Aula_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importação Dos Pacotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importação Dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
    "\n",
    "data = pd.read_csv(url, compression = 'zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise e Limpeza Dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 1 - Por que a classe tratamento é tão desbalanceada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tratamento    droga    \n",
       "com_controle  cacb2b860    1866\n",
       "com_droga     00199ff52       6\n",
       "              00251fc41       6\n",
       "              00321ea80      12\n",
       "              0034c0847       6\n",
       "                           ... \n",
       "              ff9565933       6\n",
       "              ffd66e220       6\n",
       "              ffe357f8f       1\n",
       "              ffed8e1c9       6\n",
       "              fff7d208d       6\n",
       "Length: 3289, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_droga_trat = data.groupby(['tratamento','droga']).size()\n",
    "data_droga_trat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando a o dataframe pela coluna tratamento e por droga e calculando o tamanho, percebemos que todos os experimentos 'com_controle' possuem a coluna droga como 'cacb2b860' podendo indicar a ausência da droga, o que faz sentido ter menos experimentos já que o que vai variar no controle são as outras váriaveis tempo, e dose(a dose nesse caso pode indicar o tamanho do grupo de controle), e não a variável droga"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 2 - Plotar as 5 últimas linhas do dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 3 - Proporção na classe tratamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "com_droga       0.921643\n",
       "com_controle    0.078357\n",
       "Name: tratamento, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['tratamento'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 4 - Quantos tipos de droga foram investigados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3289"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['droga'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 5 - Documentação da função query do pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 6 - Renomear as colunas retirando o hífen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = data.columns.str.replace('-', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>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",
       "    <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>23807</th>\n",
       "      <td>id_fff7e6992</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>48</td>\n",
       "      <td>D1</td>\n",
       "      <td>a28556d51</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>-0.3696</td>\n",
       "      <td>-0.0093</td>\n",
       "      <td>-0.2495</td>\n",
       "      <td>-0.0175</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.5230</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>0.7715</td>\n",
       "      <td>0.5497</td>\n",
       "      <td>0.6929</td>\n",
       "      <td>0.3738</td>\n",
       "      <td>0.8636</td>\n",
       "      <td>0.4494</td>\n",
       "      <td>0.1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23808</th>\n",
       "      <td>id_fff8c2444</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>72</td>\n",
       "      <td>D1</td>\n",
       "      <td>6c3a459be</td>\n",
       "      <td>0.1608</td>\n",
       "      <td>-1.0500</td>\n",
       "      <td>0.2551</td>\n",
       "      <td>-0.2239</td>\n",
       "      <td>-0.2431</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>0.3538</td>\n",
       "      <td>0.0558</td>\n",
       "      <td>0.3377</td>\n",
       "      <td>-0.4753</td>\n",
       "      <td>-0.2504</td>\n",
       "      <td>-0.7415</td>\n",
       "      <td>0.8413</td>\n",
       "      <td>-0.4259</td>\n",
       "      <td>0.2434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23809</th>\n",
       "      <td>id_fffb1ceed</td>\n",
       "      <td>com_droga</td>\n",
       "      <td>24</td>\n",
       "      <td>D2</td>\n",
       "      <td>df1d0a5a1</td>\n",
       "      <td>0.1394</td>\n",
       "      <td>-0.0636</td>\n",
       "      <td>-0.1112</td>\n",
       "      <td>-0.5080</td>\n",
       "      <td>-0.4713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1969</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>-0.8121</td>\n",
