU4KeMt/zJgxHDlyhGeffRafz8fkyZP5+c9/Tm5uLlOnTqWhoYFJkybx6KOPAlBYWMicOXPweDwkJSWRkpLSZSshIiId06nz/F999VVeffXVZmNOpxOn09li3sTERPbs2dOZxYmISBfRJ3xFRExI5S8iYkIqfxERE9IF2aXddA1/kb5DW/7SbrqGv0jfofIXETEhlb+IiAmp/EVETEjlLyJiQip/ERET0nl7YhpNL+9c5/Fy7eqNICcSCR6Vv5jG7Zd3Ds0L+Ir0DO32ERExIZW/iIgJqfxFRExI5S8iYkKdOuC7bt069u/fD0BSUhILFixg0aJFlJeXM3DgQABmz57N2LFjKSsrY/ny5Xg8HsaNG0dubm7n04uISEACLv+ysjI+/fRTdu3ahcViYebMmXz00UccPXqUrVu3NrtBe11dHXl5eWzZsoWhQ4eSnZ3NoUOHSEpK6pKVEOkonfYpZhfwbh+r1crChQuJiIggPDyc+++/n6qqKqqqqsjLy8PpdLJ27Vp8Ph8VFRUMGzaMuLg4wsLCcDqdlJaWduV6SDeJvnsgVmu0vyj7ilunfTrnlegy1WJKAf/WP/jgg/7HlZWV7N+/n23btvHFF19QUFBAdHQ02dnZFBcXM2jQIKxWq39+m81GdXV1h5Y3ZEhUq+OhXEp9JVvTc+P7qo48H33l59rTQjkbhHa+7sjW6U2ekydPkp2dzYIFC7jvvvtYv369/2tTpkxh9+7dOBwOLBaLf9wwjGbT7VFT48bnM5qNWa3RuFyh+VGdvpItlF8QXakjz0df+Ln2tFDOBqGdL9Bs/fpZ2txohk6e7VNeXs706dOZN28eEydO5MSJExw4cMD/dcMwCAsLIyYmBpfL5R93uVzNjgmIiEjPCrj8L1y4wCuvvMKqVauYMGECcLPs33jjDa5cuUJDQwM7duxg7NixJCQkcOrUKU6fPk1jYyN79+7Fbrd32UqIiEjHBLzbZ/PmzXg8HgoLC/1jGRkZzJo1i8zMTLxeL8nJyaSmpgJQWFjInDlz8Hg8JCUlkZKS0vn0IiISkIDLPz8/n/z8/Fa/lpWV1WIsMTGRPXv2BLo4ERHpQjrHTUxP5/yLGan8xfR0qWcxI13bR0TEhLTlL9KEdgGJWaj8RZrQLiAxC5W/tBB990Bd70akj9M+f2lhQGSY/6JnItI3qfxFRExIf9uLtKHpwV8AT30jcPNCWzoYLL2dyl+kDU0P/sLNA8C3pt8rTNVZQdKrqfxFB3gDoLOCpLfTK178B3hv6cs3bRGRm1T+Ip3U0Q+GNf1LS7uMJFhU/iKd1HQXUNNjAZ76RiIj+rd4DOjYgQSdyt+ktJ+/e9x+LKCtx63N39YbQUf/Umhr/o6OS9+mV7+JNH2RN93Pr338oaGtNwK4818K0XcPBP53z+WO/D9Nx9s6eK03iL6nR8v/gw8+YOPGjXi9XqZNm9bqTV+k+6jwe4/b/4Jobfz7Crwj/09bvwu3/3XY1nLb2r2lN4nQ1mPlX11dzZo1a3j//feJiIggIyODUaNG8cADD/RUBJE+pT0F3lG3f7DtTm8ct77W2u6tpm8S9Q2N/vmbvqm0542jrfn15tI5PVb+ZWVljB49mnvuuQcAh8NBaWkps2fPbtf39+tn6dB4KOjJbFFRA4i8wwsKwPaDgXd83N75zPY4VHJ05+OI8P689PqHAGzOT+7U89L0/9r4h183e1NpuozWHrdn/qbztPU7397HcHOXWbNxjxe3uw5o3+ur6fyd1XR59Q2NAXXJnb7HYhiGEVC6Dtq0aRPXr18nNzcXgJ07d1JRUcGf//znnli8iIg00WMXdvP5fFgs/3snMgyj2bSIiPScHiv/mJgYXC6Xf9rlcmGz2Xpq8SIi0kSPlf8vf/lLPv/8cy5fvsyNGzf48MMPsdvtPbV4ERFposcO+P7oRz8iNzeXqVOn0tDQwKRJk3j00Ud7avEiItJEjx3wFRGR0KE7eYmImJDKX0TEhFT+IiImpPIXETGhXl3+Fy9eZNasWTz77LNkZGRw7ty5YEdq4dixYzzyyCPBjtFCeXk5kyZNIi0tjWnTpnH+/PlgR+KDDz5g/PjxJCcns23btmDHaWbdunVMmDCBCRMmsGLFimDHadWbb77JwoULgx2jhYMHD5Kens64ceN4/fXXgx2nmZKSEv/P9c033wx2HADcbjepqan+PisrK8PpdJKcnMyaNWu6bkFGLzZt2jRj+/bthmEYxvbt2425c+cGOVFz169fNzIyMoyHHnoo2FFaGDNmjHH8+HHDMAxj586dxssvvxzUPP/973+NMWPGGN9++61RW1trOJ1O4+TJk0HNdMtnn31m/OY3vzE8Ho9RX19vTJ061fjwww+DHauZsrIyY9SoUcYf/vCHYEdp5syZM8ZTTz1lXLhwwaivrzcyMzONf/7zn8GOZRjGzdfnE088YdTU1BgNDQ3GpEmTjM8++yyomb766isjNTXVGD58uHH27Fnjxo0bRlJSknHmzBmjoaHBmDFjRpc9f712y//y5ct8/fXXZGRkAPDcc8/x6quvBjlVc4WFhUybNi3YMVqor69n7ty5xMfHA/DTn/6UCxcuBDVT0wv/DRo0yH/hv1BgtVpZuHAhERERhIeHc//991NVVRXsWH7fffcda9as4eWXXw52lBY++ugjxo8fT0xMDOHh4axZs4aEhIRgxwKgsbERn8/HjRs38Hq9eL1eIiMjg5qpqKiIgoIC/9UPKioqGDZsGHFxcYSFheF0OrvsddFrb+Zy9uxZfvzjH1NYWMjhw4exWq388Y9/DHYsv48//pi6ujpSUlKCHaWFiIgI0tJuXqLX5/Oxbt06nn766aBmunjxIlar1T9ts9moqKgIYqL/efDBB/2PKysr2b9/P3//+9+DmKi5JUuWkJubG/Q38NacPn2a8PBwXn75ZS5cuMCvfvWrkNlIi4qKYu7cuYwbN46BAwfyxBNP8Pjjjwc107Jly5pNt/a6qK6u7pJl9Yry379/P8uXL282NmzYMI4dO8acOXNYtGgRO3fuZOHChWzZsiXo2e677z7cbjfvvPNOj2ZpTVv53nnnHerr61m4cCFer5fs7OwgJbypN1z47+TJk2RnZ7NgwQLuvffeYMcBbl4dd+jQoSQmJvL+++8HO04LjY2NHD58mC1btjBo0CBycnLYtWsX6enpwY7G119/zXvvvccnn3xCdHQ0v//979m8eTMzZ84MdjS/7nxd9IryHzduHOPGjWs2dubMGSZOnMiYMWMASE1NDcrBpNay7dy5k02bNjW7U1laWhrbtm0jKioq6PkAamtrycnJ4Z577mHjxo2Eh4f3aK7bxcTEcPjwYf90qF34r7y8nN/97nfk5eUxYcKEYMfx27dvHy6Xi7S0NK5cucL169d54403yMvLC3Y0AH74wx+SmJjI4MGDAXj66aepqKgIifL/9NNPSUxMZMiQIQCkp6ezffv2kCr/br0gZpccOQiS8ePH+w9+/OMf/zAyMzODnKh1oXjANycnx8jPzzcaGxuDHcUwjP8d8K2pqTGuX79uPPPMM8aRI0eCHcswDMOoqqoyRo0aZZSVlQU7yvd67733Qu6A71dffWU4HA7jypUrhtfrNbKzs42ioqJgxzIMwzD+9a9/Gc8884xRW1tr+Hw+449//KOxdu3aYMcyDOPmCRlnz5416urqDLvdblRWVhper9d46aWXjH379nXJMnrFln9b/vrXv1JQUMDKlSuJioqisLAw2JF6hWPHjvHxxx/zwAMPMHHiRODmvsS33noraJlC+cJ/mzdvxuPxNPv9ysjIIDMzM4ipeoeEhARmzpzJ5MmTaWho4Mknn+S5554LdiwAnnrqKY4dO0Z6ejrh4eH87Gc/Y9asWcGO1UxkZCSFhYXMmTMHj8dDUlJSlx1H1IXdRERMqNee6ikiIoFT+YuImJDKX0TEhFT+IiImpPIXETD5104AAAAYSURBVDEhlb+IiAmp/EVETEjlLyJiQv8Pj03K8SYCoacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas['g0'].hist(bins = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui, estamos testando o mesmo histograma para outro gene, o ```g19```. E queremos fazer uma comparação entre os gráficos e podemos fazer algumas considerações sobre ambos os gráficos.\n",
    "Percebemos, por exemplo, que a imagem seguem a mesma tendência de curva mas há um deslocamento para a direita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701810c08>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas['g19'].hist(bins = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotar figuras lado a lado para comparação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ax1 = datas['g0'].hist(bins = 100)\n",
    "#ax2 = datas['g19'].hist(bins = 100)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,6))\n",
    "fig.suptitle('Expressões Gênicas')\n",
    "ax1.hist(datas['g0'], bins = 100)\n",
    "ax1.set_title('G-0')\n",
    "ax2.hist(datas['g19'], bins = 100)\n",
    "ax2.set_title('G-19')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como concluimos que plotar os gráficos de todos os genes é inviável, vamos analisar resumidamente algumas estatísticas sobre eles. Para isso, vamos usar a função describe do Pandas que já calcula e descreve algumas estatísticas importantes para o entendimento dos dados (contagem, média, desvio padrão, mínimo, alguns quartis e máximo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tempo</th>\n",
