five_personality_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importando bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from io import open\n",
    "pd.options.display.max_columns = 150"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carregando o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data-final.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando o dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9419.0</td>\n",
       "      <td>5491.0</td>\n",
       "      <td>3959.0</td>\n",
       "      <td>4821.0</td>\n",
       "      <td>5611.0</td>\n",
       "      <td>2756.0</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>4110.0</td>\n",
       "      <td>6135.0</td>\n",
       "      <td>4150.0</td>\n",
       "      <td>5739.0</td>\n",
       "      <td>6364.0</td>\n",
       "      <td>3663.0</td>\n",
       "      <td>5070.0</td>\n",
       "      <td>5709.0</td>\n",
       "      <td>4285.0</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>11641.0</td>\n",
       "      <td>3115.0</td>\n",
       "      <td>3207.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>10235.0</td>\n",
       "      <td>5897.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>6602.0</td>\n",
       "      <td>5457.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>3762.0</td>\n",
       "      <td>4420.0</td>\n",
       "      <td>9382.0</td>\n",
       "      <td>5286.0</td>\n",
       "      <td>4983.0</td>\n",
       "      <td>6339.0</td>\n",
       "      <td>3146.0</td>\n",
       "      <td>4067.0</td>\n",
       "      <td>2959.0</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>4920.0</td>\n",
       "      <td>4436.0</td>\n",
       "      <td>3116.0</td>\n",
       "      <td>2992.0</td>\n",
       "      <td>4354.0</td>\n",
       "      <td>2016-03-03 02:01:01</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>51.5448</td>\n",
       "      <td>0.1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7235.0</td>\n",
       "      <td>3598.0</td>\n",
       "      <td>3315.0</td>\n",
       "      <td>2564.0</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>4787.0</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>9036.0</td>\n",
       "      <td>2406.0</td>\n",
       "      <td>3484.0</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>3061.0</td>\n",
       "      <td>2539.0</td>\n",
       "      <td>4226.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>1607.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>3422.0</td>\n",
       "      <td>5324.0</td>\n",
       "      <td>4494.0</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>1747.0</td>\n",
       "      <td>5163.0</td>\n",
       "      <td>5240.0</td>\n",
       "      <td>7208.0</td>\n",
       "      <td>2783.0</td>\n",
       "      <td>4103.0</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>3347.0</td>\n",
       "      <td>2399.0</td>\n",
       "      <td>3360.0</td>\n",
       "      <td>5595.0</td>\n",
       "      <td>2624.0</td>\n",
       "      <td>4985.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>3026.0</td>\n",
       "      <td>4742.0</td>\n",
       "      <td>3336.0</td>\n",
       "      <td>2718.0</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>3096.0</td>\n",
       "      <td>3019.0</td>\n",
       "      <td>2016-03-03 02:01:20</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>MY</td>\n",
       "      <td>3.1698</td>\n",
       "      <td>101.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4657.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>5847.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>4922.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>14621.0</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>5128.0</td>\n",
       "      <td>3675.0</td>\n",
       "      <td>3442.0</td>\n",
       "      <td>4546.0</td>\n",
       "      <td>8275.0</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>3813.0</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>3386.0</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2562.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>13719.0</td>\n",
       "      <td>3892.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>4286.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>2713.0</td>\n",
       "      <td>2813.0</td>\n",
       "      <td>4237.0</td>\n",
       "      <td>6308.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>2983.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1644.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>8114.0</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>6295.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>2529.0</td>\n",
       "      <td>2016-03-03 02:01:56</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>54.9119</td>\n",
       "      <td>-1.3833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3996.0</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>5096.0</td>\n",
       "      <td>4240.0</td>\n",
       "      <td>5168.0</td>\n",
       "      <td>5456.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>5240.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3736.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>3015.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>4064.0</td>\n",
       "      <td>4208.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>7336.0</td>\n",
       "      <td>3896.0</td>\n",
       "      <td>6062.0</td>\n",
       "      <td>11952.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>3664.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>4912.0</td>\n",
       "      <td>7545.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>6896.0</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2368.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>2848.0</td>\n",
       "      <td>6264.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>10472.0</td>\n",
       "      <td>3192.0</td>\n",
       "      <td>7704.0</td>\n",
       "      <td>3456.0</td>\n",
       "      <td>6665.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>3728.0</td>\n",
       "      <td>4128.0</td>\n",
       "      <td>3776.0</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>4192.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>3257.0</td>\n",
       "      <td>2016-03-03 02:02:02</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>51.75</td>\n",
       "      <td>-1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6004.0</td>\n",
       "      <td>3965.0</td>\n",
       "      <td>2721.0</td>\n",
       "      <td>3706.0</td>\n",
       "      <td>2968.0</td>\n",
       "      <td>2426.0</td>\n",
       "      <td>7339.0</td>\n",
       "      <td>3302.0</td>\n",
       "      <td>16819.0</td>\n",
       "      <td>3731.0</td>\n",
       "      <td>4740.0</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>7461.0</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>3324.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>4308.0</td>\n",
       "      <td>4506.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>3115.0</td>\n",
       "      <td>6771.0</td>\n",
       "      <td>2819.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>16204.0</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>28983.0</td>\n",
       "      <td>1612.0</td>\n",
       "      <td>2437.0</td>\n",
       "      <td>4532.0</td>\n",
       "      <td>3843.0</td>\n",
       "      <td>7019.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>6550.0</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>21500.0</td>\n",
