Personality_system_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AGR6_E</th>\n",
       "      <th>AGR7_E</th>\n",
       "      <th>AGR8_E</th>\n",
       "      <th>AGR9_E</th>\n",
       "      <th>AGR10_E</th>\n",
       "      <th>CSN1_E</th>\n",
       "      <th>CSN2_E</th>\n",
       "      <th>CSN3_E</th>\n",
       "      <th>CSN4_E</th>\n",
       "      <th>CSN5_E</th>\n",
       "      <th>CSN6_E</th>\n",
       "      <th>CSN7_E</th>\n",
       "      <th>CSN8_E</th>\n",
       "      <th>CSN9_E</th>\n",
       "      <th>CSN10_E</th>\n",
       "      <th>OPN1_E</th>\n",
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       "      <th>dateload</th>\n",
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       "      <th>endelapse</th>\n",
       "      <th>IPC</th>\n",
       "      <th>country</th>\n",
       "      <th>lat_appx_lots_of_err</th>\n",
       "      <th>long_appx_lots_of_err</th>\n",
       "    </tr>\n",
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       "  <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",
       "      <td>9419.0</td>\n",
       "      <td>5491.0</td>\n",
       "      <td>3959.0</td>\n",
       "      <td>4821.0</td>\n",
       "      <td>5611.0</td>\n",
       "      <td>2756.0</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>4110.0</td>\n",
       "      <td>6135.0</td>\n",
       "      <td>4150.0</td>\n",
       "      <td>5739.0</td>\n",
       "      <td>6364.0</td>\n",
       "      <td>3663.0</td>\n",
       "      <td>5070.0</td>\n",
       "      <td>5709.0</td>\n",
       "      <td>4285.0</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>11641.0</td>\n",
       "      <td>3115.0</td>\n",
       "      <td>3207.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>10235.0</td>\n",
       "      <td>5897.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>6602.0</td>\n",
       "      <td>5457.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>3762.0</td>\n",
       "      <td>4420.0</td>\n",
       "      <td>9382.0</td>\n",
       "      <td>5286.0</td>\n",
       "      <td>4983.0</td>\n",
       "      <td>6339.0</td>\n",
       "      <td>3146.0</td>\n",
       "      <td>4067.0</td>\n",
       "      <td>2959.0</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>4920.0</td>\n",
       "      <td>4436.0</td>\n",
       "      <td>3116.0</td>\n",
       "      <td>2992.0</td>\n",
       "      <td>4354.0</td>\n",
       "      <td>2016-03-03 02:01:01</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>51.5448</td>\n",
       "      <td>0.1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7235.0</td>\n",
       "      <td>3598.0</td>\n",
       "      <td>3315.0</td>\n",
       "      <td>2564.0</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>4787.0</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>9036.0</td>\n",
       "      <td>2406.0</td>\n",
       "      <td>3484.0</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>3061.0</td>\n",
       "      <td>2539.0</td>\n",
       "      <td>4226.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>1607.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>3422.0</td>\n",
       "      <td>5324.0</td>\n",
       "      <td>4494.0</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>1747.0</td>\n",
       "      <td>5163.0</td>\n",
       "      <td>5240.0</td>\n",
       "      <td>7208.0</td>\n",
       "      <td>2783.0</td>\n",
       "      <td>4103.0</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>3347.0</td>\n",
       "      <td>2399.0</td>\n",
       "      <td>3360.0</td>\n",
       "      <td>5595.0</td>\n",
       "      <td>2624.0</td>\n",
       "      <td>4985.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>3026.0</td>\n",
       "      <td>4742.0</td>\n",
       "      <td>3336.0</td>\n",
       "      <td>2718.0</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>3096.0</td>\n",
       "      <td>3019.0</td>\n",
       "      <td>2016-03-03 02:01:20</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>MY</td>\n",
       "      <td>3.1698</td>\n",
       "      <td>101.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4657.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>5847.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>4922.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>14621.0</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>5128.0</td>\n",
       "      <td>3675.0</td>\n",
       "      <td>3442.0</td>\n",
       "      <td>4546.0</td>\n",
       "      <td>8275.0</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>3813.0</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>3386.0</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2562.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>13719.0</td>\n",
       "      <td>3892.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>4286.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>2713.0</td>\n",
       "      <td>2813.0</td>\n",
       "      <td>4237.0</td>\n",
       "      <td>6308.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>2983.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1644.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>8114.0</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>6295.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>2529.0</td>\n",