       "      <td>0.3434</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>-0.3246</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.9171</td>\n",
       "      <td>0.5258</td>\n",
       "      <td>0.4680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23811</th>\n",
       "      <td>id_fffc1c3f4</td>\n",
       "      <td>com_controle</td>\n",
       "      <td>48</td>\n",
       "      <td>D2</td>\n",
       "      <td>cacb2b860</td>\n",
       "      <td>0.3942</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>-0.7389</td>\n",
       "      <td>0.5505</td>\n",
       "      <td>...</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>11769 rows × 877 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id    tratamento  tempo dose      droga      g0      g1  \\\n",
       "0      id_000644bb2     com_droga     24   D1  b68db1d53  1.0620  0.5577   \n",
       "1      id_000779bfc     com_droga     72   D1  df89a8e5a  0.0743  0.4087   \n",
       "2      id_000a6266a     com_droga     48   D1  18bb41b2c  0.6280  0.5817   \n",
       "6      id_001bd861f     com_droga     24   D2  746ca1f5a  2.0440  1.7000   \n",
       "7      id_0020d0484     com_droga     48   D1  8b87a7a83  0.2711  0.5133   \n",
       "...             ...           ...    ...  ...        ...     ...     ...   \n",
       "23807  id_fff7e6992     com_droga     48   D1  a28556d51  0.1420 -0.3696   \n",
       "23808  id_fff8c2444     com_droga     72   D1  6c3a459be  0.1608 -1.0500   \n",
       "23809  id_fffb1ceed     com_droga     24   D2  df1d0a5a1  0.1394 -0.0636   \n",
       "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",
       "0     -0.2479 -0.6208 -0.1944  ...  0.2862  0.2584  0.8076  0.5523 -0.1912   \n",
       "1      0.2991  0.0604  1.0190  ... -0.4265  0.7543  0.4708  0.0230  0.2957   \n",
       "2      1.5540 -0.0764 -0.0323  ... -0.7250 -0.6297  0.6103  0.0223 -1.3240   \n",
       "6     -1.5390  5.9440 -2.1670  ...  0.1855  1.1720  0.8325  0.6486  0.8090   \n",
       "7     -0.1327  2.5950  0.6980  ...  0.3230 -0.4140  0.2532  0.0513  0.8600   \n",
       "...       ...     ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "23807 -0.0093 -0.2495 -0.0175  ...  0.8775  0.5230  0.1589  0.7715  0.5497   \n",
       "23808  0.2551 -0.2239 -0.2431  ...  0.0789  0.3538  0.0558  0.3377 -0.4753   \n",
       "23809 -0.1112 -0.5080 -0.4713  ...  0.1969  0.0262 -0.8121  0.3434  0.5372   \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",
       "0      0.6584 -0.3981  0.2139  0.3801  0.4176  \n",
       "1      0.4899  0.1522  0.1241  0.6077  0.7371  \n",
       "2     -0.3174 -0.6417 -0.2187 -1.4080  0.6931  \n",
       "6      1.5880  2.4670  0.0357  0.1351 -0.3179  \n",
       "7      1.4250  0.6633  0.4562 -0.9622  0.0260  \n",
       "...       ...     ...     ...     ...     ...  \n",
       "23807  0.6929  0.3738  0.8636  0.4494  0.1218  \n",
       "23808 -0.2504 -0.7415  0.8413 -0.4259  0.2434  \n",
       "23809 -0.3246  0.0631  0.9171  0.5258  0.4680  \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",
       "[11769 rows x 877 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('g0 > 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>index</th>\n",
       "      <th>proporcao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>com_droga</td>\n",
       "      <td>0.921643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>com_controle</td>\n",
       "      <td>0.078357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          index  proporcao\n",
       "0     com_droga   0.921643\n",
       "1  com_controle   0.078357"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " data['tratamento'].value_counts(normalize=True).reset_index(name='proporcao')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 7 - Melhorar os gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>tratamento</th>\n",
       "      <th>proporcao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>com_droga</td>\n",
       "      <td>0.921643</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>com_controle</td>\n",
       "      <td>0.078357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tratamento  proporcao\n",
       "0     com_droga   0.921643\n",
       "1  com_controle   0.078357"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Criando outro dataframe com a propoção da classe tratamento\n",
    "prop_trat = data['tratamento'].value_counts(normalize=True).reset_index()\n",
    "prop_trat.columns = ['tratamento', 'proporcao']\n",