       "      <td>20587.0</td>\n",
       "      <td>8458.0</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>17042.0</td>\n",
       "      <td>7029.0</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>5835.0</td>\n",
       "      <td>6846.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>11401.0</td>\n",
       "      <td>8642.0</td>\n",
       "      <td>2016-03-03 02:02:57</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>KE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  EST2  \\\n",
       "0   4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0   1.0   4.0   \n",
       "1   3.0   5.0   3.0   4.0   3.0   3.0   2.0   5.0   1.0    5.0   2.0   3.0   \n",
       "2   2.0   3.0   4.0   4.0   3.0   2.0   1.0   3.0   2.0    5.0   4.0   4.0   \n",
       "3   2.0   2.0   2.0   3.0   4.0   2.0   2.0   4.0   1.0    4.0   3.0   3.0   \n",
       "4   3.0   3.0   3.0   3.0   5.0   3.0   3.0   5.0   3.0    4.0   1.0   5.0   \n",
       "\n",
       "   EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  AGR3  AGR4  \\\n",
       "0   4.0   2.0   2.0   2.0   2.0   2.0   3.0    2.0   2.0   5.0   2.0   4.0   \n",
       "1   4.0   1.0   3.0   1.0   2.0   1.0   3.0    1.0   1.0   4.0   1.0   5.0   \n",
       "2   4.0   2.0   2.0   2.0   2.0   2.0   1.0    3.0   1.0   4.0   1.0   4.0   \n",
       "3   3.0   2.0   3.0   2.0   2.0   2.0   4.0    3.0   2.0   4.0   3.0   4.0   \n",
       "4   5.0   3.0   1.0   1.0   1.0   1.0   3.0    2.0   1.0   5.0   1.0   5.0   \n",
       "\n",
       "   AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  CSN4  CSN5  CSN6  \\\n",
       "0   2.0   3.0   2.0   4.0   3.0    4.0   3.0   4.0   3.0   2.0   2.0   4.0   \n",
       "1   1.0   5.0   3.0   4.0   5.0    3.0   3.0   2.0   5.0   3.0   3.0   1.0   \n",
       "2   2.0   4.0   1.0   4.0   4.0    3.0   4.0   2.0   2.0   2.0   3.0   3.0   \n",
       "3   2.0   4.0   2.0   4.0   3.0    4.0   2.0   4.0   4.0   4.0   1.0   2.0   \n",
       "4   1.0   3.0   1.0   5.0   5.0    3.0   5.0   1.0   5.0   1.0   3.0   1.0   \n",
       "\n",
       "   CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  \\\n",
       "0   4.0   2.0   4.0    4.0   5.0   1.0   4.0   1.0   4.0   1.0   5.0   3.0   \n",
       "1   3.0   3.0   5.0    3.0   1.0   2.0   4.0   2.0   3.0   1.0   4.0   2.0   \n",
       "2   4.0   2.0   4.0    2.0   5.0   1.0   2.0   1.0   4.0   2.0   5.0   3.0   \n",
       "3   2.0   3.0   1.0    4.0   4.0   2.0   5.0   2.0   3.0   1.0   4.0   4.0   \n",
       "4   5.0   1.0   5.0    5.0   5.0   1.0   5.0   1.0   5.0   1.0   5.0   3.0   \n",
       "\n",
       "   OPN9  OPN10  EXT1_E  EXT2_E  EXT3_E  EXT4_E  EXT5_E  EXT6_E  EXT7_E  \\\n",
       "0   4.0    5.0  9419.0  5491.0  3959.0  4821.0  5611.0  2756.0  2388.0   \n",
       "1   5.0    3.0  7235.0  3598.0  3315.0  2564.0  2976.0  3050.0  4787.0   \n",
       "2   4.0    4.0  4657.0  3549.0  2543.0  3335.0  5847.0  2540.0  4922.0   \n",
       "3   3.0    3.0  3996.0  2896.0  5096.0  4240.0  5168.0  5456.0  4360.0   \n",
       "4   5.0    5.0  6004.0  3965.0  2721.0  3706.0  2968.0  2426.0  7339.0   \n",
       "\n",
       "   EXT8_E   EXT9_E  EXT10_E  EST1_E  EST2_E  EST3_E  EST4_E  EST5_E  EST6_E  \\\n",
       "0  2113.0   5900.0   4110.0  6135.0  4150.0  5739.0  6364.0  3663.0  5070.0   \n",
       "1  3228.0   3465.0   3309.0  9036.0  2406.0  3484.0  3359.0  3061.0  2539.0   \n",
       "2  3142.0  14621.0   2191.0  5128.0  3675.0  3442.0  4546.0  8275.0  2185.0   \n",
       "3  4496.0   5240.0   4000.0  3736.0  4616.0  3015.0  2711.0  3960.0  4064.0   \n",
       "4  3302.0  16819.0   3731.0  4740.0  2856.0  7461.0  2179.0  3324.0  2255.0   \n",
       "\n",
       "   EST7_E  EST8_E  EST9_E  EST10_E  AGR1_E   AGR2_E   AGR3_E  AGR4_E   AGR5_E  \\\n",
       "0  5709.0  4285.0  2587.0   3997.0  4750.0   5475.0  11641.0  3115.0   3207.0   \n",
       "1  4226.0  2962.0  1799.0   1607.0  2158.0   2090.0   2143.0  2807.0   3422.0   \n",
       "2  2164.0  1175.0  3813.0   1593.0  1089.0   2203.0   3386.0  1464.0   2562.0   \n",
       "3  4208.0  2936.0  7336.0   3896.0  6062.0  11952.0   1040.0  2264.0   3664.0   \n",
       "4  4308.0  4506.0  3127.0   3115.0  6771.0   2819.0   3682.0  2511.0  16204.0   \n",
       "\n",
       "   AGR6_E   AGR7_E   AGR8_E  AGR9_E  AGR10_E  CSN1_E  CSN2_E  CSN3_E  CSN4_E  \\\n",
       "0  3260.0  10235.0   5897.0  1758.0   3081.0  6602.0  5457.0  1569.0  2129.0   \n",
       "1  5324.0   4494.0   3627.0  1850.0   1747.0  5163.0  5240.0  7208.0  2783.0   \n",
       "2  1493.0   3067.0  13719.0  3892.0   4100.0  4286.0  4775.0  2713.0  2813.0   \n",
       "3  3049.0   4912.0   7545.0  4632.0   6896.0  2824.0   520.0  2368.0  3225.0   \n",
       "4  1736.0  28983.0   1612.0  2437.0   4532.0  3843.0  7019.0  3102.0  3153.0   \n",
       "\n",
       "   CSN5_E  CSN6_E  CSN7_E   CSN8_E   CSN9_E  CSN10_E  OPN1_E  OPN2_E   OPN3_E  \\\n",
       "0  3762.0  4420.0  9382.0   5286.0   4983.0   6339.0  3146.0  4067.0   2959.0   \n",
       "1  4103.0  3431.0  3347.0   2399.0   3360.0   5595.0  2624.0  4985.0   1684.0   \n",
       "2  4237.0  6308.0  2690.0   1516.0   2379.0   2983.0  1930.0  1470.0   1644.0   \n",
       "3  2848.0  6264.0  3760.0  10472.0   3192.0   7704.0  3456.0  6665.0   1977.0   \n",
       "4  2869.0  6550.0  1811.0   3682.0  21500.0  20587.0  8458.0  3510.0  17042.0   \n",
       "\n",
       "   OPN4_E  OPN5_E  OPN6_E  OPN7_E  OPN8_E   OPN9_E  OPN10_E  \\\n",
       "0  3411.0  2170.0  4920.0  4436.0  3116.0   2992.0   4354.0   \n",
       "1  3026.0  4742.0  3336.0  2718.0  3374.0   3096.0   3019.0   \n",
       "2  1683.0  2229.0  8114.0  2043.0  6295.0   1585.0   2529.0   \n",
       "3  3728.0  4128.0  3776.0  2984.0  4192.0   3480.0   3257.0   \n",
       "4  7029.0  2327.0  5835.0  6846.0  5320.0  11401.0   8642.0   \n",
       "\n",
       "              dateload  screenw  screenh  introelapse  testelapse  endelapse  \\\n",
       "0  2016-03-03 02:01:01    768.0   1024.0          9.0       234.0          6   \n",
       "1  2016-03-03 02:01:20   1360.0    768.0         12.0       179.0         11   \n",
       "2  2016-03-03 02:01:56   1366.0    768.0          3.0       186.0          7   \n",
       "3  2016-03-03 02:02:02   1920.0   1200.0        186.0       219.0          7   \n",
       "4  2016-03-03 02:02:57   1366.0    768.0          8.0       315.0         17   \n",
       "\n",
       "   IPC country lat_appx_lots_of_err long_appx_lots_of_err  \n",
       "0    1      GB              51.5448                0.1991  \n",
       "1    1      MY               3.1698               101.706  \n",
       "2    1      GB              54.9119               -1.3833  \n",
       "3    1      GB                51.75                 -1.25  \n",
       "4    2      KE                  1.0                  38.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Excluindo os atributos irrelevantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(data.columns[50:110], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.drop(data.columns[50:], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando novamente os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>EST7</th>\n",
       "      <th>EST8</th>\n",
       "      <th>EST9</th>\n",
       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
       "      <th>AGR2</th>\n",
       "      <th>AGR3</th>\n",
       "      <th>AGR4</th>\n",
       "      <th>AGR5</th>\n",
       "      <th>AGR6</th>\n",
       "      <th>AGR7</th>\n",
       "      <th>AGR8</th>\n",
       "      <th>AGR9</th>\n",