       "      <td>2016-03-03 02:01:56</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>54.9119</td>\n",
       "      <td>-1.3833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3996.0</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>5096.0</td>\n",
       "      <td>4240.0</td>\n",
       "      <td>5168.0</td>\n",
       "      <td>5456.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>5240.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3736.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>3015.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>4064.0</td>\n",
       "      <td>4208.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>7336.0</td>\n",
       "      <td>3896.0</td>\n",
       "      <td>6062.0</td>\n",
       "      <td>11952.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>3664.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>4912.0</td>\n",
       "      <td>7545.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>6896.0</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2368.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>2848.0</td>\n",
       "      <td>6264.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>10472.0</td>\n",
       "      <td>3192.0</td>\n",
       "      <td>7704.0</td>\n",
       "      <td>3456.0</td>\n",
       "      <td>6665.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>3728.0</td>\n",
       "      <td>4128.0</td>\n",
       "      <td>3776.0</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>4192.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>3257.0</td>\n",
       "      <td>2016-03-03 02:02:02</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>51.75</td>\n",
       "      <td>-1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6004.0</td>\n",
       "      <td>3965.0</td>\n",
       "      <td>2721.0</td>\n",
       "      <td>3706.0</td>\n",
       "      <td>2968.0</td>\n",
       "      <td>2426.0</td>\n",
       "      <td>7339.0</td>\n",
       "      <td>3302.0</td>\n",
       "      <td>16819.0</td>\n",
       "      <td>3731.0</td>\n",
       "      <td>4740.0</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>7461.0</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>3324.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>4308.0</td>\n",
       "      <td>4506.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>3115.0</td>\n",
       "      <td>6771.0</td>\n",
       "      <td>2819.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>16204.0</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>28983.0</td>\n",
       "      <td>1612.0</td>\n",
       "      <td>2437.0</td>\n",
       "      <td>4532.0</td>\n",
       "      <td>3843.0</td>\n",
       "      <td>7019.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>6550.0</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>21500.0</td>\n",
       "      <td>20587.0</td>\n",
       "      <td>8458.0</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>17042.0</td>\n",
       "      <td>7029.0</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>5835.0</td>\n",
       "      <td>6846.0</td>\n",
       "      <td>5320.0</td>\n",
       "      <td>11401.0</td>\n",
       "      <td>8642.0</td>\n",
       "      <td>2016-03-03 02:02:57</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>KE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015336</th>\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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</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>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</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>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5694.0</td>\n",
       "      <td>3927.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>5255.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>2216.0</td>\n",
       "      <td>5064.0</td>\n",
       "      <td>3288.0</td>\n",
       "      <td>4207.0</td>\n",
       "      <td>3807.0</td>\n",
       "      <td>3607.0</td>\n",
       "      <td>3256.0</td>\n",
       "      <td>8575.0</td>\n",
       "      <td>1696.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>5488.0</td>\n",
       "      <td>2456.0</td>\n",
       "      <td>3487.0</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>3151.0</td>\n",
       "      <td>2576.0</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>2815.0</td>\n",
       "      <td>2964.0</td>\n",
       "      <td>2665.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>3008.0</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>2544.0</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>4784.0</td>\n",
       "      <td>3529.0</td>\n",
       "      <td>5072.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>2649.0</td>\n",
       "      <td>3544.0</td>\n",
       "      <td>7577.0</td>\n",
       "      <td>3096.0</td>\n",
       "      <td>1896.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>2018-11-08 12:04:58</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>39.9883</td>\n",
       "      <td>-75.2208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015337</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</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>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>3.0</td>\n",