       "      <th>g0</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "      <th>g4</th>\n",
       "      <th>g5</th>\n",
       "      <th>g6</th>\n",
       "      <th>g7</th>\n",
       "      <th>g8</th>\n",
       "      <th>...</th>\n",
       "      <th>c90</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.020156</td>\n",
       "      <td>0.248366</td>\n",
       "      <td>-0.095684</td>\n",
       "      <td>0.152253</td>\n",
       "      <td>0.081971</td>\n",
       "      <td>0.057347</td>\n",
       "      <td>-0.138836</td>\n",
       "      <td>0.035961</td>\n",
       "      <td>-0.202651</td>\n",
       "      <td>-0.190083</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.469244</td>\n",
       "      <td>-0.461411</td>\n",
       "      <td>-0.513256</td>\n",
       "      <td>-0.500142</td>\n",
       "      <td>-0.507093</td>\n",
       "      <td>-0.353726</td>\n",
       "      <td>-0.463485</td>\n",
       "      <td>-0.378241</td>\n",
       "      <td>-0.470252</td>\n",
       "      <td>-0.301505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.402807</td>\n",
       "      <td>1.393399</td>\n",
       "      <td>0.812363</td>\n",
       "      <td>1.035731</td>\n",
       "      <td>0.950012</td>\n",
       "      <td>1.032091</td>\n",
       "      <td>1.179388</td>\n",
       "      <td>0.882395</td>\n",
       "      <td>1.125494</td>\n",
       "      <td>1.749885</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000488</td>\n",
       "      <td>2.042475</td>\n",
       "      <td>2.001714</td>\n",
       "      <td>2.107105</td>\n",
       "      <td>2.159589</td>\n",
       "      <td>1.629291</td>\n",
       "      <td>2.059725</td>\n",
       "      <td>1.703615</td>\n",
       "      <td>1.834828</td>\n",
       "      <td>1.407918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>-5.513000</td>\n",
       "      <td>-5.737000</td>\n",
       "      <td>-9.104000</td>\n",
       "      <td>-5.998000</td>\n",
       "      <td>-6.369000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>-0.473075</td>\n",
       "      <td>-0.562200</td>\n",
       "      <td>-0.437750</td>\n",
       "      <td>-0.429575</td>\n",
       "      <td>-0.470925</td>\n",
       "      <td>-0.602225</td>\n",
       "      <td>-0.493900</td>\n",
       "      <td>-0.525175</td>\n",
       "      <td>-0.511675</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.566175</td>\n",
       "      <td>-0.565975</td>\n",
       "      <td>-0.589975</td>\n",
       "      <td>-0.568700</td>\n",
       "      <td>-0.563775</td>\n",
       "      <td>-0.567975</td>\n",
       "      <td>-0.552575</td>\n",
       "      <td>-0.561000</td>\n",
       "      <td>-0.592600</td>\n",
       "      <td>-0.562900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>-0.008850</td>\n",
       "      <td>-0.046600</td>\n",
       "      <td>0.075200</td>\n",
       "      <td>0.008050</td>\n",
       "      <td>-0.026900</td>\n",
       "      <td>-0.015650</td>\n",
       "      <td>-0.000650</td>\n",
       "      <td>-0.017900</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009900</td>\n",
       "      <td>0.003250</td>\n",
       "      <td>-0.009100</td>\n",
       "      <td>-0.013750</td>\n",
       "      <td>-0.003300</td>\n",
       "      <td>-0.010250</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>-0.006800</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>-0.019500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.525700</td>\n",
       "      <td>0.403075</td>\n",
       "      <td>0.663925</td>\n",
       "      <td>0.463400</td>\n",
       "      <td>0.465375</td>\n",
       "      <td>0.510425</td>\n",
       "      <td>0.528725</td>\n",
       "      <td>0.411900</td>\n",
       "      <td>0.549225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.457750</td>\n",
       "      <td>0.461500</td>\n",
       "      <td>0.445675</td>\n",
       "      <td>0.452900</td>\n",
       "      <td>0.470900</td>\n",
       "      <td>0.444750</td>\n",
       "      <td>0.465225</td>\n",
       "      <td>0.446400</td>\n",
       "      <td>0.461275</td>\n",
       "      <td>0.438650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.039000</td>\n",
       "      <td>8.257000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.282000</td>\n",
       "      <td>7.333000</td>\n",
       "      <td>5.473000</td>\n",
       "      <td>8.887000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.069000</td>\n",
       "      <td>3.960000</td>\n",
       "      <td>3.927000</td>\n",
       "      <td>3.596000</td>\n",
       "      <td>3.747000</td>\n",
       "      <td>2.814000</td>\n",
       "      <td>3.505000</td>\n",
       "      <td>2.924000</td>\n",
       "      <td>3.111000</td>\n",
       "      <td>3.805000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 873 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              tempo            g0            g1            g2            g3  \\\n",