       "      <th>AGR10</th>\n",
       "      <th>CSN1</th>\n",
       "      <th>CSN2</th>\n",
       "      <th>CSN3</th>\n",
       "      <th>CSN4</th>\n",
       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  EST2  \\\n",
       "0   4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0   1.0   4.0   \n",
       "1   3.0   5.0   3.0   4.0   3.0   3.0   2.0   5.0   1.0    5.0   2.0   3.0   \n",
       "2   2.0   3.0   4.0   4.0   3.0   2.0   1.0   3.0   2.0    5.0   4.0   4.0   \n",
       "3   2.0   2.0   2.0   3.0   4.0   2.0   2.0   4.0   1.0    4.0   3.0   3.0   \n",
       "4   3.0   3.0   3.0   3.0   5.0   3.0   3.0   5.0   3.0    4.0   1.0   5.0   \n",
       "\n",
       "   EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  AGR3  AGR4  \\\n",
       "0   4.0   2.0   2.0   2.0   2.0   2.0   3.0    2.0   2.0   5.0   2.0   4.0   \n",
       "1   4.0   1.0   3.0   1.0   2.0   1.0   3.0    1.0   1.0   4.0   1.0   5.0   \n",
       "2   4.0   2.0   2.0   2.0   2.0   2.0   1.0    3.0   1.0   4.0   1.0   4.0   \n",
       "3   3.0   2.0   3.0   2.0   2.0   2.0   4.0    3.0   2.0   4.0   3.0   4.0   \n",
       "4   5.0   3.0   1.0   1.0   1.0   1.0   3.0    2.0   1.0   5.0   1.0   5.0   \n",
       "\n",
       "   AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  CSN4  CSN5  CSN6  \\\n",
       "0   2.0   3.0   2.0   4.0   3.0    4.0   3.0   4.0   3.0   2.0   2.0   4.0   \n",
       "1   1.0   5.0   3.0   4.0   5.0    3.0   3.0   2.0   5.0   3.0   3.0   1.0   \n",
       "2   2.0   4.0   1.0   4.0   4.0    3.0   4.0   2.0   2.0   2.0   3.0   3.0   \n",
       "3   2.0   4.0   2.0   4.0   3.0    4.0   2.0   4.0   4.0   4.0   1.0   2.0   \n",
       "4   1.0   3.0   1.0   5.0   5.0    3.0   5.0   1.0   5.0   1.0   3.0   1.0   \n",
       "\n",
       "   CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  \\\n",
       "0   4.0   2.0   4.0    4.0   5.0   1.0   4.0   1.0   4.0   1.0   5.0   3.0   \n",
       "1   3.0   3.0   5.0    3.0   1.0   2.0   4.0   2.0   3.0   1.0   4.0   2.0   \n",
       "2   4.0   2.0   4.0    2.0   5.0   1.0   2.0   1.0   4.0   2.0   5.0   3.0   \n",
       "3   2.0   3.0   1.0    4.0   4.0   2.0   5.0   2.0   3.0   1.0   4.0   4.0   \n",
       "4   5.0   1.0   5.0    5.0   5.0   1.0   5.0   1.0   5.0   1.0   5.0   3.0   \n",
       "\n",
       "   OPN9  OPN10  \n",
       "0   4.0    5.0  \n",
       "1   5.0    3.0  \n",
       "2   4.0    4.0  \n",
       "3   3.0    3.0  \n",
       "4   5.0    5.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    288622\n",
       "1.0    249711\n",
       "2.0    199165\n",
       "4.0    191638\n",
       "5.0     80702\n",
       "0.0      3720\n",
       "Name: EXT1, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"EXT1\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analisando as estatísticas da base de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
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       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
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       "      <th>count</th>\n",
       "      <td>1013558.00</td>\n",
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       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.65</td>\n",
       "      <td>2.77</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.77</td>\n",
       "      <td>3.41</td>\n",
       "      <td>2.96</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.17</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.05</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.09</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.26</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.26</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.27</td>\n",
       "      <td>3.74</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.59</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.93</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.83</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.47</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.59</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.79</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.18</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EXT1       EXT2       EXT3       EXT4       EXT5       EXT6  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        2.65       2.77       3.29       3.14       3.28       2.40   \n",
       "std         1.26       1.32       1.22       1.24       1.28       1.23   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         1.00       2.00       2.00       2.00       2.00       1.00   \n",
       "50%         3.00       3.00       3.00       3.00       3.00       2.00   \n",
       "75%         4.00       4.00       4.00       4.00       4.00       3.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            EXT7       EXT8       EXT9      EXT10       EST1       EST2  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        2.77       3.41       2.96       3.56       3.29       3.17   \n",
       "std         1.40       1.27       1.35       1.31       1.35       1.23   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         2.00       2.00       2.00       3.00       2.00       2.00   \n",
       "50%         3.00       4.00       3.00       4.00       3.00       3.00   \n",
       "75%         4.00       4.00       4.00       5.00       4.00       4.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            EST3       EST4       EST5       EST6       EST7       EST8  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        3.85       2.66       2.84       2.84       3.05       2.68   \n",
       "std         1.16       1.25       1.27       1.33       1.29       1.34   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         3.00       2.00       2.00       2.00       2.00       2.00   \n",
       "50%         4.00       3.00       3.00       3.00       3.00       3.00   \n",
       "75%         5.00       4.00       4.00       4.00       4.00       4.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            EST9      EST10       AGR1       AGR2       AGR3       AGR4  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        3.09       2.77       2.26       3.83       2.26       3.93   \n",
       "std         1.30       1.32       1.34       1.14       1.28       1.13   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         2.00       2.00       1.00       3.00       1.00       3.00   \n",
       "50%         3.00       3.00       2.00       4.00       2.00       4.00   \n",
       "75%         4.00       4.00       3.00       5.00       3.00       5.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            AGR5       AGR6       AGR7       AGR8       AGR9      AGR10  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        2.27       3.74       2.20       3.69       3.79       3.59   \n",
       "std         1.17       1.22       1.12       1.10       1.17       1.08   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         1.00       3.00       1.00       3.00       3.00       3.00   \n",
       "50%         2.00       4.00       2.00       4.00       4.00       4.00   \n",
       "75%         3.00       5.00       3.00       4.00       5.00       4.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            CSN1       CSN2       CSN3       CSN4       CSN5       CSN6  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        3.30       2.93       3.98       2.62       2.63       2.83   \n",