       "      <td>2.0</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>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>3696.0</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>2272.0</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>2296.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>3287.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>1768.0</td>\n",
       "      <td>4503.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>2422.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>3216.0</td>\n",
       "      <td>6160.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>1513.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>3833.0</td>\n",
       "      <td>3280.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>3209.0</td>\n",
       "      <td>1744.0</td>\n",
       "      <td>4392.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>2263.0</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>6632.0</td>\n",
       "      <td>2312.0</td>\n",
       "      <td>2376.0</td>\n",
       "      <td>2969.0</td>\n",
       "      <td>2271.0</td>\n",
       "      <td>4064.0</td>\n",
       "      <td>1144.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>2018-11-08 12:07:18</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015338</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</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>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</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>5.0</td>\n",
       "      <td>4.0</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>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4077.0</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>5329.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>2992.0</td>\n",
       "      <td>2952.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>2305.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>3096.0</td>\n",
       "      <td>3206.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>2482.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>2487.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>4550.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>5873.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>5286.0</td>\n",
       "      <td>2232.0</td>\n",
       "      <td>2942.0</td>\n",
       "      <td>2296.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>4719.0</td>\n",
       "      <td>2121.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>2335.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>3007.0</td>\n",
       "      <td>2727.0</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>2646.0</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>2018-11-08 12:07:49</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>US</td>\n",
       "      <td>36.1473</td>\n",
       "      <td>-86.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015339</th>\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>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</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>4.0</td>\n",
       "      <td>4.0</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>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>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>2.0</td>\n",
       "      <td>2.0</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>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>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8647.0</td>\n",
       "      <td>2664.0</td>\n",
       "      <td>3544.0</td>\n",
       "      <td>2263.0</td>\n",
       "      <td>2248.0</td>\n",
       "      <td>3024.0</td>\n",
       "      <td>4704.0</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>2488.0</td>\n",
       "      <td>3183.0</td>\n",
       "      <td>2423.0</td>\n",
       "      <td>5807.0</td>\n",
       "      <td>1384.0</td>\n",
       "      <td>2696.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>4655.0</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>5064.0</td>\n",
       "      <td>4921.0</td>\n",
       "      <td>2982.0</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>2168.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>3055.0</td>\n",
       "      <td>2580.0</td>\n",
       "      <td>2816.0</td>\n",
       "      <td>2544.0</td>\n",
       "      <td>3744.0</td>\n",
       "      <td>5168.0</td>\n",
       "      <td>3903.0</td>\n",
       "      <td>37726.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>5056.0</td>\n",
       "      <td>3216.0</td>\n",
       "      <td>3320.0</td>\n",
       "      <td>2263.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>5024.0</td>\n",
       "      <td>4664.0</td>\n",
       "      <td>4792.0</td>\n",
       "      <td>6471.0</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>3129.0</td>\n",
       "      <td>2799.0</td>\n",
       "      <td>7184.0</td>\n",
       "      <td>2526.0</td>\n",
       "      <td>2018-11-08 12:08:34</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>34.1067</td>\n",