       "count  23814.000000  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean      48.020156      0.248366     -0.095684      0.152253      0.081971   \n",
       "std       19.402807      1.393399      0.812363      1.035731      0.950012   \n",
       "min       24.000000     -5.513000     -5.737000     -9.104000     -5.998000   \n",
       "25%       24.000000     -0.473075     -0.562200     -0.437750     -0.429575   \n",
       "50%       48.000000     -0.008850     -0.046600      0.075200      0.008050   \n",
       "75%       72.000000      0.525700      0.403075      0.663925      0.463400   \n",
       "max       72.000000     10.000000      5.039000      8.257000     10.000000   \n",
       "\n",
       "                 g4            g5            g6            g7            g8  \\\n",
       "count  23814.000000  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean       0.057347     -0.138836      0.035961     -0.202651     -0.190083   \n",
       "std        1.032091      1.179388      0.882395      1.125494      1.749885   \n",
       "min       -6.369000    -10.000000    -10.000000    -10.000000    -10.000000   \n",
       "25%       -0.470925     -0.602225     -0.493900     -0.525175     -0.511675   \n",
       "50%       -0.026900     -0.015650     -0.000650     -0.017900      0.010000   \n",
       "75%        0.465375      0.510425      0.528725      0.411900      0.549225   \n",
       "max       10.000000      7.282000      7.333000      5.473000      8.887000   \n",
       "\n",
       "       ...           c90           c91           c92           c93  \\\n",
       "count  ...  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean   ...     -0.469244     -0.461411     -0.513256     -0.500142   \n",
       "std    ...      2.000488      2.042475      2.001714      2.107105   \n",
       "min    ...    -10.000000    -10.000000    -10.000000    -10.000000   \n",
       "25%    ...     -0.566175     -0.565975     -0.589975     -0.568700   \n",
       "50%    ...     -0.009900      0.003250     -0.009100     -0.013750   \n",
       "75%    ...      0.457750      0.461500      0.445675      0.452900   \n",
       "max    ...      4.069000      3.960000      3.927000      3.596000   \n",
       "\n",
       "                c94           c95           c96           c97           c98  \\\n",
       "count  23814.000000  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean      -0.507093     -0.353726     -0.463485     -0.378241     -0.470252   \n",
       "std        2.159589      1.629291      2.059725      1.703615      1.834828   \n",
       "min      -10.000000    -10.000000    -10.000000    -10.000000    -10.000000   \n",
       "25%       -0.563775     -0.567975     -0.552575     -0.561000     -0.592600   \n",
       "50%       -0.003300     -0.010250     -0.001250     -0.006800      0.014000   \n",
       "75%        0.470900      0.444750      0.465225      0.446400      0.461275   \n",
       "max        3.747000      2.814000      3.505000      2.924000      3.111000   \n",
       "\n",
       "                c99  \n",
       "count  23814.000000  \n",
       "mean      -0.301505  \n",
       "std        1.407918  \n",
       "min      -10.000000  \n",
       "25%       -0.562900  \n",
       "50%       -0.019500  \n",
       "75%        0.438650  \n",
       "max        3.805000  \n",
       "\n",
       "[8 rows x 873 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resumo estatístico: \n",
    "\n",
    "A função .describe() retorna o resumo estatístico dos dados, que entendemos como\n",
    "\n",
    "- Média:\n",
    "\n",
    "    A média aritmética, conceito familiar, é a soma das observações dividida pelo número delas. Assim, a média aritmética de ```3, 4, 7, 8 e 8``` é ```(3 + 4 + 7 + 8 + 8)/5 = 6```\n",
    "\n",
    "\n",
    "- Desvio Padrão:\n",
    "\n",
    "    O desvio padrão é uma medida que expressa o grau de dispersão de um conjunto de dados. Ou seja, o desvio padrão indica o quanto um conjunto de dados é uniforme. Quanto mais próximo de 0 for o desvio padrão, mais homogêneo são os dados.\n",
    "\n",
    "\n",
    "- Mínimo:\n",
    "\n",
    "     O mínimo é o menor valor do conjunto de dados analisado. \n",
    "\n",
    "\n",
    "- Maxímo:\n",
    "\n",
    "    O máximo é o maior valor do conjunto de dados analisado.\n",
    "    \n",
    "    \n",
    "- Quartis (Q1, Q2 e Q3):\n",
    "    \n",
    "    São valores dados a partir do conjunto de observações ordenado em ordem crescente, que dividem a distribuição em quatro partes iguais.\n",
    "    \n",
    "    \n",
    "        Q1: O primeiro quartil, é o número que deixa 25% das observações abaixo e 75% acima do range\n",
    "        \n",
    "        Q2: O segundo quartil, é a mediana deixa 50% das observações abaixo e 50% das observações acima\n",
    "        \n",
    "        Q2: O terceiro quartil, é o número que deixa 75% das observações abaixo e 25% acima do range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma estratégia mais direta em que não precisaremos escrever a lista gene a gene, é o uso da função ```loc[]``` do Pandas.\n",