       "std         1.18       1.39       1.04       1.25       1.27       1.42   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         3.00       2.00       3.00       2.00       2.00       2.00   \n",
       "50%         3.00       3.00       4.00       2.00       3.00       3.00   \n",
       "75%         4.00       4.00       5.00       4.00       4.00       4.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            CSN7       CSN8       CSN9      CSN10       OPN1       OPN2  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        3.70       2.47       3.20       3.59       3.65       2.08   \n",
       "std         1.13       1.14       1.28       1.05       1.16       1.11   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         3.00       2.00       2.00       3.00       3.00       1.00   \n",
       "50%         4.00       2.00       3.00       4.00       4.00       2.00   \n",
       "75%         5.00       3.00       4.00       4.00       5.00       3.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            OPN3       OPN4       OPN5       OPN6       OPN7       OPN8  \\\n",
       "count 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00 1013558.00   \n",
       "mean        4.00       2.00       3.79       1.89       3.98       3.18   \n",
       "std         1.10       1.09       0.99       1.10       1.01       1.26   \n",
       "min         0.00       0.00       0.00       0.00       0.00       0.00   \n",
       "25%         3.00       1.00       3.00       1.00       3.00       2.00   \n",
       "50%         4.00       2.00       4.00       2.00       4.00       3.00   \n",
       "75%         5.00       3.00       5.00       2.00       5.00       4.00   \n",
       "max         5.00       5.00       5.00       5.00       5.00       5.00   \n",
       "\n",
       "            OPN9      OPN10  \n",
       "count 1013558.00 1013558.00  \n",
       "mean        4.12       3.96  \n",
       "std         1.04       1.03  \n",
       "min         0.00       0.00  \n",
       "25%         4.00       3.00  \n",
       "50%         4.00       4.00  \n",
       "75%         5.00       5.00  \n",
       "max         5.00       5.00  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.float_format = \"{:.2f}\".format\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando a contagem de registros por valor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.00    288622\n",
       "1.00    249711\n",
       "2.00    199165\n",
       "4.00    191638\n",
       "5.00     80702\n",
       "0.00      3720\n",
       "Name: EXT1, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"EXT1\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecionando o total de registros com o valor zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
       "      <th>EXT4</th>\n",
       "      <th>EXT5</th>\n",
       "      <th>EXT6</th>\n",
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       "      <th>EST2</th>\n",
       "      <th>EST3</th>\n",
       "      <th>EST4</th>\n",
       "      <th>EST5</th>\n",
       "      <th>EST6</th>\n",
       "      <th>EST7</th>\n",
       "      <th>EST8</th>\n",
       "      <th>EST9</th>\n",
       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
       "      <th>AGR2</th>\n",
       "      <th>AGR3</th>\n",
       "      <th>AGR4</th>\n",
       "      <th>AGR5</th>\n",
       "      <th>AGR6</th>\n",
       "      <th>AGR7</th>\n",
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       "      <th>AGR9</th>\n",
       "      <th>AGR10</th>\n",
       "      <th>CSN1</th>\n",
       "      <th>CSN2</th>\n",
       "      <th>CSN3</th>\n",
       "      <th>CSN4</th>\n",
       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "      <td>2094.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         EXT1    EXT2    EXT3    EXT4    EXT5    EXT6    EXT7    EXT8    EXT9  \\\n",
       "count 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00   \n",
       "mean     0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "std      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "min      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "25%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "50%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "75%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "max      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "\n",
       "        EXT10    EST1    EST2    EST3    EST4    EST5    EST6    EST7    EST8  \\\n",
       "count 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00   \n",
       "mean     0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "std      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "min      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "25%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "50%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "75%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "max      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "\n",
       "         EST9   EST10    AGR1    AGR2    AGR3    AGR4    AGR5    AGR6    AGR7  \\\n",
       "count 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00   \n",
       "mean     0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "std      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "min      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "25%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "50%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "75%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "max      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "\n",
       "         AGR8    AGR9   AGR10    CSN1    CSN2    CSN3    CSN4    CSN5    CSN6  \\\n",
       "count 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00   \n",
       "mean     0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "std      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "min      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "25%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "50%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "75%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "max      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "\n",
       "         CSN7    CSN8    CSN9   CSN10    OPN1    OPN2    OPN3    OPN4    OPN5  \\\n",
       "count 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00 2094.00   \n",
       "mean     0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "std      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "min      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "25%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "50%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "75%      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "max      0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "\n",
       "         OPN6    OPN7    OPN8    OPN9   OPN10  \n",
       "count 2094.00 2094.00 2094.00 2094.00 2094.00  \n",
       "mean     0.00    0.00    0.00    0.00    0.00  \n",
       "std      0.00    0.00    0.00    0.00    0.00  \n",
       "min      0.00    0.00    0.00    0.00    0.00  \n",
       "25%      0.00    0.00    0.00    0.00    0.00  \n",
       "50%      0.00    0.00    0.00    0.00    0.00  \n",
       "75%      0.00    0.00    0.00    0.00    0.00  \n",
       "max      0.00    0.00    0.00    0.00    0.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data == 0.00).all(axis=1)].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Limpando o Dataframe com apenas registros maiores que zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[(data > 0.00).all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando a contagem de registros por valor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.00    247835\n",