    "prop_trat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando figura e eixos\n",
    "fig, ax = plt.subplots(figsize=(20,10)) \n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#criando uma palheta de 2 cores\n",
    "sns.set_palette(sns.color_palette(\"hsv\", 2))\n",
    "\n",
    "#Criando o gráfico \n",
    "sns.barplot(x='tratamento', y='proporcao', data=prop_trat, ax=ax)\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "ax.set_xticklabels(labels= ['Com Droga', 'Com Controle'])\n",
    "plt.title('Proporção De Cada Tipo de Tratamento', fontsize=20)\n",
    "plt.xlabel('Tratamento', fontsize=15)\n",
    "plt.ylabel('Proporção', fontsize=15)\n",
    "plt.xticks( fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "#Adicionando os valores acima da barra\n",
    "for index, row in prop_trat.iterrows():\n",
    "    ax.text(index, row['proporcao'], round(row['proporcao'] * 100,2), color='black', ha=\"center\", fontsize=15)\n",
    "    \n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>tempo</th>\n",
       "      <th>quantidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48</td>\n",
       "      <td>8250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>7792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>7772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tempo  quantidade\n",
       "0     48        8250\n",
       "1     72        7792\n",
       "2     24        7772"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Contando a quantidade de experimentos pelo tempo\n",
    "df_group_tempo = data.groupby('tempo').size().reset_index(name='quantidade')\n",
    "df_group_tempo = df_group_tempo.sort_values('quantidade', ascending=False,  ignore_index=True)\n",
    "df_group_tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando figura e eixos\n",
    "fig, ax = plt.subplots(figsize=(20,10)) \n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#criando uma palheta de 3 cores\n",
    "sns.set_palette(sns.color_palette(\"winter\", 3))\n",
    "\n",
    "#Criando o gráfico \n",
    "sns.barplot(x='tempo', y='quantidade', data=df_group_tempo, ax=ax, order=df_group_tempo['tempo'])\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "plt.title('Quantidade De Experimentos Por Cada Tempo', fontsize=20)\n",
    "plt.xlabel('Tempo', fontsize=15)\n",
    "plt.ylabel('Quantidade', fontsize=15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "#Adicionando os valores acima da barra\n",
    "for index, row in df_group_tempo.iterrows():\n",
    "    ax.text(index, row['quantidade'], row['quantidade'], color='black', ha=\"center\", fontsize=15)\n",
    "\n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>dose</th>\n",
       "      <th>tratamento</th>\n",
       "      <th>Proporcao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D1</td>\n",
       "      <td>Com Droga</td>\n",
       "      <td>0.921709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D1</td>\n",
       "      <td>Com Controle</td>\n",
       "      <td>0.078291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D2</td>\n",
       "      <td>Com Droga</td>\n",
       "      <td>0.921574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D2</td>\n",
       "      <td>Com Controle</td>\n",
       "      <td>0.078426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dose    tratamento  Proporcao\n",
       "0   D1     Com Droga   0.921709\n",
       "1   D1  Com Controle   0.078291\n",
       "2   D2     Com Droga   0.921574\n",
       "3   D2  Com Controle   0.078426"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Agrupando por dose e comparando a proporção de tratamento\n",
    "data_group_dose = data.groupby('dose')['tratamento'].value_counts(normalize=True)\n",
    "data_group_dose = data_group_dose.reset_index(name='Proporcao')\n",
    "data_group_dose['tratamento'] = data_group_dose['tratamento'].str.replace('com_droga', 'Com Droga')\n",
    "data_group_dose['tratamento'] = data_group_dose['tratamento'].str.replace('com_controle', 'Com Controle')\n",
    "data_group_dose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando figura e eixos\n",
    "fig, ax = plt.subplots(figsize=(20,10)) \n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#criando uma palheta de 2 cores\n",
    "sns.set_palette(sns.color_palette(\"prism\", 2))\n",
    "\n",
    "#Criando o gráfico\n",
    "sns.barplot(x='dose', y='Proporcao', hue='tratamento', data=data_group_dose, ax=ax)\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "plt.title('Proporção dos Tipos De Tratamento Por Dose', fontsize=20)\n",