    "Como argumentos, passamos primeiramente o ```:```, os dois pontos faz com que o ```loc[]```retorne todos os elementos de uma determinada coluna, isso é importante quando não sabemos qual a quantidade de linhas de um dataframe. E, o segundo elemento, passamos as colunas que são de nosso interesse. No caso, queremos que a função nos retorne todos os elementos das colunas ```g0```até ```g771```.\n",
    "E, por fim, podemos declarar a nossa função de interesse a partir deste filtro realizado nos dados, o ```describe()```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g0</th>\n",
       "      <th>g1</th>\n",
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       "      <th>g769</th>\n",
       "      <th>g770</th>\n",
       "      <th>g771</th>\n",
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       "      <th>count</th>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "      <td>23814.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.248366</td>\n",
       "      <td>-0.095684</td>\n",
       "      <td>0.152253</td>\n",
       "      <td>0.081971</td>\n",
       "      <td>0.057347</td>\n",
       "      <td>-0.138836</td>\n",
       "      <td>0.035961</td>\n",
       "      <td>-0.202651</td>\n",
       "      <td>-0.190083</td>\n",
       "      <td>0.119905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.207299</td>\n",
       "      <td>0.009161</td>\n",
       "      <td>0.171027</td>\n",
       "      <td>0.002316</td>\n",
       "      <td>-0.209799</td>\n",
       "      <td>-0.076251</td>\n",
       "      <td>0.134162</td>\n",
       "      <td>-0.128018</td>\n",
       "      <td>-0.219210</td>\n",
       "      <td>0.101524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.393399</td>\n",
       "      <td>0.812363</td>\n",
       "      <td>1.035731</td>\n",
       "      <td>0.950012</td>\n",
       "      <td>1.032091</td>\n",
       "      <td>1.179388</td>\n",
       "      <td>0.882395</td>\n",
       "      <td>1.125494</td>\n",
       "      <td>1.749885</td>\n",
       "      <td>1.087180</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096488</td>\n",
       "      <td>1.200395</td>\n",
       "      <td>1.357701</td>\n",
       "      <td>1.052771</td>\n",
       "      <td>1.201918</td>\n",
       "      <td>1.115477</td>\n",
       "      <td>0.951264</td>\n",
       "      <td>1.230636</td>\n",
       "      <td>1.326193</td>\n",
       "      <td>1.417674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.513000</td>\n",
       "      <td>-5.737000</td>\n",
       "      <td>-9.104000</td>\n",
       "      <td>-5.998000</td>\n",
       "      <td>-6.369000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-8.337000</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.193000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-9.795000</td>\n",
       "      <td>-8.180000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-4.269000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.473075</td>\n",
       "      <td>-0.562200</td>\n",
       "      <td>-0.437750</td>\n",
       "      <td>-0.429575</td>\n",
       "      <td>-0.470925</td>\n",
       "      <td>-0.602225</td>\n",
       "      <td>-0.493900</td>\n",
       "      <td>-0.525175</td>\n",
       "      <td>-0.511675</td>\n",
       "      <td>-0.360200</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.433875</td>\n",
       "      <td>-0.502250</td>\n",
       "      <td>-0.537075</td>\n",
       "      <td>-0.508775</td>\n",
       "      <td>-0.606175</td>\n",
       "      <td>-0.506200</td>\n",
       "      <td>-0.353100</td>\n",
       "      <td>-0.544600</td>\n",
       "      <td>-0.554400</td>\n",
       "      <td>-0.523800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.008850</td>\n",
       "      <td>-0.046600</td>\n",
       "      <td>0.075200</td>\n",
       "      <td>0.008050</td>\n",
       "      <td>-0.026900</td>\n",
       "      <td>-0.015650</td>\n",
       "      <td>-0.000650</td>\n",
       "      <td>-0.017900</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.160450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059600</td>\n",
       "      <td>-0.005900</td>\n",
       "      <td>-0.013600</td>\n",
       "      <td>-0.027700</td>\n",
       "      <td>-0.011850</td>\n",
       "      <td>0.009900</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.028700</td>\n",
       "      <td>-0.006500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.525700</td>\n",
       "      <td>0.403075</td>\n",
       "      <td>0.663925</td>\n",
       "      <td>0.463400</td>\n",
       "      <td>0.465375</td>\n",
       "      <td>0.510425</td>\n",
       "      <td>0.528725</td>\n",
       "      <td>0.411900</td>\n",
       "      <td>0.549225</td>\n",
       "      <td>0.697775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.648500</td>\n",
       "      <td>0.492150</td>\n",
       "      <td>0.522500</td>\n",
       "      <td>0.457000</td>\n",
       "      <td>0.483000</td>\n",
       "      <td>0.511175</td>\n",
       "      <td>0.409075</td>\n",
       "      <td>0.498500</td>\n",
       "      <td>0.496400</td>\n",
       "      <td>0.536950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.039000</td>\n",
       "      <td>8.257000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.282000</td>\n",
       "      <td>7.333000</td>\n",
