Cinco_Personalidades.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b3a8fba",
   "metadata": {},
   "source": [
    "# Cinco traços de personalidades"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e5f6e978",
   "metadata": {},
   "source": [
    "-Openness to experience (inventive/curious vs. consistent/cautious)\n",
    "-Conscientiousness (efficient/organized vs. easy-going/careless)\n",
    "-Extroversion (outgoing/energetic vs. solitary/reserved)\n",
    "-Agreeableness (friendly/compassionate vs. challenging/detached)\n",
    "-Neuroticism (sensitive/nervous vs. secure/confident)\n",
    "\n",
    "Link para projeto e base de dados: https://www.kaggle.com/tunguz/big-five-personality-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db9045a4",
   "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": "code",
   "execution_count": 2,
   "id": "f6599364",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data-final.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49227ad0",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <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",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <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",
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       "      <td>6339.0</td>\n",
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       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15465f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Excluir atributos irrelevantes:\n",
    "data.drop(data.columns[50:110], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "934aed4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <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",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34f1bcb0",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"EXT1\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6548162d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>EXT6</th>\n",
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       "      <th>EST9</th>\n",
       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\n",
       "      <td>1013558.00</td>\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",
       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Análise estatística:\n",
    "pd.options.display.float_format = \"{:.2f}\".format\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a55fe248",
   "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",
       "      <th>EXT6</th>\n",
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       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
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       "      <th>OPN3</th>\n",
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       "      <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",
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       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#registros com valor 0:\n",
    "data[(data == 0.00).all(axis=1)].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ae6660b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[(data > 0.00).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c782a2e",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"EXT1\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a692b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from yellowbrick.cluster import KElbowVisualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0ca674d",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans()\n",
    "visualizer = KElbowVisualizer(kmeans, k=(2,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f889da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sample = data.sample(n=5000, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97a0a8c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Teste\n",
    "visualizer.fit(data_sample)\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4fde5506",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</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>4</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>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  4.00  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         0  \n",
       "1  5.00   3.00         1  \n",
       "2  4.00   4.00         1  \n",
       "3  3.00   3.00         4  \n",
       "4  5.00   5.00         0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Atribuindo os registros aos devidos grupos\n",
    "\n",
    "kmeans = KMeans(n_clusters=5)\n",
    "k_fit = kmeans.fit(data)\n",
    "\n",
    "#Inserindo os rótulos dos clusters no dataframe\n",
    "predicoes = k_fit.labels_\n",
    "data['Clusters'] = predicoes\n",
    "\n",
    "#Verificando os dados\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02cf19d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    193480\n",
       "0    182716\n",
       "1    181348\n",
       "3    170391\n",
       "4    146499\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Clusters\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ca8ae174",
   "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",
       "      <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",
       "    </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>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>2.32</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.89</td>\n",
       "      <td>4.45</td>\n",
       "      <td>1.84</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.59</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>4.33</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.11</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.14</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.23</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.33</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.18</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.72</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1.68</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.96</td>\n",
       "      <td>4.18</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.21</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.06</td>\n",
       "      <td>4.18</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>3.88</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.71</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.96</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.45</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.82</td>\n",