       "      <td>-117.8067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015340</th>\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>1.0</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>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</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>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4425.0</td>\n",
       "      <td>2472.0</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>2571.0</td>\n",
       "      <td>984.0</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>4771.0</td>\n",
       "      <td>3255.0</td>\n",
       "      <td>2918.0</td>\n",
       "      <td>2386.0</td>\n",
       "      <td>2269.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>3037.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>1719.0</td>\n",
       "      <td>4572.0</td>\n",
       "      <td>3087.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>6502.0</td>\n",
       "      <td>3751.0</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>6857.0</td>\n",
       "      <td>6608.0</td>\n",
       "      <td>3870.0</td>\n",
       "      <td>8258.0</td>\n",
       "      <td>3037.0</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>3853.0</td>\n",
       "      <td>5106.0</td>\n",
       "      <td>3087.0</td>\n",
       "      <td>3988.0</td>\n",
       "      <td>2469.0</td>\n",
       "      <td>2919.0</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>2252.0</td>\n",
       "      <td>4539.0</td>\n",
       "      <td>4539.0</td>\n",
       "      <td>5039.0</td>\n",
       "      <td>2493.0</td>\n",
       "      <td>2302.0</td>\n",
       "      <td>5072.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>7174.0</td>\n",
       "      <td>4037.0</td>\n",
       "      <td>3422.0</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>2018-11-08 12:08:45</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1015341 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  \\\n",
       "0         4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0   1.0   \n",
       "1         3.0   5.0   3.0   4.0   3.0   3.0   2.0   5.0   1.0    5.0   2.0   \n",
       "2         2.0   3.0   4.0   4.0   3.0   2.0   1.0   3.0   2.0    5.0   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   \n",
       "4         3.0   3.0   3.0   3.0   5.0   3.0   3.0   5.0   3.0    4.0   1.0   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...    ...   ...   \n",
       "1015336   4.0   2.0   4.0   3.0   4.0   3.0   3.0   3.0   3.0    3.0   4.0   \n",
       "1015337   4.0   3.0   4.0   3.0   3.0   3.0   4.0   4.0   3.0    3.0   4.0   \n",
       "1015338   4.0   2.0   4.0   3.0   5.0   1.0   4.0   2.0   4.0    4.0   3.0   \n",
       "1015339   2.0   4.0   3.0   4.0   2.0   2.0   1.0   4.0   2.0    4.0   4.0   \n",
       "1015340   4.0   2.0   4.0   2.0   4.0   1.0   4.0   2.0   4.0    4.0   4.0   \n",
       "\n",
       "         EST2  EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  \\\n",
       "0         4.0   4.0   2.0   2.0   2.0   2.0   2.0   3.0    2.0   2.0   5.0   \n",
       "1         3.0   4.0   1.0   3.0   1.0   2.0   1.0   3.0    1.0   1.0   4.0   \n",
       "2         4.0   4.0   2.0   2.0   2.0   2.0   2.0   1.0    3.0   1.0   4.0   \n",
       "3         3.0   3.0   2.0   3.0   2.0   2.0   2.0   4.0    3.0   2.0   4.0   \n",
       "4         5.0   5.0   3.0   1.0   1.0   1.0   1.0   3.0    2.0   1.0   5.0   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...    ...   ...   ...   \n",
       "1015336   3.0   3.0   3.0   4.0   3.0   4.0   3.0   3.0    3.0   5.0   4.0   \n",
       "1015337   3.0   5.0   1.0   5.0   5.0   4.0   4.0   4.0    5.0   2.0   4.0   \n",
       "1015338   2.0   4.0   3.0   2.0   2.0   4.0   2.0   4.0    1.0   3.0   5.0   \n",
       "1015339   3.0   4.0   2.0   4.0   4.0   2.0   2.0   4.0    4.0   2.0   3.0   \n",
       "1015340   3.0   4.0   3.0   2.0   3.0   3.0   1.0   4.0    2.0   1.0   5.0   \n",
       "\n",
       "         AGR3  AGR4  AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  \\\n",
       "0         2.0   4.0   2.0   3.0   2.0   4.0   3.0    4.0   3.0   4.0   3.0   \n",
       "1         1.0   5.0   1.0   5.0   3.0   4.0   5.0    3.0   3.0   2.0   5.0   \n",
       "2         1.0   4.0   2.0   4.0   1.0   4.0   4.0    3.0   4.0   2.0   2.0   \n",
       "3         3.0   4.0   2.0   4.0   2.0   4.0   3.0    4.0   2.0   4.0   4.0   \n",
       "4         1.0   5.0   1.0   3.0   1.0   5.0   5.0    3.0   5.0   1.0   5.0   \n",
       "...       ...   ...   ...   ...   ...   ...   ...    ...   ...   ...   ...   \n",
       "1015336   2.0   5.0   2.0   4.0   2.0   4.0   4.0    4.0   2.0   3.0   3.0   \n",
       "1015337   1.0   4.0   3.0   5.0   3.0   3.0   5.0    3.0   3.0   2.0   3.0   \n",
       "1015338   5.0   3.0   2.0   3.0   2.0   4.0   3.0    5.0   4.0   5.0   3.0   \n",
       "1015339   2.0   4.0   3.0   4.0   2.0   4.0   4.0    3.0   4.0   2.0   4.0   \n",
       "1015340   2.0   4.0   3.0   5.0   2.0   3.0   4.0    5.0   3.0   4.0   3.0   \n",
       "\n",
       "         CSN4  CSN5  CSN6  CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  \\\n",
       "0         2.0   2.0   4.0   4.0   2.0   4.0    4.0   5.0   1.0   4.0   1.0   \n",
       "1         3.0   3.0   1.0   3.0   3.0   5.0    3.0   1.0   2.0   4.0   2.0   \n",
       "2         2.0   3.0   3.0   4.0   2.0   4.0    2.0   5.0   1.0   2.0   1.0   \n",
       "3         4.0   1.0   2.0   2.0   3.0   1.0    4.0   4.0   2.0   5.0   2.0   \n",