       "      <td>5.473000</td>\n",
       "      <td>8.887000</td>\n",
       "      <td>6.433000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.552000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.346000</td>\n",
       "      <td>5.444000</td>\n",
       "      <td>6.317000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.911000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 772 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 g0            g1            g2            g3            g4  \\\n",
       "count  23814.000000  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean       0.248366     -0.095684      0.152253      0.081971      0.057347   \n",
       "std        1.393399      0.812363      1.035731      0.950012      1.032091   \n",
       "min       -5.513000     -5.737000     -9.104000     -5.998000     -6.369000   \n",
       "25%       -0.473075     -0.562200     -0.437750     -0.429575     -0.470925   \n",
       "50%       -0.008850     -0.046600      0.075200      0.008050     -0.026900   \n",
       "75%        0.525700      0.403075      0.663925      0.463400      0.465375   \n",
       "max       10.000000      5.039000      8.257000     10.000000     10.000000   \n",
       "\n",
       "                 g5            g6            g7            g8            g9  \\\n",
       "count  23814.000000  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean      -0.138836      0.035961     -0.202651     -0.190083      0.119905   \n",
       "std        1.179388      0.882395      1.125494      1.749885      1.087180   \n",
       "min      -10.000000    -10.000000    -10.000000    -10.000000     -8.337000   \n",
       "25%       -0.602225     -0.493900     -0.525175     -0.511675     -0.360200   \n",
       "50%       -0.015650     -0.000650     -0.017900      0.010000      0.160450   \n",
       "75%        0.510425      0.528725      0.411900      0.549225      0.697775   \n",
       "max        7.282000      7.333000      5.473000      8.887000      6.433000   \n",
       "\n",
       "       ...          g762          g763          g764          g765  \\\n",
       "count  ...  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean   ...      0.207299      0.009161      0.171027      0.002316   \n",
       "std    ...      1.096488      1.200395      1.357701      1.052771   \n",
       "min    ...     -8.193000    -10.000000     -9.795000     -8.180000   \n",
       "25%    ...     -0.433875     -0.502250     -0.537075     -0.508775   \n",
       "50%    ...      0.059600     -0.005900     -0.013600     -0.027700   \n",
       "75%    ...      0.648500      0.492150      0.522500      0.457000   \n",
       "max    ...     10.000000      9.552000     10.000000      8.346000   \n",
       "\n",
       "               g766          g767          g768          g769          g770  \\\n",
       "count  23814.000000  23814.000000  23814.000000  23814.000000  23814.000000   \n",
       "mean      -0.209799     -0.076251      0.134162     -0.128018     -0.219210   \n",
       "std        1.201918      1.115477      0.951264      1.230636      1.326193   \n",
       "min      -10.000000    -10.000000     -4.269000    -10.000000    -10.000000   \n",
       "25%       -0.606175     -0.506200     -0.353100     -0.544600     -0.554400   \n",
       "50%       -0.011850      0.009900      0.005400      0.000600      0.028700   \n",
       "75%        0.483000      0.511175      0.409075      0.498500      0.496400   \n",
       "max        5.444000      6.317000     10.000000      5.911000     10.000000   \n",
       "\n",
       "               g771  \n",
       "count  23814.000000  \n",
       "mean       0.101524  \n",
       "std        1.417674  \n",
       "min      -10.000000  \n",
       "25%       -0.523800  \n",
       "50%       -0.006500  \n",
       "75%        0.536950  \n",
       "max       10.000000  \n",
       "\n",
       "[8 rows x 772 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.loc[:,'g0':'g771'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apesar do describe reunir as nossas estatísticas de interesse, é bastante complexo analisar o dataframe resposta. Para facilitar o nosso entendimento, vamos plotar histogramas que nos ajudaram na visualização das estatísticas de todas as colunas selecionadas.\n",
    "Olhando o dataframe original, anteriormente, fizemos o histograma de apenas uma coluna. Mas agora, nosso conjunto de dados de interesse é o ```describe()``` que fizemos a partir do ```loc[]``` e, deste ponto de vista, não queremos mais fazer o histograma coluna a coluna (genes), queremos que ele seja a partir das linhas (estatísticas). Por isso, vamos transpor as linhas e colunas (transformar as linhas em colunas e vice-versa).\n",
    "Para isso, vamos usar o ```.T[]``` no código anterior que produzimos para organizar o ```describe()```.\n",