       "1.00    216949\n",
       "2.00    176207\n",
       "4.00    166599\n",
       "5.00     66844\n",
       "Name: EXT1, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"EXT1\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Qual o número de clusters que vai ser definido?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instalando a yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yellowbrick\n",
      "  Downloading yellowbrick-1.3.post1-py3-none-any.whl (271 kB)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from yellowbrick) (1.5.2)\n",
      "Requirement already satisfied: numpy<1.20,>=1.16.0 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from yellowbrick) (1.19.2)\n",
      "Requirement already satisfied: scikit-learn>=0.20 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from yellowbrick) (0.23.2)\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from yellowbrick) (3.3.2)\n",
      "Requirement already satisfied: cycler>=0.10.0 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from yellowbrick) (0.10.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20->yellowbrick) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20->yellowbrick) (0.17.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2020.6.20)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (8.0.1)\n",
      "Requirement already satisfied: six in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from cycler>=0.10.0->yellowbrick) (1.15.0)\n",
      "Installing collected packages: yellowbrick\n",
      "Successfully installed yellowbrick-1.3.post1\n"
     ]
    }
   ],
   "source": [
    "!pip install yellowbrick"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importando as bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from yellowbrick.cluster import KElbowVisualizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instanciando o método KMeans e o Visualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans()\n",
    "visualizer = KElbowVisualizer(kmeans, k=(2,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecionando uma amostra aleatória dos dados com 5000 observações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sample = data.sample(n=5000, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executando o teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer.fit(data_sample)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agrupando os participantes em 5 grupos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Atribuindo os registros aos devidos grupos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=5)\n",
    "k_fit = kmeans.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inserindo os rótulos dos clusters no dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicoes = k_fit.labels_\n",
    "data['Clusters'] = predicoes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
       "      <th>EXT4</th>\n",
       "      <th>EXT5</th>\n",
       "      <th>EXT6</th>\n",
       "      <th>EXT7</th>\n",
       "      <th>EXT8</th>\n",
       "      <th>EXT9</th>\n",
       "      <th>EXT10</th>\n",
       "      <th>EST1</th>\n",
       "      <th>EST2</th>\n",
       "      <th>EST3</th>\n",
       "      <th>EST4</th>\n",
       "      <th>EST5</th>\n",
       "      <th>EST6</th>\n",
       "      <th>EST7</th>\n",
       "      <th>EST8</th>\n",
       "      <th>EST9</th>\n",
       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
       "      <th>AGR2</th>\n",
       "      <th>AGR3</th>\n",
       "      <th>AGR4</th>\n",
       "      <th>AGR5</th>\n",
       "      <th>AGR6</th>\n",
       "      <th>AGR7</th>\n",
       "      <th>AGR8</th>\n",
       "      <th>AGR9</th>\n",
       "      <th>AGR10</th>\n",
       "      <th>CSN1</th>\n",
       "      <th>CSN2</th>\n",
       "      <th>CSN3</th>\n",
       "      <th>CSN4</th>\n",
       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  EST2  \\\n",
       "0  4.00  1.00  5.00  2.00  5.00  1.00  5.00  2.00  4.00   1.00  1.00  4.00   \n",
       "1  3.00  5.00  3.00  4.00  3.00  3.00  2.00  5.00  1.00   5.00  2.00  3.00   \n",
       "2  2.00  3.00  4.00  4.00  3.00  2.00  1.00  3.00  2.00   5.00  4.00  4.00   \n",
       "3  2.00  2.00  2.00  3.00  4.00  2.00  2.00  4.00  1.00   4.00  3.00  3.00   \n",
       "4  3.00  3.00  3.00  3.00  5.00  3.00  3.00  5.00  3.00   4.00  1.00  5.00   \n",
       "\n",
       "   EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  AGR3  AGR4  \\\n",
       "0  4.00  2.00  2.00  2.00  2.00  2.00  3.00   2.00  2.00  5.00  2.00  4.00   \n",
       "1  4.00  1.00  3.00  1.00  2.00  1.00  3.00   1.00  1.00  4.00  1.00  5.00   \n",
       "2  4.00  2.00  2.00  2.00  2.00  2.00  1.00   3.00  1.00  4.00  1.00  4.00   \n",
       "3  3.00  2.00  3.00  2.00  2.00  2.00  4.00   3.00  2.00  4.00  3.00  4.00   \n",
       "4  5.00  3.00  1.00  1.00  1.00  1.00  3.00   2.00  1.00  5.00  1.00  5.00   \n",
       "\n",
       "   AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  CSN4  CSN5  CSN6  \\\n",
       "0  2.00  3.00  2.00  4.00  3.00   4.00  3.00  4.00  3.00  2.00  2.00  4.00   \n",
       "1  1.00  5.00  3.00  4.00  5.00   3.00  3.00  2.00  5.00  3.00  3.00  1.00   \n",
       "2  2.00  4.00  1.00  4.00  4.00   3.00  4.00  2.00  2.00  2.00  3.00  3.00   \n",
       "3  2.00  4.00  2.00  4.00  3.00   4.00  2.00  4.00  4.00  4.00  1.00  2.00   \n",
       "4  1.00  3.00  1.00  5.00  5.00   3.00  5.00  1.00  5.00  1.00  3.00  1.00   \n",
       "\n",
       "   CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  \\\n",
       "0  4.00  2.00  4.00   4.00  5.00  1.00  4.00  1.00  4.00  1.00  5.00  3.00   \n",
       "1  3.00  3.00  5.00   3.00  1.00  2.00  4.00  2.00  3.00  1.00  4.00  2.00   \n",
       "2  4.00  2.00  4.00   2.00  5.00  1.00  2.00  1.00  4.00  2.00  5.00  3.00   \n",
       "3  2.00  3.00  1.00   4.00  4.00  2.00  5.00  2.00  3.00  1.00  4.00  4.00   \n",
       "4  5.00  1.00  5.00   5.00  5.00  1.00  5.00  1.00  5.00  1.00  5.00  3.00   \n",
       "\n",
       "   OPN9  OPN10  Clusters  \n",
       "0  4.00   5.00         4  \n",
       "1  5.00   3.00         1  \n",
       "2  4.00   4.00         1  \n",
       "3  3.00   3.00         0  \n",
       "4  5.00   5.00         4  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisando os grupos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Qual a quantidade de observações em cada grupo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    193373\n",
       "1    182408\n",
       "4    181652\n",
       "3    171229\n",
       "0    145772\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Clusters\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Agrupando os registros por grupos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
       "      <th>EXT4</th>\n",
       "      <th>EXT5</th>\n",
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       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
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       "      <th>AGR5</th>\n",
       "      <th>AGR6</th>\n",
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       "      <th>AGR8</th>\n",
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       "      <th>AGR10</th>\n",
       "      <th>CSN1</th>\n",
       "      <th>CSN2</th>\n",
       "      <th>CSN3</th>\n",
       "      <th>CSN4</th>\n",