       "      <td>3.66</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.95</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2.42</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.19</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>4.14</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.63</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.22</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.94</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>4.22</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.80</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2.23</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.29</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.74</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.17</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2.46</td>\n",
       "      <td>4.52</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.57</td>\n",
       "      <td>3.89</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.46</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.59</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.56</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.57</td>\n",
       "      <td>3.05</td>\n",
       "      <td>2.77</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.52</td>\n",
       "      <td>2.38</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.17</td>\n",
       "      <td>3.44</td>\n",
       "      <td>2.81</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.71</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.42</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.01</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.09</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.92</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.89</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.73</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.05</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.85</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         3.47  1.95  4.34  2.23  4.27  1.67  3.88  2.81  3.75   2.52  2.32   \n",
       "1         2.14  3.28  3.11  3.65  2.99  2.69  2.23  4.00  2.33   4.12  3.55   \n",
       "2         3.45  1.91  3.80  2.50  4.06  1.82  3.66  2.76  3.78   3.00  3.83   \n",
       "3         1.80  3.64  2.23  4.07  2.29  3.19  1.74  4.08  2.17   4.49  4.29   \n",
       "4         2.17  3.44  2.81  3.58  2.63  2.86  2.13  3.70  2.71   3.98  2.42   \n",
       "\n",
       "          EST2  EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  \\\n",
       "Clusters                                                                      \n",
       "0         3.85  3.15  3.32  2.12  1.91  2.11  1.67  2.12   1.72  1.89  4.45   \n",
       "1         3.07  4.18  2.61  2.84  2.83  2.89  2.45  2.99   2.72  1.85  3.98   \n",
       "2         2.95  4.25  2.42  3.28  3.48  3.81  3.46  3.65   3.19  2.14  4.30   \n",
       "3         2.46  4.52  2.13  3.57  3.89  4.00  3.78  4.01   3.96  2.46  3.37   \n",
       "4         3.73  3.12  3.00  2.39  2.09  2.39  2.00  2.69   2.37  3.08  3.01   \n",
       "\n",
       "          AGR3  AGR4  AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  \\\n",
       "Clusters                                                                      \n",
       "0         1.84  4.25  1.87  3.86  1.60  4.10  4.09   4.20  3.75  2.59  4.20   \n",
       "1         1.68  4.38  1.96  4.18  1.99  4.01  4.19   3.73  3.85  2.21  4.30   \n",
       "2         2.66  4.20  2.00  4.02  1.85  3.91  4.14   3.86  2.98  3.63  3.85   \n",
       "3         2.59  3.84  2.56  3.86  2.72  3.44  3.67   3.06  2.82  3.38  3.81   \n",
       "4         2.66  2.88  3.26  2.76  3.12  2.96  2.70   3.09  3.28  2.92  3.83   \n",
       "\n",
       "          CSN4  CSN5  CSN6  CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  \\\n",
       "Clusters                                                                      \n",
       "0         1.90  3.13  2.28  3.88  1.95  3.59   3.89  3.91  1.75  4.10  1.79   \n",
       "1         2.02  3.25  2.06  4.18  2.02  3.85   3.89  3.56  2.17  3.88  2.08   \n",
       "2         3.25  2.22  3.53  3.47  2.84  2.94   3.49  3.78  2.10  4.27  1.95   \n",
       "3         3.49  2.08  3.51  3.57  3.05  2.77   3.31  3.52  2.38  4.07  2.13   \n",
       "4         2.47  2.44  2.82  3.55  2.58  2.89   3.51  3.73  1.95  3.80  2.05   \n",
       "\n",
       "          OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  \n",
       "Clusters                                       \n",
       "0         4.17  1.66  4.33  3.25  4.11   4.29  \n",
       "1         3.71  2.00  4.00  2.96  4.26   3.78  \n",
       "2         3.98  1.76  4.00  3.40  4.22   4.21  \n",
       "3         3.43  2.09  3.69  3.19  4.29   3.72  \n",
       "4         3.80  2.05  4.09  3.31  3.92   3.85  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Clusters').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b518dca",
   "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>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>3.09</td>\n",
       "      <td>2.43</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.05</td>\n",
       "      <td>3.01</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>3.00</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          extroversion  neurotic  agreeable  conscientious  open\n",
       "clusters                                                        \n",
       "0                 3.09      2.43       3.21           3.11  3.34\n",
       "1                 3.05      3.01       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                 3.00      2.62       2.95           3.03  3.25"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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]\n",
    "\n",
    "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\n",
    "\n",
    "data_soma.groupby('clusters').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "187ead59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2200x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_clusters = data_soma.groupby('clusters').mean()\n",
    "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": "code",
   "execution_count": null,
   "id": "92dd2111",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}