    "Ou seja, vamos manter todo o código até o ```describe()``` e, ao final, acrescentaremos o ```.T[]```. Mas, ao rodarmos essa linha, percebemos que temos como devolutiva o mesmo dataframe mas transposto. E, como aqui, nosso interesse é produzir histogramas, acrescentamos como argumento do ```.T[]``` a estatística alvo (```.T['estatística']```) e, por último, acrescentamos o ```.hist(bins = número de quebras)``` para que o histograma seja observado.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701a34e08>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas.loc[:,'g0':'g771'].describe().T['mean'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A seguir, reproduzimos o código acima apenas alterando o parâmetro estatístico a ser analisado (mínimo e máximo, por exemplo). \n",
    "E assim, podemos perceber as nuances de cada métrica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701f3f308>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas.loc[:,'g0':'g771'].describe().T['min'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701b69748>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas.loc[:,'g0':'g771'].describe().T['max'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É muito interessante que a gente replique a análise desenvolvida para os ```genes (g)```, nos ```tipos celulares (c)```. \n",
    "Por isso, vamos copiar a linha de código que produz os histogramas mas aqui, vamos modificar o argumento ```loc[:,'g-0':'g-771'] -> loc[:,'c-0':'c-99']``` e a quantidade de bins ```hist(bins=100) -> hist(bins=50)```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701f5abc8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1110ZrG5wgw0a7OzsVDAYVFlZmZYuXar+/n5nJzZDxIIFC8wrr7xijDFm9erV5sknnzxjn7/97W/mwQcfNJFIxHz22WfmxhtvNJFIJNOlxuWkj9bWVvPII49kurSEOOnjlMWLF5uJEyeat956K1PlOeakjyVLlpjGxkZjjDHt7e1m9uzZGa3RCSd9LFq0yDzzzDPGGGN27NhhbrjhBtPf35/ROgfjpI9wOGzq6+vNL3/5SzN37txMlxjTgQMHzJQpU8yRI0fMsWPHTFVVlfn8889P26eiosK89957xhhjHnroIdPQ0ODo3EPijryvr09bt25VWVmZJCkYDKq1tfWM/VpaWnT33XcrLy9P48eP15o1a2Ry6NOXTvv44IMP9NlnnykQCOjOO+/Up59+mulS43LahyRt2rRJo0eP1uWXX57JEh1x2kdtba1mz54tSdq3b5/GjBmT0ToH47SP3/zmN6qsrJQkjRs3Tj09PTp+/HhGa43HaR87duzQjh07cm7u0/cHDY4aNWpg0OApX375pU6ePKlrr71WUvy/mx8aEkF+5MgRFRYWKj//u+83+f1+HTx48Iz99uzZo61bt+r222/X7NmzdfjwYXk8ufMUOO3D6/VqxowZampq0vz583Xfffept7c30+XG5LSP/fv361//+pcWL16c6RIdcdqHx+ORx+PRtGnTtHLlSt1xxx2ZLjUup32UlZXpnHPOkSTV19friiuukM/ny2it8TjtY/z48aqtrR3oJVccOnRIfr9/4HFxcfFp9f/w97H6i8b1NzuzpaWlRStXrjxt27hx484Y2BVtgFc4HNaBAwfU0NCgTz/9VDU1NWppacnKizWZPh544IGBnydPnqxVq1Zp586dmjBhQnqKjcNtH5FIREuXLtUjjzyiESNGpL3OwSRzPU5pbW1VZ2en5s2bp5aWloERz5mUij7++c9/6vnnn9czzzyTlhqdSEUfuWawQYPJDCK0LsjLy8tVXl5+2ra+vj5NmjRJ4XBYw4YNiznA64ILLlBFRYXy8vI0YcIElZSUaNeuXVkZLZBMH2vXrlVlZaXOO+88Sd9d8FN3KZnmto+dO3dq586dWrp0qSTpP//5j5YtW6ZHH31U119/fcbqPyWZ6/H6669r4sSJGj16tK644gpdfPHF2rt3b1aCPJk+JOnJJ59Ue3u7GhoaVFJSkomSo0q2j1xUUlKibdu2DTz+Yf0/HER4+PBhx/3lzvsKSRg+fLiuu+46bdq0SZL08ssvRx3gNWXKlIF99u7dq6+++ko//vGPM1prPE772Lp1q1588UVJ0jvvvKNIJKKf/OQnGa01Hid9XHrppWpvb1dzc7Oam5v1s5/9TCtWrMhKiMfi9Ho0NTVp3bp1kqQvvvhChw8ftu56SN/dib/99tt69tlnsxrisTjtI1cNNmhw7Nix8nq9evfddyVJzc3NzvtL6T/LZtG+ffvM3LlzTXl5uZk3b5755ptvjDHGNDY2mr/+9a/GGGOOHj1q/vznP5vp06eb6dOnm7a2tmyWHJWTPg4cOGCqq6tNRUWFCQaDprOzM5slR+Wkj++bO3duTn5qxen1mDdvnqmqqjKzZs0yW7duzWbJUQ3WRyQSMdddd5359a9/bWbMmDHw34EDB7Jc+ekSeV299dZbOfWpFWOM2bBhg6moqDBTp041//jHP4wxxtTU1Jjt27cbY4zp7Ow0M2fONGVlZebBBx80PT09js7L0CwAsNyQeGsFAM5mBDkAWI4gBwDLEeQAYDmCHAAsR5ADgOUIcgCwHEEOAJb7H0Wu9Ziss0D8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas.loc[:,'c0':'c99'].describe().T['mean'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um outro tipo de gráfico super interessante e útil é o boxplot. \n",
    "Para visualizá-lo, vamos usar a função ```boxplot```do Seaborn e, como argumentos dessa função vamos passar um ```x```, onde ```x = coluna que será plotada neste eixo``` e a base de dados ```data = conjunto de dados```.\n",
    "O boxplot apresenta uma caixa no meio onde podemos identificar a mediana (linha no meio da caixa que é o ponto onde metade dos dados estão na direita e a outra metade para a esquerda), os outliers (pontos acima ou abaixo do eixo principal do gráfico que representam valores discrepantes para mais ou para menos), a maior concentração dos dados (caixa principal que representa onde está a mior parte dos dados - primeiro quartil (25%) e terceiro quartil (75%)) e os máximos e mínimos desconsiderando os outliers (linhas laterais à caixa principal).\n",
    "O boxplot é uma importante ferramenta na visualização de dados porque em apenas um gráfico, podemos identificar várias métricas estatísticas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701faf648>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='g0' , data=datas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos também, além de definir apenas os dados que irão no eixo x, definimos os dados para o outro eixo, atribuindo um valor para o parâmetro ```y``` (```y = variável que vai ser plotada neste eixo```).\n",
    "Como podemos perceber, no boxplot que representa o ```tratamento = com_droga``` apresenta muitos outliers e isso gera uma discussão bastante interessante pois, do ponto de vista biológico a investigação desses pontos é importante mas, dependendo da área que estamos trabalhando, esse ponto pode apresentar outras soluções. \n",