       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.15</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.79</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.12</td>\n",
       "      <td>3.71</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.71</td>\n",
       "      <td>3.14</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.71</td>\n",
       "      <td>2.39</td>\n",
       "      <td>3.09</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.86</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3.14</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.69</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.92</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.83</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.73</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.79</td>\n",
       "      <td>2.05</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.15</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.24</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.08</td>\n",
       "      <td>4.16</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.86</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1.67</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.96</td>\n",
       "      <td>4.18</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.22</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.06</td>\n",
       "      <td>4.17</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.56</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.87</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.71</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.46</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2.49</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.82</td>\n",
       "      <td>3.67</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.82</td>\n",
       "      <td>2.95</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2.42</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.14</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.66</td>\n",
       "      <td>4.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.02</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.91</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.23</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.49</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.10</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.81</td>\n",
       "      <td>3.63</td>\n",
       "      <td>2.24</td>\n",
       "      <td>4.06</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.18</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2.45</td>\n",
       "      <td>4.53</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.39</td>\n",
       "      <td>2.58</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.54</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.83</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.82</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.57</td>\n",
       "      <td>3.05</td>\n",
       "      <td>2.78</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3.52</td>\n",
       "      <td>2.38</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.12</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.47</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.34</td>\n",
       "      <td>2.23</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.81</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.31</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.89</td>\n",
       "      <td>4.44</td>\n",
       "      <td>1.84</td>\n",
       "      <td>4.24</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.19</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.29</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.91</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4.10</td>\n",
       "      <td>1.79</td>\n",
       "      <td>4.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>4.34</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.11</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  \\\n",
       "Clusters                                                                      \n",
       "0         2.15  3.45  2.79  3.59  2.61  2.87  2.12  3.71  2.70   3.99  2.45   \n",
       "1         2.15  3.28  3.12  3.64  3.00  2.69  2.24  3.99  2.34   4.12  3.53   \n",
       "2         3.46  1.91  3.81  2.49  4.06  1.82  3.67  2.76  3.78   2.99  3.82   \n",
       "3         1.81  3.63  2.24  4.06  2.30  3.18  1.75  4.08  2.18   4.49  4.29   \n",
       "4         3.47  1.95  4.34  2.23  4.27  1.67  3.88  2.81  3.75   2.52  2.31   \n",
       "\n",
       "          EST2  EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  \\\n",
       "Clusters                                                                      \n",
       "0         3.71  3.14  2.99  2.40  2.11  2.41  2.01  2.71   2.39  3.09  2.99   \n",
       "1         3.08  4.16  2.62  2.83  2.81  2.87  2.44  2.97   2.70  1.86  3.98   \n",
       "2         2.95  4.24  2.42  3.28  3.48  3.80  3.45  3.65   3.18  2.14  4.30   \n",
       "3         2.45  4.53  2.13  3.58  3.90  4.01  3.78  4.01   3.96  2.45  3.39   \n",
       "4         3.86  3.14  3.32  2.11  1.90  2.10  1.67  2.11   1.71  1.89  4.44   \n",
       "\n",
       "          AGR3  AGR4  AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  \\\n",
       "Clusters                                                                      \n",
       "0         2.67  2.86  3.28  2.75  3.14  2.95  2.69   3.07  3.28  2.92  3.83   \n",
       "1         1.67  4.38  1.96  4.18  1.99  4.00  4.19   3.74  3.85  2.22  4.29   \n",
       "2         2.66  4.20  2.00  4.02  1.85  3.91  4.13   3.86  2.99  3.62  3.85   \n",
       "3         2.58  3.86  2.54  3.88  2.70  3.45  3.70   3.07  2.83  3.38  3.82   \n",
       "4         1.84  4.24  1.87  3.85  1.60  4.10  4.08   4.20  3.75  2.59  4.19   \n",
       "\n",
       "          CSN4  CSN5  CSN6  CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  \\\n",
       "Clusters                                                                      \n",
       "0         2.48  2.43  2.83  3.55  2.59  2.88   3.51  3.73  1.96  3.80  2.05   \n",
       "1         2.02  3.25  2.06  4.17  2.02  3.85   3.89  3.56  2.17  3.87  2.08   \n",
       "2         3.24  2.23  3.52  3.47  2.83  2.95   3.49  3.78  2.10  4.27  1.95   \n",
       "3         3.49  2.08  3.50  3.57  3.05  2.78   3.32  3.52  2.38  4.08  2.12   \n",
       "4         1.90  3.13  2.29  3.88  1.95  3.58   3.89  3.91  1.75  4.10  1.79   \n",
       "\n",
       "          OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  \n",
       "Clusters                                       \n",
       "0         3.79  2.05  4.09  3.32  3.92   3.85  \n",
       "1         3.71  2.00  4.00  2.95  4.25   3.78  \n",
       "2         3.98  1.76  4.00  3.40  4.21   4.21  \n",
       "3         3.43  2.08  3.69  3.19  4.30   3.73  \n",
       "4         4.17  1.66  4.34  3.25  4.11   4.30  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Clusters').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculando a média de cada grupo de questões para verificar um padrão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecionando as colunas de cada grupo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_list = list(data)\n",
    "ext = col_list[0:10]\n",
    "est = col_list[10:20]\n",
    "agr = col_list[20:30]\n",
    "csn = col_list[30:40]\n",
    "opn = col_list[40:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Somando os valores de cada grupo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_soma = pd.DataFrame()\n",