       "4         1.0   3.0   1.0   5.0   1.0   5.0    5.0   5.0   1.0   5.0   1.0   \n",
       "...       ...   ...   ...   ...   ...   ...    ...   ...   ...   ...   ...   \n",
       "1015336   3.0   4.0   4.0   4.0   2.0   3.0    3.0   2.0   2.0   4.0   3.0   \n",
       "1015337   4.0   3.0   3.0   2.0   3.0   2.0    3.0   4.0   1.0   5.0   1.0   \n",
       "1015338   5.0   1.0   5.0   1.0   4.0   1.0    4.0   5.0   1.0   5.0   1.0   \n",
       "1015339   2.0   2.0   2.0   4.0   2.0   4.0    4.0   5.0   2.0   4.0   2.0   \n",
       "1015340   3.0   2.0   3.0   3.0   2.0   2.0    3.0   5.0   1.0   5.0   1.0   \n",
       "\n",
       "         OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  EXT1_E  EXT2_E  EXT3_E  EXT4_E  \\\n",
       "0         4.0   1.0   5.0   3.0   4.0    5.0  9419.0  5491.0  3959.0  4821.0   \n",
       "1         3.0   1.0   4.0   2.0   5.0    3.0  7235.0  3598.0  3315.0  2564.0   \n",
       "2         4.0   2.0   5.0   3.0   4.0    4.0  4657.0  3549.0  2543.0  3335.0   \n",
       "3         3.0   1.0   4.0   4.0   3.0    3.0  3996.0  2896.0  5096.0  4240.0   \n",
       "4         5.0   1.0   5.0   3.0   5.0    5.0  6004.0  3965.0  2721.0  3706.0   \n",
       "...       ...   ...   ...   ...   ...    ...     ...     ...     ...     ...   \n",
       "1015336   4.0   2.0   4.0   2.0   2.0    4.0  5694.0  3927.0  4600.0  5255.0   \n",
       "1015337   5.0   1.0   3.0   4.0   5.0    4.0  2382.0  1984.0  3696.0  1736.0   \n",
       "1015338   4.0   1.0   5.0   5.0   4.0    5.0  4077.0  2120.0  1688.0  2528.0   \n",
       "1015339   3.0   2.0   4.0   5.0   5.0    3.0  8647.0  2664.0  3544.0  2263.0   \n",
       "1015340   3.0   1.0   5.0   4.0   5.0    5.0  4425.0  2472.0  3163.0  2571.0   \n",
       "\n",
       "         EXT5_E  EXT6_E  EXT7_E  EXT8_E   EXT9_E  EXT10_E  EST1_E  EST2_E  \\\n",
       "0        5611.0  2756.0  2388.0  2113.0   5900.0   4110.0  6135.0  4150.0   \n",
       "1        2976.0  3050.0  4787.0  3228.0   3465.0   3309.0  9036.0  2406.0   \n",
       "2        5847.0  2540.0  4922.0  3142.0  14621.0   2191.0  5128.0  3675.0   \n",
       "3        5168.0  5456.0  4360.0  4496.0   5240.0   4000.0  3736.0  4616.0   \n",
       "4        2968.0  2426.0  7339.0  3302.0  16819.0   3731.0  4740.0  2856.0   \n",
       "...         ...     ...     ...     ...      ...      ...     ...     ...   \n",
       "1015336  1688.0  2216.0  5064.0  3288.0   4207.0   3807.0  3607.0  3256.0   \n",
       "1015337  2272.0  2327.0  2088.0  2296.0   1585.0   3287.0  1752.0  1728.0   \n",
       "1015338  1721.0  2711.0  5329.0  2080.0   2992.0   2952.0  1967.0  2305.0   \n",
       "1015339  2248.0  3024.0  4704.0  3383.0   2488.0   3183.0  2423.0  5807.0   \n",
       "1015340   984.0  2853.0  2987.0  4771.0   3255.0   2918.0  2386.0  2269.0   \n",
       "\n",
       "         EST3_E  EST4_E  EST5_E  EST6_E  EST7_E  EST8_E  EST9_E  EST10_E  \\\n",
       "0        5739.0  6364.0  3663.0  5070.0  5709.0  4285.0  2587.0   3997.0   \n",
       "1        3484.0  3359.0  3061.0  2539.0  4226.0  2962.0  1799.0   1607.0   \n",
       "2        3442.0  4546.0  8275.0  2185.0  2164.0  1175.0  3813.0   1593.0   \n",
       "3        3015.0  2711.0  3960.0  4064.0  4208.0  2936.0  7336.0   3896.0   \n",
       "4        7461.0  2179.0  3324.0  2255.0  4308.0  4506.0  3127.0   3115.0   \n",
       "...         ...     ...     ...     ...     ...     ...     ...      ...   \n",
       "1015336  8575.0  1696.0  2016.0  5488.0  2456.0  3487.0  1655.0   2119.0   \n",
       "1015337  1279.0  1768.0  4503.0  1329.0  1640.0  1464.0  2183.0    953.0   \n",
       "1015338  1752.0  3096.0  3206.0  2825.0  2482.0  2358.0  1946.0   2416.0   \n",
       "1015339  1384.0  2696.0  2800.0  1847.0  4655.0  1232.0  5064.0   4921.0   \n",
       "1015340  1952.0  3037.0  2370.0  2370.0  1719.0  4572.0  3087.0   2352.0   \n",
       "\n",
       "         AGR1_E   AGR2_E   AGR3_E  AGR4_E   AGR5_E  AGR6_E   AGR7_E   AGR8_E  \\\n",
       "0        4750.0   5475.0  11641.0  3115.0   3207.0  3260.0  10235.0   5897.0   \n",
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       "4        6771.0   2819.0   3682.0  2511.0  16204.0  1736.0  28983.0   1612.0   \n",
       "...         ...      ...      ...     ...      ...     ...      ...      ...   \n",
       "1015336  1655.0   1937.0   1233.0  3151.0   2576.0  1888.0   2815.0   2964.0   \n",
       "1015337  2422.0   1448.0   3216.0  6160.0   2208.0  1513.0   2785.0   3833.0   \n",
       "1015338  2487.0   1863.0   1745.0  4040.0   4068.0  1480.0   4550.0   3000.0   \n",
       "1015339  2982.0   5584.0   2567.0  2168.0   6320.0  3055.0   2580.0   2816.0   \n",
       "1015340  6502.0   3751.0   3136.0  6857.0   6608.0  3870.0   8258.0   3037.0   \n",
       "\n",
       "         AGR9_E  AGR10_E  CSN1_E  CSN2_E   CSN3_E  CSN4_E  CSN5_E  CSN6_E  \\\n",
       "0        1758.0   3081.0  6602.0  5457.0   1569.0  2129.0  3762.0  4420.0   \n",
       "1        1850.0   1747.0  5163.0  5240.0   7208.0  2783.0  4103.0  3431.0   \n",
       "2        3892.0   4100.0  4286.0  4775.0   2713.0  2813.0  4237.0  6308.0   \n",
       "3        4632.0   6896.0  2824.0   520.0   2368.0  3225.0  2848.0  6264.0   \n",