    "plt.xlabel('Dose', fontsize=15)\n",
    "plt.ylabel('Proporção', fontsize=15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "#Adicionando os valores acima da barra\n",
    "dict_loc = {0:-0.2, 1:0.2, 2:0.8, 3:1.2}\n",
    "for index, row in data_group_dose.iterrows():\n",
    "    ax.text(dict_loc[index], row['Proporcao'], round(row['Proporcao']*100,2), color='black', ha=\"center\", fontsize=12)\n",
    "    \n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Agrupando por tempo e comparando a proporção de tratamento\n",
    "data_group_tempo = data.groupby('tempo')['tratamento'].value_counts(normalize=True)\n",
    "data_group_tempo = data_group_tempo.reset_index(name='Proporcao')\n",
    "data_group_tempo['tratamento'] = data_group_tempo['tratamento'].str.replace('com_droga', 'Com Droga')\n",
    "data_group_tempo['tratamento'] = data_group_tempo['tratamento'].str.replace('com_controle', 'Com Controle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando figura e eixos\n",
    "fig, ax = plt.subplots(figsize=(20,10)) \n",
    "\n",
    "#Mudando o estilo de fundo\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#criando uma palheta de 2 cores\n",
    "sns.set_palette(sns.color_palette(\"prism\", 2))\n",
    "\n",
    "#Criando o gráfico\n",
    "sns.barplot(x='tempo', y='Proporcao', hue='tratamento', data=data_group_tempo, ax=ax)\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "plt.title('Proporção dos Tipos De Tratamento Por Tempo', fontsize=20)\n",
    "plt.xlabel('Tempo', fontsize=15)\n",
    "plt.ylabel('Proporção', fontsize=15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "#Adicionando os valores acima da barra\n",
    "dict_loc = {0:-0.2, 1:0.2, 2:0.8, 3:1.2, 4:1.8, 5:2.2}\n",
    "for index, row in data_group_tempo.iterrows():\n",
    "    ax.text(dict_loc[index], row['Proporcao'], round(row['Proporcao']*100,2), color='black', ha=\"center\", fontsize=12)\n",
    "    \n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partindo da hipótese que os dados das variáveis numéricas estão normalizados podemos analisar suas distribuições para checar se assemelham a uma distribuição normal, aqui estão os gráficos das 9 primeiras variáveis numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Criando figura e eixos\n",
    "fig, ax = plt.subplots(3, 3, figsize=(20,10)) \n",
    "\n",
    "#Criando o gráfico\n",
    "sns.distplot(data['g0'], ax=ax[0,0],color='b')\n",
    "sns.distplot(data['g1'], ax=ax[0,1],color='b')\n",
    "sns.distplot(data['g2'], ax=ax[0,2],color='b')\n",
    "sns.distplot(data['g3'], ax=ax[1,0],color='b')\n",
    "sns.distplot(data['g4'], ax=ax[1,1],color='b')\n",
    "sns.distplot(data['g5'], ax=ax[1,2],color='b')\n",
    "sns.distplot(data['g6'], ax=ax[2,0],color='b')\n",
    "sns.distplot(data['g7'], ax=ax[2,1],color='b')\n",
    "sns.distplot(data['g8'], ax=ax[2,2],color='b')\n",
    "\n",
    "#Plotando as legendas e mudando o tamanho das fontes\n",
    "fig.suptitle('Distribuição Das 9 Primeiras Variáveis Com Prefixo g', fontsize=20)\n",
    "    \n",
    "#Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De fato observando as distribuições é possível ver que algumas seguem uma distribuição normal e outras se aproximam de uma, também notamos que a maior incidência de valores está sempre perto do zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Desafio 8 - Resumo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir dos dados e dos gráficos observamos que os experimentos seguem o padrão de criar um experimento controle com determinada condição e em cima desse controle é possível realizar diversos experimentos variando apenas a droga, por esta razão a proporção de experimentos com droga é muito maior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dados obtidos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proporção na classe tratamento - 92.16% com_droga e 7.84 com_controle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A proporção na classe tratamento não muda conforme a dose e o tempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantidade de drogas diferentes - 3289"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nome : Pedro Henrique Corrêa de Almeida\n",
    "Linkedin : https://www.linkedin.com/in/pedro-henrique-corr%C3%AAa-de-almeida-15398b105/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linkedin : https://www.linkedin.com/in/pedro-henrique-corr%C3%AAa-de-almeida-15398b105/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}