    "data_soma['extroversion'] = data[ext].sum(axis=1)/10\n",
    "data_soma['neurotic'] = data[est].sum(axis=1)/10\n",
    "data_soma['agreeable'] = data[agr].sum(axis=1)/10\n",
    "data_soma['conscientious'] = data[csn].sum(axis=1)/10\n",
    "data_soma['open'] = data[opn].sum(axis=1)/10\n",
    "data_soma['clusters'] = predicoes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exibindo o valor médio por grupo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>extroversion</th>\n",
       "      <th>neurotic</th>\n",
       "      <th>agreeable</th>\n",
       "      <th>conscientious</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.00</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.06</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.07</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.97</td>\n",
       "      <td>3.66</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.09</td>\n",
       "      <td>2.42</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          extroversion  neurotic  agreeable  conscientious  open\n",
       "clusters                                                        \n",
       "0                 3.00      2.63       2.95           3.03  3.25\n",
       "1                 3.06      3.00       3.20           3.16  3.24\n",
       "2                 3.07      3.43       3.31           3.22  3.37\n",
       "3                 2.97      3.66       3.16           3.18  3.25\n",
       "4                 3.09      2.42       3.21           3.11  3.34"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_soma.groupby('clusters').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizando as médias por grupo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clusters = data_soma.groupby('clusters').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22,3))\n",
    "for i in range(0, 5):\n",
    "    plt.subplot(1,5,i+1)\n",
    "    plt.bar(data_clusters.columns, data_clusters.iloc[:, i], color='green', alpha=0.2)\n",
    "    plt.plot(data_clusters.columns, data_clusters.iloc[:, i], color='red')\n",
    "    plt.title('Grupo ' + str(i))\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0,4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instalando a biblioteca gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gradio\n",
      "  Downloading gradio-1.7.7-py3-none-any.whl (1.1 MB)\n",
      "Requirement already satisfied: pillow in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from gradio) (8.0.1)\n",
      "Requirement already satisfied: Flask>=1.1.1 in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from gradio) (1.1.2)\n",
      "Collecting analytics-python\n",
      "  Downloading analytics_python-1.2.9-py2.py3-none-any.whl (13 kB)\n",
      "Collecting markdown2\n",
      "  Downloading markdown2-2.4.0-py2.py3-none-any.whl (34 kB)\n",
      "Collecting Flask-Cors>=3.0.8\n",
      "  Downloading Flask_Cors-3.0.10-py2.py3-none-any.whl (14 kB)\n",
      "Collecting Flask-Login\n",
      "  Downloading Flask_Login-0.5.0-py2.py3-none-any.whl (16 kB)\n",
      "Collecting flask-cachebuster\n",
      "  Downloading Flask-CacheBuster-1.0.0.tar.gz (3.1 kB)\n",
      "Requirement already satisfied: paramiko in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from gradio) (2.7.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from gradio) (1.5.2)\n",
      "Requirement already satisfied: pandas in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from gradio) (1.1.3)\n",
      "Collecting pycryptodome\n",
      "  Downloading pycryptodome-3.10.1-cp35-abi3-win_amd64.whl (1.6 MB)\n",
      "Collecting ffmpy\n",
      "  Downloading ffmpy-0.3.0.tar.gz (4.8 kB)\n",
      "Requirement already satisfied: requests in c:\\users\\carlos\\anaconda3\\lib\\site-packages (from gradio) (2.24.0)\n",
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      "Building wheels for collected packages: flask-cachebuster, ffmpy\n",
      "  Building wheel for flask-cachebuster (setup.py): started\n",
      "  Building wheel for flask-cachebuster (setup.py): finished with status 'done'\n",
      "  Created wheel for flask-cachebuster: filename=Flask_CacheBuster-1.0.0-py3-none-any.whl size=3376 sha256=1a4cf2c64c3a1ad4be90050e6af13a68b4500510e85feaa257574f37761ecfd5\n",
      "  Stored in directory: c:\\users\\carlos\\appdata\\local\\pip\\cache\\wheels\\27\\1b\\de\\34a3e06893944387266e2e7e93d33243589f01ac1b451e367b\n",
      "  Building wheel for ffmpy (setup.py): started\n",
      "  Building wheel for ffmpy (setup.py): finished with status 'done'\n",
      "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4714 sha256=cf8fd25053374d032fb6d3d44335d196884955832767b917d671461b2ce60696\n",
      "  Stored in directory: c:\\users\\carlos\\appdata\\local\\pip\\cache\\wheels\\ff\\5b\\59\\913b443e7369dc04b61f607a746b6f7d83fb65e2e19fcc958d\n",
      "Successfully built flask-cachebuster ffmpy\n",
      "Installing collected packages: analytics-python, markdown2, Flask-Cors, Flask-Login, flask-cachebuster, pycryptodome, ffmpy, gradio\n",
      "Successfully installed Flask-Cors-3.0.10 Flask-Login-0.5.0 analytics-python-1.2.9 ffmpy-0.3.0 flask-cachebuster-1.0.0 gradio-1.7.7 markdown2-2.4.0 pycryptodome-3.10.1\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lendo os dados com as questões"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicio_questions = open(\"questions.txt\").read().split(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EXT1\\tI am the life of the party.',\n",
       " \"EXT2\\tI don't talk a lot.\",\n",
       " 'EXT3\\tI feel comfortable around people.',\n",
       " 'EXT4\\tI keep in the background.',\n",
       " 'EXT5\\tI start conversations.',\n",
       " 'EXT6\\tI have little to say.',\n",
       " 'EXT7\\tI talk to a lot of different people at parties.',\n",
       " \"EXT8\\tI don't like to draw attention to myself.\",\n",
       " \"EXT9\\tI don't mind being the center of attention.\",\n",
       " 'EXT10\\tI am quiet around strangers.',\n",
       " 'EST1\\tI get stressed out easily.',\n",
       " 'EST2\\tI am relaxed most of the time.',\n",
       " 'EST3\\tI worry about things.',\n",
       " 'EST4\\tI seldom feel blue.',\n",
       " 'EST5\\tI am easily disturbed.',\n",
       " 'EST6\\tI get upset easily.',\n",
       " 'EST7\\tI change my mood a lot.',\n",
       " 'EST8\\tI have frequent mood swings.',\n",
       " 'EST9\\tI get irritated easily.',\n",
       " 'EST10\\tI often feel blue.',\n",
       " 'AGR1\\tI feel little concern for others.',\n",
       " 'AGR2\\tI am interested in people.',\n",
       " 'AGR3\\tI insult people.',\n",
       " \"AGR4\\tI sympathize with others' feelings.\",\n",
       " \"AGR5\\tI am not interested in other people's problems.\",\n",
       " 'AGR6\\tI have a soft heart.',\n",
       " 'AGR7\\tI am not really interested in others.',\n",
       " 'AGR8\\tI take time out for others.',\n",
       " \"AGR9\\tI feel others' emotions.\",\n",
       " 'AGR10\\tI make people feel at ease.',\n",
       " 'CSN1\\tI am always prepared.',\n",
       " 'CSN2\\tI leave my belongings around.',\n",
       " 'CSN3\\tI pay attention to details.',\n",
       " 'CSN4\\tI make a mess of things.',\n",
       " 'CSN5\\tI get chores done right away.',\n",
       " 'CSN6\\tI often forget to put things back in their proper place.',\n",
       " 'CSN7\\tI like order.',\n",
       " 'CSN8\\tI shirk my duties.',\n",
       " 'CSN9\\tI follow a schedule.',\n",
       " 'CSN10\\tI am exacting in my work.',\n",
       " 'OPN1\\tI have a rich vocabulary.',\n",
       " 'OPN2\\tI have difficulty understanding abstract ideas.',\n",