       "4        2437.0   4532.0  3843.0  7019.0   3102.0  3153.0  2869.0  6550.0   \n",
       "...         ...      ...     ...     ...      ...     ...     ...     ...   \n",
       "1015336  2665.0   2888.0  3008.0  2367.0   2504.0  2544.0  2144.0  4784.0   \n",
       "1015337  3280.0   1184.0  2096.0  1880.0   3209.0  1744.0  4392.0  1943.0   \n",
       "1015338  5873.0   2088.0  5286.0  2232.0   2942.0  2296.0  1841.0  2303.0   \n",
       "1015339  2544.0   3744.0  5168.0  3903.0  37726.0  2735.0  1367.0  5056.0   \n",
       "1015340  2251.0   3853.0  5106.0  3087.0   3988.0  2469.0  2919.0  3020.0   \n",
       "\n",
       "         CSN7_E   CSN8_E   CSN9_E  CSN10_E  OPN1_E  OPN2_E   OPN3_E  OPN4_E  \\\n",
       "0        9382.0   5286.0   4983.0   6339.0  3146.0  4067.0   2959.0  3411.0   \n",
       "1        3347.0   2399.0   3360.0   5595.0  2624.0  4985.0   1684.0  3026.0   \n",
       "2        2690.0   1516.0   2379.0   2983.0  1930.0  1470.0   1644.0  1683.0   \n",
       "3        3760.0  10472.0   3192.0   7704.0  3456.0  6665.0   1977.0  3728.0   \n",
       "4        1811.0   3682.0  21500.0  20587.0  8458.0  3510.0  17042.0  7029.0   \n",
       "...         ...      ...      ...      ...     ...     ...      ...     ...   \n",
       "1015336  3529.0   5072.0   2016.0   3353.0  2649.0  3544.0   7577.0  3096.0   \n",
       "1015337  2263.0   1559.0   1304.0   2176.0  2560.0  6632.0   2312.0  2376.0   \n",
       "1015338  1791.0   2744.0   1196.0   4719.0  2121.0  2807.0   1711.0  2335.0   \n",
       "1015339  3216.0   3320.0   2263.0   1415.0  5024.0  4664.0   4792.0  6471.0   \n",
       "1015340  3104.0   2252.0   4539.0   4539.0  5039.0  2493.0   2302.0  5072.0   \n",
       "\n",
       "         OPN5_E  OPN6_E  OPN7_E  OPN8_E   OPN9_E  OPN10_E  \\\n",
       "0        2170.0  4920.0  4436.0  3116.0   2992.0   4354.0   \n",
       "1        4742.0  3336.0  2718.0  3374.0   3096.0   3019.0   \n",
       "2        2229.0  8114.0  2043.0  6295.0   1585.0   2529.0   \n",
       "3        4128.0  3776.0  2984.0  4192.0   3480.0   3257.0   \n",
       "4        2327.0  5835.0  6846.0  5320.0  11401.0   8642.0   \n",
       "...         ...     ...     ...     ...      ...      ...   \n",
       "1015336  1896.0  3912.0  2744.0  2025.0   1873.0   1232.0   \n",
       "1015337  2969.0  2271.0  4064.0  1144.0   2936.0   1615.0   \n",
       "1015338  1609.0  3007.0  2727.0  2648.0   2646.0   1287.0   \n",
       "1015339  1873.0  3136.0  3129.0  2799.0   7184.0   2526.0   \n",
       "1015340  1752.0  1935.0  7174.0  4037.0   3422.0   1769.0   \n",
       "\n",
       "                    dateload  screenw  screenh  introelapse  testelapse  \\\n",
       "0        2016-03-03 02:01:01    768.0   1024.0          9.0       234.0   \n",
       "1        2016-03-03 02:01:20   1360.0    768.0         12.0       179.0   \n",
       "2        2016-03-03 02:01:56   1366.0    768.0          3.0       186.0   \n",
       "3        2016-03-03 02:02:02   1920.0   1200.0        186.0       219.0   \n",
       "4        2016-03-03 02:02:57   1366.0    768.0          8.0       315.0   \n",
       "...                      ...      ...      ...          ...         ...   \n",
       "1015336  2018-11-08 12:04:58   1920.0   1080.0          3.0       160.0   \n",
       "1015337  2018-11-08 12:07:18   1920.0   1080.0          3.0       122.0   \n",
       "1015338  2018-11-08 12:07:49   1920.0   1080.0          2.0       135.0   \n",
       "1015339  2018-11-08 12:08:34   1920.0   1080.0          6.0       212.0   \n",
       "1015340  2018-11-08 12:08:45   1920.0   1080.0          3.0       176.0   \n",
       "\n",
       "         endelapse  IPC country lat_appx_lots_of_err long_appx_lots_of_err  \n",
       "0                6    1      GB              51.5448                0.1991  \n",
       "1               11    1      MY               3.1698               101.706  \n",
       "2                7    1      GB              54.9119               -1.3833  \n",
       "3                7    1      GB                51.75                 -1.25  \n",
       "4               17    2      KE                  1.0                  38.0  \n",
       "...            ...  ...     ...                  ...                   ...  \n",
       "1015336         10    2      US              39.9883              -75.2208  \n",
       "1015337          7    1      US                 38.0                 -97.0  \n",
       "1015338         12    6      US              36.1473               -86.777  \n",
       "1015339          8    1      US              34.1067             -117.8067  \n",
       "1015340          9    1      US                 38.0                 -97.0  \n",
       "\n",
       "[1015341 rows x 110 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"D:\\GeneralDocs\\WWC_Mentorship_program\\data-final.csv\\data-final.csv\",delimiter='\\t')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXT1\n",
      "EXT2\n",
      "EXT3\n",
      "EXT4\n",
      "EXT5\n",
      "EXT6\n",
      "EXT7\n",
      "EXT8\n",
      "EXT9\n",
      "EXT10\n",
      "EST1\n",
      "EST2\n",
      "EST3\n",
      "EST4\n",
      "EST5\n",
      "EST6\n",
      "EST7\n",
      "EST8\n",
      "EST9\n",
      "EST10\n",
      "AGR1\n",
      "AGR2\n",
      "AGR3\n",
      "AGR4\n",
      "AGR5\n",
      "AGR6\n",