    "Dito isso, é importante para um cientista de dados não só entender e manipular a base de dados mas também saber acerca do negócio que estamos tratando."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a701eccec8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sKSlRcXGxFi5cKJ/Pp3Hjxuntt9/W4MGDG5YcjbJixXsqLl7uOoY106Y97jpCk8vJ6a/s7H6uYwCtRlJSkmpqaiJm4GTFtBP+wYMHVVlZWT9XVlae9FXgMzIyVFBQoKSkJCUmJur73/++vvzyy5N6LgAAbPl2+TrWDDSEzxhjTnSn5557Tq+99pquueYaGWO0ZMkS3XDDDQoEAlq9erWefvrpkzr4li1bNGbMGL388ss666yzTuo5gO/Kzc096raioiIHSYDWZ+nSpXr77bddx2gW69evP+q2Cy+80EGS5jV48GBdeeWVrmN4XkxvQY4fP17nnXee3nvvPSUkJOiRRx5R7969tX79euXl5Z3UgT///HPdeeedevDBBxtUvsrLKxUOn7AzohVr06atqqv/d4W2bdt2Kis74DAR0Hp8/XWVamtDrmM0C5/Pp2+vWfh8Pk/+Wb/+uorXzCbg9/uUnp583O/HtALW1NasWaN7771XkydP1rXXXtugx1LAEIsf/eim+q//9Ke/OEwCwCs+/XSdfvObwvr5Zz+brPPP994KGJrGiQpYTCtgTWnXrl2aMGGCpk+frqysLNuHRyvTtm071xEAeMQFF/SsXwVr164d5QuNYr2APf/886qurlZh4f/+LWL06NFs7Iom1aPHeZKkSZMecZwEgJecfvr/0c6dOzRhwn2uo6CFs17AHn74YT388MO2DwsAQKMlJ3dQjx7nsfqFRotpGwoAAAA0HQoYAACAZRQwAAAAyyhgAAAAllHAAAAALKOAAQAAWEYBAwAAsIwCBgAAYBkFDAAAwDIKGAAAgGUUMAAAAMsoYAAAAJZRwAAAACyjgAEAAFhGAQMAALCMAgYAAGAZBQwAAMAyChgAAIBlFDAAAADLKGAAAACWUcAAAAAso4ABAABYRgEDAACwjAIGAABgGQUMAADAMgoYAACAZRQwAAAAyyhgAAAAllHAAAAALKOAAQAAWJbgOkC8+stf/p+2b9/qOgZO0rZtR/7dTZv2uOMkOBlnntlVN9001nUMAGg2FLDj2L59qzZ9/oUCbVNdR8FJCIcCkqQvtu91nAQNFTocdB0BAJodBSyKQNtUte96lesYQKtyaOs7riMAQLPjHDAAAADLWAEDAIs4v7Rl4/zSli2ezi+lgAGARdu3b9WWLzaqczIvvy1Re4UlSYe/+sJxEjTUV5V1riNE4BUAACzrnJyg/+jZ0XUMoFV5Yd0+1xEicA4YAACAZRQwAAAAyyhgAAAAllHAAAAALKOAAQAAWOakgBUVFWnYsGEaMmSIXnrpJRcRAAAAnLG+DcXu3bs1ffp0LViwQElJSRo9erSuuOIKdevWzXYUAAAAJ6wXsJKSEvXu3VupqUcucn311Vfrrbfe0j333GM7SlQVFUGFDge5Lh1gWehwUBUVbFEIwNusv8rt2bNHGRkZ9XNmZqbWrVsX8+PT05ObI9ZRAgFOjwNcCQT8ysjo4DpGs0hMDOiw6xBAK5WYGIib1xbrBSwcDsvn89XPxpiI+UTKyysVDpvmiBYhOfl7CrStUfuuVzX7sQD8r0Nb31Fy8vdUVnbAdZRmUVsbch0BaLVqa0PWXlv8fl/URSPrBaxz585avXp1/VxWVqbMzEzbMQDAiYqKoPZX1sXdZVEAr/uqsk5pFUHXMepZf5+tT58+Ki0t1b59+1RVVaUlS5aoX79+tmMAAAA4Y30FrFOnTrrvvvs0duxY1dbWatSoUerZs6ftGADgREpKqtpU7eVi3IBlL6zbp7Ypqa5j1HPyUaPc3Fzl5ua6ODQAAIBzfNQPAADAMgoYAACAZRQwAAAAyyhgAAAAllHAAAAALOOCa1FwLciWK1x35GIv/oS2jpOgoUKHg5JOdR0DAJoVBew4zjyzq+sIaIRt27ZKkrqcyS/yludU/v8D4HkUsOO46aaxriOgEaZNe1ySNGnSI46TAABwNM4BAwAAsIwVMACw7Csuxt1iVdaEJUnJSaxftDRfVdbpLNchvoUCBgAWcX5by7bnX+eXntqZf48tzVmKr///KGAAYBHnl7ZsnF+KpsIaKgAAgGUUMAAAAMsoYAAAAJZRwAAAACyjgAEAAFhGAQMAALCMAgYAAGAZBQwAAMAyChgAAIBlFDAAAADLKGAAAACWUcAAAAAso4ABAABYRgEDAACwjAIGAABgGQUMAADAMgoYAACAZRQwAAAAyyhgAAAAllHAAAAALKOAAQAAWEYBAwAAsIwCBgAAYBkFDAAAwDIKGAAAgGUUMAAAAMsoYAAAAJZRwAAAACyjgMGTysr2aNOm/9FbbxW5jgIAwFEoYPCkffvKJUmvvvqy4yQAABzNegFbs2aNRo0apREjRui2227Tzp07bUeAx73xxusRM6tgAIB4k2D7gA888ICeeeYZnXvuuZo3b56eeOIJzZ4923aMVm/FivdUXLzcdYxmsWnT/0TMr776stau/cRRmuaTk9Nf2dn9XMcAAJwEqytgNTU1+slPfqJzzz1XktSjRw/t2rXLZgQAAADnrK6AJSUlacSIEZKkcDisWbNmadCgQQ16jvT05OaI1uqMHHmtRo681nWMZpGbm3vUbU899SsHSQB4TWJiQJKUkdHBcRK0dM1WwBYvXqwnn3wy4razzz5bL774ompqalRQUKC6ujrdeeedDXre8vJKhcOmKaOiFSgrO+A6AgAPqK0NSeI1BSfm9/uiLho1WwEbOnSohg4detTtBw8e1N13363U1FTNnj1biYmJzRUBAGCZl88vlaRt27ZKkqZNe9xxkubD+aV2ODkJv2vXrvqv//ov+f3sggEAaDlSUlJcR4BHWC1gGzZs0DvvvKNu3bopLy9PkpSZman//u//thkDANBMsrP7sXoCxMBqATv//PO1adMmm4cEAKDJBIP79eyzM3X33fcqJSXVdRy0YLwHCABAjIqKFurzzzdp0aIFrqOghaOAAQAQg2Bwv95/f7mMMSouXq6KiqDrSGjBKGAAAMSgqGihQqE6SVJdXR2rYGgUChgAADEoKSmWMUf2oTTGqKSk2HEitGQUMAAAYpCenh51BhqCAgYAQAzKy8ujzkBDUMAAAIhBnz45knz/mnz/moGTQwEDACAGubl5Skg4cjHuhIQEDR+e7zgRWjIKGAAAMUhNTVPfvgPk8/nUt29/NmJFo1i/FiQAAC1Vbm6edu7cweoXGo0CBgBAjFJT01RQMMV1DHgAb0HCczp1Oi3qDACAaxQweM4tt9wWMd966384SgIAwLFRwOA5paUrvjOzWzUAIL5QwOA5H3xAAQMAxDcKGDwnHA5HnQEAcI0CBgAAYBkFDJ7j8/mizgAAuEYBg+cYY6LOAAC4RgGD5/j9/qgzAACu8ZsJnpOamhYxp6V1dJQEAIBjo4DBc/btK4+Yy8v3OkoCAMCxUcAAAAAso4DBcwKBQNQZAE5WMLhfhYWPqaIi6DoKWjgKGDwnFApFnQHgZM2b94o++2yj5s592XUUtHAUMHjO6af/n6gzAJyMYHB//aXNSktXsAqGRqGAwXPGj58QMd955z2OkgDwknnzXqnfV9CYMKtgaBQKGDynS5ez1L79KZKk9u1P0ZlndnWcCIAXrFxZEnUGGoICBs8JBverurpaklRTU83bBACAuEMBg+cUFS2s/9oYadGiBQ7TAPCKK67oEzH37p3tKAm8gAIGzyktXaFQqE6SFArVqbR0heNEALxg1KjR9Zc28/v9GjVqtONEaMkoYPCcrKxs+f1H9v7y+wPKyuJvqQAaLzU1rX7VKysrRykpqY4ToSWjgMFzcnPzZExYkmSM0fDh+Y4TAfCKUaNGq3v3c1n9QqMluA4ANAefzydjjHw+10kAeElqapoKCqa4jgEPYAUMnlNUtFC+fzUvn8/HSfgAgLhDAYPnHDkJ/8jlh0KhECfhAwDiDgUMnpOVla1A4Mi764FAAifhAwDiDgUMnpObmye//8hbkH6/n5PwAQBxhwIGz0lNTVNOTn/5fD7l5PTjo+IAgLjDpyDhSbm5edq5cwerXwCAuOQz31zavYUoL69UONyiIgMAgFbG7/cpPT35+N+3mAWwJhjcr8LCx7gQNwAgLjkrYBs2bNCFF17o6vDwuHnzXtFnn23UvHmvuI4CAMBRnBSwqqoqPf7446qtrXVxeHhcMLi/fu+v0tJiVsEAAHHHSQErLCzUbbfd5uLQaAXmzXul/lqQ4XCYVTAAQNyx/inId955R4cPH9Y111xzUo+PdkIbIEkrV5ZEzB98sEIFBQ84SgMAwNGarYAtXrxYTz75ZMRtZ599tiorK/Xiiy+e9PPyKUicyDeXIfr2XFZ2wFEaAEBrdKJPQTZbARs6dKiGDh0acdvcuXP1hz/8QTfffHP9bSNGjNBLL72k5GRWttA0/H6/wuFwxAwAQDxxug9Yjx49tGnTpgY9hhUwnMgf/zhbJSXv1899+vTVuHF3O0wEAGht2AcMrc6oUaMj5htuGOMoCQAAx+a0gDV09QsAAMALWAGD5xQVLYyYFy1a4CgJAADHRgGD55SUFEedAQBwjQIGz0lPT486AwDgGgUMnlNeXh51BgDANQoYPKdPn5yoMwAArlHA4DmXXNIrYr700ssdJQEA4NgoYPCcP//5/0bMc+a84CgJAADHRgGD5+zevSvqDACAaxQwAAAAyyhg8Byfzx91BgDANX4zwXOysrKjzgAAuEYBg+dwMW4AQLyjgMFzUlPT1KdPX0lSnz59lZKS6jgRAK8IBversPAxVVQEXUdBC0cBgycNGTJU7dq109VXD3MdBYCHFBUt1Oefb9KiRQtcR0ELRwGDJy1fvlSHDx/WsmXvuI4CwCOCwf0qLl4uY4yKi99jFQyNQgGD5/AiCaA5FBUtVDhsJEnhcJhVMDQKBQyec+RFMixJCodDvEgCaBKlpSsUCtVJkkKhOpWWrnCcCC0ZBQyec+RFMiRJCoVCvEgCaBJZWdkKBBIkSYFAAlvcoFEoYPCcoy/GfZmjJAC8JDc3T36/T5Lk9/s1fHi+40RoyShg8DxjjOsIADwgNTVNOTn95fP5lJPTjy1u0CgJrgMATe2jj1ZHnQHgZOXm5mnnzh2sfqHRWAGD53ApIgDNJTU1TQUFU1j9QqNRwOA5/ftfGTEPGHCVoyQAABwbBQyes2TJ4qgzAACuUcDgOStXlkTMH3zANhQAgPhCAYPnfLMH2PFmAABco4DBc/x+f9QZAADX+M0Ez+ndOzvqDACAaxQweM6QIUMj5quvHuYoCQAAx0YBg+csX75UPt+Ry4X4fD4tW/aO40QAAESigMFzSktX1F9+yBjDxbgBAHGHAgbP4WLcAIB4RwGD53ExbgBAvKGAwXO4GDcAIN5RwOA5WVnZ8vsDkiS/P8DFuAEAcYcCBs/Jzc1TIHDkP+1AIKDhw/MdJwIAIBIFDJ6TmpqmnJz+8vl8ysnpp5SUVNeRAACIkOA6ANAccnPztHPnDla/AABxyWda2EfEyssrFQ63qMgAAKCV8ft9Sk9PPv73LWYBAACAKGAAAADWUcAAAAAss17A9uzZo/Hjx2vkyJEaPXq0duzYYTsCAACAU9YL2IMPPqiBAwfqtdde04gRI/TUU0/ZjgAAAOCU1U9B7tu3T8OGDVNpaal8Pp9qamr05Zdf6qyzzor5OfgUJAAAiHcn+hSk1X3Atm/frtNPP12FhYVavXq1MjIy9MgjjzToOaL9YQAAAFqCZlsBW7x4sZ588smI27p27apVq1Zp9uzZGjhwoObOnatFixZpzpw5MT8vK2AAACDenWgFzOpbkNu2bVNeXp7WrFkjSaqqqlLv3r21du3amJ+DAgYAAOJdXG3E2qVLF3Xu3FnLly+XJL377ru64IILbEYAAABwzvqliP75z3/q0Ucf1f79+5WcnKzCwkJOwgcAAJ4SV29BNgUKGAAAiHdx9SnIpuD3+1xHAAAAiOpEfaXFrYABAAC0dFwLEgAAwDIKGAAAgGUUMAAAAMsoYAAAAJZRwAAAACyjgAEAAFhGAQMAALCMAgYAAGAZBQwAAMAyChgAAIBlFDC0CgUFBVqwYIHrGABamXXr1unXv/51gx4zc+ZMzZw5s5kSIV5QwAAAaCZffPGFysvLXcdAHEpwHawRXkAAAAm+SURBVACtizFGTz31lP72t78pEAjoxhtvVL9+/TRlyhQFg0G1b99eDz30kHr27KmCggK1a9dOGzZs0Ndff637779fr7/+ujZu3KhBgwapoKAg6nEKCwu1bNkyZWZmKhQK6fLLL9eOHTs0btw4paWlqW3btnr++ec1depUlZaWyufzafjw4Ro/frwk6Te/+Y3++te/Ki0tTRkZGbryyiuVn5+v6dOnq7S0VBUVFcrMzNT06dN16qmn2voRAq2azdeQ7x7ntttu0+bNm497rOTkZH366afavXu3JkyYoMGDB2vGjBk6dOiQZs+erU6dOmnhwoUKBoMaOHCgxo4dq4ceekhffvmlEhISdN9996lfv34ROd577z3NmDFDdXV1OuOMM/T4448rLS2tuX/MsMEAFr355ptm9OjRprq62lRWVprhw4ebIUOGmL/+9a/GGGM+/vhjM2DAAFNdXW0mTZpkfvzjHxtjjFmwYIG59NJLzd69e82BAwfMxRdfbL7++uvjHmfx4sXmlltuMTU1Naa8vNxkZ2eb+fPnm+3bt5vu3bub7du3G2OM+fOf/2x+/OMfm7q6OnPo0CFz/fXXm3fffde88847ZsyYMaa6utoEg0EzcOBAM3/+fLNlyxZzzz33mFAoZIwx5oEHHjDPP/98M//UAHzD1mvIsY6zZ88ec/311x/3WBMmTDDhcNhs3LjRXH755cYYY+bPn28mTZpU//XgwYNNbW2tMcaYe++91/zpT38yxhizbds2k52dbcrKysyMGTPMjBkzTHl5uRk+fLgJBoPGGGNefvllM3ny5Gb4qcIFVsBg1apVqzR06FAlJSUpKSlJf/nLXzRw4EANGTJEkvTDH/5QKSkp+uc//ylJ9X8bPP3003XOOecoPT1dkpSamqqKigp16NDhmMf58MMPNWTIECUmJqpjx44Rf6tMT0/XGWecIUlauXKl8vLyFAgE1K5dO+Xm5qq0tFShUCgi56BBgyRJXbt21aRJkzR37lxt3rxZn3zyibp06dI8PywAR7H1GvLd47z++us6ePCgtm3bdtxjZWdny+fzqXv37goGg8d83vPPP18JCUd+9X7wwQd64oknJElnnnmmLrroIq1du7b+vmvXrtWuXbs0duxYSVI4HFZKSsrJ//AQVyhgsCohIUE+n69+3r59u4wxEfcxxigUCkmSEhMTIx4bK5/PF/G8335s27Zt678Oh8PHPLbf7z/qe5K0fv16/fSnP9Xtt9+uq6++Wn6//6j8AJqPrdeQ7x5nx44dxyw/3z5WmzZtJCnicd/17defaLklKRQK6ZJLLtGzzz4rSaqurtbBgwdj/jMgvnESPqy67LLLtGTJEtXW1qqqqkoTJ06Uz+fTkiVLJEmffPKJ9u7dq3POOadRx8nKytLixYtVU1OjiooKvf/++8e8X+/evfXaa68pFAqpqqpKRUVFuuKKK9SnTx8tWbJENTU1qqys1LJly+Tz+bRq1SpdfvnlGjNmjM466ywtW7Ys4gUTQPOy9Rry3eOMGzdOe/fu1RlnnNGgYwUCAdXV1R3ze71799a8efMkHSmSH330kX74wx/Wf/+iiy7SJ598os2bN0uSnnnmGf3qV79q1J8L8YMVMFg1ePBgrV+/Xvn5+QqHwxo7dqyuuOIK/eIXv9DMmTOVmJiomTNnKikpqVHHGTRokP7+97/ruuuu06mnnqrvf//7x7zfjTfeqC1btmjEiBGqra1Vbm6uBg8eLEn6+OOPlZeXp5SUFGVmZqpNmzYaNmyY7rnnHuXm5kqSLrzwQu3YsaNRWQHEztZryLGO82//9m/69a9/3aBj9ezZU7NmzdJTTz2ls88+O+J7Dz30kKZMmVK/Rc4TTzyhzMzM+u9nZGRo6tSpmjhxosLhsDp16tTgLS0Qv3yG90+Ao3z88cfasmWL8vLyVFtbqxtvvFFTp07Vueee6zoaAMADKGBosVavXq3HH3/8mN977rnn1KlTp5N+7mAwqJ/+9KcqKyuTMUYjR47UHXfccdLPByD+NOdrCHAiFDAAAADLOAkfAADAMgoYAACAZRQwAAAAyyhgAOLSj370I+3bt6/ZH9PU5s6dq5deeslpBgDxjwIGIC6tWLHCymOa2po1a3T48GHXMQDEOTZiBRB3fv7zn0uSbrvtNn3xxRe6+uqrtWnTJt1///1KSEjQH/7wB9XU1Gjfvn0aOXKkJk6cGPGY5557Ths3bjzm/VauXKnf/va3Ou2007R582a1a9dO48eP15w5c7R582YNGTJEkydPliQtXbpUs2fPVm1trdq2batJkybp4osv1syZM7Vz506VlZVp586d9Rtkrl27VkuXLtWKFSvUtm1b/fu//7sKCwtVWlqqQCCgnj176uc//7mSk5Od/WwBxAnrl/8GgBh0797dlJeXm4EDB5pZs2YZY4wJh8PmlltuMZs3bzbGGPPVV1+Z8847z5SXl0c8Jtr9PvjgA3PeeeeZTz/91BhjzB133GFuvPFGU11dbcrLy80FF1xgvvrqK7N582Zz3XXXmX379hljjPnss89Mdna2OXjwoJkxY4a56qqrzIEDB4wxxtx5553m6aefNsYYM2nSJPPHP/7RGGPM008/be655x5TU1NjQqGQKSgoMI888oiVnx+A+MYKGIC416tXL0lHLnL87LPPatmyZXrjjTf0j3/8Q8YYVVVVRdz/RPc744wzdP7550uSunTpog4dOigpKUkdO3bUKaecooqKCq1atUp79uzR7bffHvG827ZtkyRdfvnl9StZ559/vioqKo7K/d577+m+++6rvyD0rbfeqgkTJjTtDwdAi0QBAxD32rdvL0k6dOiQ8vLyNGjQIPXq1UvXX3+9/va3v8l8Zz/pE93vu9fuS0g4+qUwHA4rKytLv/vd7+pv27VrlzIzM/X222+rbdu29bf7fL6jMnzzHD6fL2Kura09iZ8AAK/hJHwAcSkQCKiuri7itq1bt6qyslITJ07UlVdeqZUrV6qmpkbhcDjiMSe6XyyysrK0YsUK/eMf/5AkLV++XMOHDz/hCfbfzt23b1+9/PLLqq2tVTgc1ksvvaTs7OyG/BgAeBQrYADi0jXXXKNbb71VBw8erL+tR48eGjBggIYOHaqkpCR1795d3bp109atW9WlS5f6xzz99NPHvd93V7+Op1u3bnrsscd0//33yxijhIQEzZ49W6ecckrUx/Xr10+FhYWSpLvvvlvTpk3TyJEjVVdXp549e+qRRx45+R8KAM/gWpAAAACW8RYkAACAZRQwAAAAyyhgAAAAllHAAAAALKOAAQAAWEYBAwAAsIwCBgAAYNn/B4BFlUCrObuMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(y='g0', x='tratamento' , data=datas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotar os histogramas com seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y4xdsBz6bhYoYWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(25, 7))\n",
    "fig.suptitle('Comparando distribuições G-0 e G20', fontsize=30, weight='bold')\n",
    "\n",
    "ax = sns.histplot(data=datas, x='g0', ax=axs[0], color = 'orange')\n",
    "ax = sns.histplot(data=datas,x='g20', ax=axs[1], color = 'purple')\n",
    "axs[1].set_ylabel('Quantidade',fontsize=14, weight='bold')\n",
    "axs[1].set_xlabel('Composto',fontsize=14, weight='bold')\n",
    "axs[0].set_ylabel('Quantidade',fontsize=14, weight='bold')\n",
    "axs[0].set_xlabel('Composto',fontsize=14, weight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the plot\n",
    "fig.savefig(\"comparacao.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>c90</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",
       "    </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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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