       " 'OPN3\\tI have a vivid imagination.',\n",
       " 'OPN4\\tI am not interested in abstract ideas.',\n",
       " 'OPN5\\tI have excellent ideas.',\n",
       " 'OPN6\\tI do not have a good imagination.',\n",
       " 'OPN7\\tI am quick to understand things.',\n",
       " 'OPN8\\tI use difficult words.',\n",
       " 'OPN9\\tI spend time reflecting on things.',\n",
       " 'OPN10\\tI am full of ideas.']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dicio_questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Limpando os dados e recuperando apenas as questões"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = []\n",
    "for q in dicio_questions:\n",
    "  q = str(q)\n",
    "  questions.append(q[q.find(\"\\t\"):].lstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I am the life of the party.',\n",
       " \"I don't talk a lot.\",\n",
       " 'I feel comfortable around people.',\n",
       " 'I keep in the background.',\n",
       " 'I start conversations.',\n",
       " 'I have little to say.',\n",
       " 'I talk to a lot of different people at parties.',\n",
       " \"I don't like to draw attention to myself.\",\n",
       " \"I don't mind being the center of attention.\",\n",
       " 'I am quiet around strangers.',\n",
       " 'I get stressed out easily.',\n",
       " 'I am relaxed most of the time.',\n",
       " 'I worry about things.',\n",
       " 'I seldom feel blue.',\n",
       " 'I am easily disturbed.',\n",
       " 'I get upset easily.',\n",
       " 'I change my mood a lot.',\n",
       " 'I have frequent mood swings.',\n",
       " 'I get irritated easily.',\n",
       " 'I often feel blue.',\n",
       " 'I feel little concern for others.',\n",
       " 'I am interested in people.',\n",
       " 'I insult people.',\n",
       " \"I sympathize with others' feelings.\",\n",
       " \"I am not interested in other people's problems.\",\n",
       " 'I have a soft heart.',\n",
       " 'I am not really interested in others.',\n",
       " 'I take time out for others.',\n",
       " \"I feel others' emotions.\",\n",
       " 'I make people feel at ease.',\n",
       " 'I am always prepared.',\n",
       " 'I leave my belongings around.',\n",
       " 'I pay attention to details.',\n",
       " 'I make a mess of things.',\n",
       " 'I get chores done right away.',\n",
       " 'I often forget to put things back in their proper place.',\n",
       " 'I like order.',\n",
       " 'I shirk my duties.',\n",
       " 'I follow a schedule.',\n",
       " 'I am exacting in my work.',\n",
       " 'I have a rich vocabulary.',\n",
       " 'I have difficulty understanding abstract ideas.',\n",
       " 'I have a vivid imagination.',\n",
       " 'I am not interested in abstract ideas.',\n",
       " 'I have excellent ideas.',\n",
       " 'I do not have a good imagination.',\n",
       " 'I am quick to understand things.',\n",
       " 'I use difficult words.',\n",
       " 'I spend time reflecting on things.',\n",
       " 'I am full of ideas.']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando os inputs dinamicos para passar ao gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_questions = []\n",
    "for q in questions:\n",
    "  obj_input = gr.inputs.Slider(minimum=1,maximum=5,step=1,default=3,label=q)\n",
    "  inputs_questions.append(obj_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verificando os inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<gradio.inputs.Slider at 0x218923a3430>,\n",
       " <gradio.inputs.Slider at 0x218923a3f70>,\n",
       " <gradio.inputs.Slider at 0x218923a3d60>,\n",
       " <gradio.inputs.Slider at 0x218923a31c0>,\n",
       " <gradio.inputs.Slider at 0x218923a3eb0>,\n",
       " <gradio.inputs.Slider at 0x21895a0afd0>,\n",
       " <gradio.inputs.Slider at 0x2188ec4b220>,\n",
       " <gradio.inputs.Slider at 0x218be638f70>,\n",
       " <gradio.inputs.Slider at 0x218be6537f0>,\n",
       " <gradio.inputs.Slider at 0x2188ec15d30>,\n",
       " <gradio.inputs.Slider at 0x2188ec153a0>,\n",
       " <gradio.inputs.Slider at 0x2188ec15fa0>,\n",
       " <gradio.inputs.Slider at 0x2188ec15610>,\n",
       " <gradio.inputs.Slider at 0x2189a1a7bb0>,\n",
       " <gradio.inputs.Slider at 0x2189a1a7970>,\n",
       " <gradio.inputs.Slider at 0x2188ec3c8e0>,\n",
       " <gradio.inputs.Slider at 0x2188ec3c160>,\n",
       " <gradio.inputs.Slider at 0x218bd37aaf0>,\n",
       " <gradio.inputs.Slider at 0x218bd37af70>,\n",
       " <gradio.inputs.Slider at 0x218bd37a2b0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a370>,\n",
       " <gradio.inputs.Slider at 0x218bd37a3d0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a4c0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a4f0>,\n",
       " <gradio.inputs.Slider at 0x218bd37ad00>,\n",
       " <gradio.inputs.Slider at 0x218bd37a700>,\n",
       " <gradio.inputs.Slider at 0x218bd37aee0>,\n",
       " <gradio.inputs.Slider at 0x218bd37af10>,\n",
       " <gradio.inputs.Slider at 0x218bd37aa00>,\n",
       " <gradio.inputs.Slider at 0x218bd37a2e0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a400>,\n",
       " <gradio.inputs.Slider at 0x218bd37afa0>,\n",
       " <gradio.inputs.Slider at 0x218bd37adc0>,\n",
       " <gradio.inputs.Slider at 0x218bd37ae20>,\n",
       " <gradio.inputs.Slider at 0x218bd37a490>,\n",
       " <gradio.inputs.Slider at 0x218bd37a550>,\n",
       " <gradio.inputs.Slider at 0x218bd37ac70>,\n",
       " <gradio.inputs.Slider at 0x218bd37a730>,\n",
       " <gradio.inputs.Slider at 0x218bd37a640>,\n",
       " <gradio.inputs.Slider at 0x218bd37a160>,\n",
       " <gradio.inputs.Slider at 0x218bd37a5e0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a760>,\n",
       " <gradio.inputs.Slider at 0x218bd37ad60>,\n",
       " <gradio.inputs.Slider at 0x218bd37a280>,\n",
       " <gradio.inputs.Slider at 0x218bd37a0a0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a0d0>,\n",
       " <gradio.inputs.Slider at 0x218bd37abb0>,\n",
       " <gradio.inputs.Slider at 0x218bd37a880>,\n",
       " <gradio.inputs.Slider at 0x218bd37acd0>,\n",
       " <gradio.inputs.Slider at 0x218bd37aa30>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando a interface e a função predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running locally at: http://127.0.0.1:7860/\n",
      "This share link will expire in 24 hours. If you need a permanent link, visit: https://gradio.app/introducing-hosted (NEW!)\n",
      "Running on External URL: https://10376.gradio.app\n",
      "Interface loading below...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"500\"\n",
       "            src=\"https://10376.gradio.app\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2189ea77f40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Flask 'gradio.networking'>,\n",
       " 'http://127.0.0.1:7860/',\n",
       " 'https://10376.gradio.app')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict(*outputs_questions):\n",
    "    outputs_questions = np.array(outputs_questions).reshape(1, -1)\n",
    "    return k_fit.predict(outputs_questions)\n",
    "\n",
    "iface = gr.Interface(\n",
    "                    fn = predict,\n",
    "                    title = \"Big Five Personality\",\n",
    "                    description = \"Sistema para detecção de traços de personalidade.\",\n",
    "                    inputs = inputs_questions,\n",
    "                    outputs=\"text\")\n",
    "iface.launch(share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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