      "AGR7\n",
      "AGR8\n",
      "AGR9\n",
      "AGR10\n",
      "CSN1\n",
      "CSN2\n",
      "CSN3\n",
      "CSN4\n",
      "CSN5\n",
      "CSN6\n",
      "CSN7\n",
      "CSN8\n",
      "CSN9\n",
      "CSN10\n",
      "OPN1\n",
      "OPN2\n",
      "OPN3\n",
      "OPN4\n",
      "OPN5\n",
      "OPN6\n",
      "OPN7\n",
      "OPN8\n",
      "OPN9\n",
      "OPN10\n",
      "EXT1_E\n",
      "EXT2_E\n",
      "EXT3_E\n",
      "EXT4_E\n",
      "EXT5_E\n",
      "EXT6_E\n",
      "EXT7_E\n",
      "EXT8_E\n",
      "EXT9_E\n",
      "EXT10_E\n",
      "EST1_E\n",
      "EST2_E\n",
      "EST3_E\n",
      "EST4_E\n",
      "EST5_E\n",
      "EST6_E\n",
      "EST7_E\n",
      "EST8_E\n",
      "EST9_E\n",
      "EST10_E\n",
      "AGR1_E\n",
      "AGR2_E\n",
      "AGR3_E\n",
      "AGR4_E\n",
      "AGR5_E\n",
      "AGR6_E\n",
      "AGR7_E\n",
      "AGR8_E\n",
      "AGR9_E\n",
      "AGR10_E\n",
      "CSN1_E\n",
      "CSN2_E\n",
      "CSN3_E\n",
      "CSN4_E\n",
      "CSN5_E\n",
      "CSN6_E\n",
      "CSN7_E\n",
      "CSN8_E\n",
      "CSN9_E\n",
      "CSN10_E\n",
      "OPN1_E\n",
      "OPN2_E\n",
      "OPN3_E\n",
      "OPN4_E\n",
      "OPN5_E\n",
      "OPN6_E\n",
      "OPN7_E\n",
      "OPN8_E\n",
      "OPN9_E\n",
      "OPN10_E\n",
      "dateload\n",
      "screenw\n",
      "screenh\n",
      "introelapse\n",
      "testelapse\n",
      "endelapse\n",
      "IPC\n",
      "country\n",
      "lat_appx_lots_of_err\n",
      "long_appx_lots_of_err\n"
     ]
    }
   ],
   "source": [
    "for column in columns:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[df.columns[0:50]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
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       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
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       "      <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",
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       "      <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015336</th>\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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</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>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</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>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015337</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</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>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>3.0</td>\n",
       "      <td>2.0</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>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</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>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015338</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</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>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</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>5.0</td>\n",
       "      <td>4.0</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>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015339</th>\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>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</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>4.0</td>\n",
       "      <td>4.0</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>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>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>2.0</td>\n",
       "      <td>2.0</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>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>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015340</th>\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>1.0</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>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</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>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1015341 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  \\\n",
       "0         4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0   1.0   \n",
       "1         3.0   5.0   3.0   4.0   3.0   3.0   2.0   5.0   1.0    5.0   2.0   \n",
       "2         2.0   3.0   4.0   4.0   3.0   2.0   1.0   3.0   2.0    5.0   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   \n",
       "4         3.0   3.0   3.0   3.0   5.0   3.0   3.0   5.0   3.0    4.0   1.0   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...    ...   ...   \n",
       "1015336   4.0   2.0   4.0   3.0   4.0   3.0   3.0   3.0   3.0    3.0   4.0   \n",
       "1015337   4.0   3.0   4.0   3.0   3.0   3.0   4.0   4.0   3.0    3.0   4.0   \n",
       "1015338   4.0   2.0   4.0   3.0   5.0   1.0   4.0   2.0   4.0    4.0   3.0   \n",
       "1015339   2.0   4.0   3.0   4.0   2.0   2.0   1.0   4.0   2.0    4.0   4.0   \n",
       "1015340   4.0   2.0   4.0   2.0   4.0   1.0   4.0   2.0   4.0    4.0   4.0   \n",
       "\n",
       "         EST2  EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  \\\n",
       "0         4.0   4.0   2.0   2.0   2.0   2.0   2.0   3.0    2.0   2.0   5.0   \n",
       "1         3.0   4.0   1.0   3.0   1.0   2.0   1.0   3.0    1.0   1.0   4.0   \n",
       "2         4.0   4.0   2.0   2.0   2.0   2.0   2.0   1.0    3.0   1.0   4.0   \n",
       "3         3.0   3.0   2.0   3.0   2.0   2.0   2.0   4.0    3.0   2.0   4.0   \n",
       "4         5.0   5.0   3.0   1.0   1.0   1.0   1.0   3.0    2.0   1.0   5.0   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...    ...   ...   ...   \n",
       "1015336   3.0   3.0   3.0   4.0   3.0   4.0   3.0   3.0    3.0   5.0   4.0   \n",
       "1015337   3.0   5.0   1.0   5.0   5.0   4.0   4.0   4.0    5.0   2.0   4.0   \n",
       "1015338   2.0   4.0   3.0   2.0   2.0   4.0   2.0   4.0    1.0   3.0   5.0   \n",
       "1015339   3.0   4.0   2.0   4.0   4.0   2.0   2.0   4.0    4.0   2.0   3.0   \n",
       "1015340   3.0   4.0   3.0   2.0   3.0   3.0   1.0   4.0    2.0   1.0   5.0   \n",
       "\n",
       "         AGR3  AGR4  AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  \\\n",
       "0         2.0   4.0   2.0   3.0   2.0   4.0   3.0    4.0   3.0   4.0   3.0   \n",
       "1         1.0   5.0   1.0   5.0   3.0   4.0   5.0    3.0   3.0   2.0   5.0   \n",
       "2         1.0   4.0   2.0   4.0   1.0   4.0   4.0    3.0   4.0   2.0   2.0   \n",
       "3         3.0   4.0   2.0   4.0   2.0   4.0   3.0    4.0   2.0   4.0   4.0   \n",
       "4         1.0   5.0   1.0   3.0   1.0   5.0   5.0    3.0   5.0   1.0   5.0   \n",
       "...       ...   ...   ...   ...   ...   ...   ...    ...   ...   ...   ...   \n",
       "1015336   2.0   5.0   2.0   4.0   2.0   4.0   4.0    4.0   2.0   3.0   3.0   \n",
       "1015337   1.0   4.0   3.0   5.0   3.0   3.0   5.0    3.0   3.0   2.0   3.0   \n",
       "1015338   5.0   3.0   2.0   3.0   2.0   4.0   3.0    5.0   4.0   5.0   3.0   \n",
       "1015339   2.0   4.0   3.0   4.0   2.0   4.0   4.0    3.0   4.0   2.0   4.0   \n",
       "1015340   2.0   4.0   3.0   5.0   2.0   3.0   4.0    5.0   3.0   4.0   3.0   \n",
       "\n",
       "         CSN4  CSN5  CSN6  CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  \\\n",
       "0         2.0   2.0   4.0   4.0   2.0   4.0    4.0   5.0   1.0   4.0   1.0   \n",
       "1         3.0   3.0   1.0   3.0   3.0   5.0    3.0   1.0   2.0   4.0   2.0   \n",
       "2         2.0   3.0   3.0   4.0   2.0   4.0    2.0   5.0   1.0   2.0   1.0   \n",
       "3         4.0   1.0   2.0   2.0   3.0   1.0    4.0   4.0   2.0   5.0   2.0   \n",
       "4         1.0   3.0   1.0   5.0   1.0   5.0    5.0   5.0   1.0   5.0   1.0   \n",
       "...       ...   ...   ...   ...   ...   ...    ...   ...   ...   ...   ...   \n",
       "1015336   3.0   4.0   4.0   4.0   2.0   3.0    3.0   2.0   2.0   4.0   3.0   \n",
       "1015337   4.0   3.0   3.0   2.0   3.0   2.0    3.0   4.0   1.0   5.0   1.0   \n",
       "1015338   5.0   1.0   5.0   1.0   4.0   1.0    4.0   5.0   1.0   5.0   1.0   \n",
       "1015339   2.0   2.0   2.0   4.0   2.0   4.0    4.0   5.0   2.0   4.0   2.0   \n",
       "1015340   3.0   2.0   3.0   3.0   2.0   2.0    3.0   5.0   1.0   5.0   1.0   \n",
       "\n",
       "         OPN5  OPN6  OPN7  OPN8  OPN9  OPN10  \n",
       "0         4.0   1.0   5.0   3.0   4.0    5.0  \n",
       "1         3.0   1.0   4.0   2.0   5.0    3.0  \n",
       "2         4.0   2.0   5.0   3.0   4.0    4.0  \n",
       "3         3.0   1.0   4.0   4.0   3.0    3.0  \n",
       "4         5.0   1.0   5.0   3.0   5.0    5.0  \n",
       "...       ...   ...   ...   ...   ...    ...  \n",
       "1015336   4.0   2.0   4.0   2.0   2.0    4.0  \n",
       "1015337   5.0   1.0   3.0   4.0   5.0    4.0  \n",
       "1015338   4.0   1.0   5.0   5.0   4.0    5.0  \n",
       "1015339   3.0   2.0   4.0   5.0   5.0    3.0  \n",
       "1015340   3.0   1.0   5.0   4.0   5.0    5.0  \n",
       "\n",
       "[1015341 rows x 50 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = MiniBatchKMeans(n_clusters=5, random_state=0, batch_size=1000, max_iter=100).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "one = kmeans.cluster_centers_[0] #personality 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "two = kmeans.cluster_centers_[1] #personality 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "three = kmeans.cluster_centers_[2] #personality 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "four = kmeans.cluster_centers_[3] #personality 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "five = kmeans.cluster_centers_[4] #personality 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.21920362, 2.18409868, 4.16443762, 2.47526968, 4.06835382,\n",
       "       1.83553364, 3.59002082, 3.0565194 , 3.45311608, 2.81493633,\n",
       "       2.51820948, 3.69251869, 3.34036883, 3.18730983, 2.19825728,\n",
       "       2.01113634, 2.17626382, 1.74063246, 2.21409125, 1.8205004 ,\n",
       "       1.87340623, 4.36497801, 1.7448168 , 4.28620646, 1.85055045,\n",
       "       3.92409055, 1.63110045, 4.10337663, 4.13469143, 4.14148329,\n",
       "       3.82073035, 2.37752282, 4.25730617, 1.80941333, 3.28965987,\n",
       "       2.10427181, 3.99691615, 1.87288473, 3.72929901, 3.91711391,\n",
       "       3.80581619, 1.83692157, 4.0129267 , 1.86325952, 4.0799706 ,\n",
       "       1.71755083, 4.25832454, 3.13808325, 4.1157695 , 4.19539517])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    " one_scores = {}\n",
    "\n",
    "one_scores['extroversion_score'] =  one[0] - one[1] + one[2] - one[3] + one[4] - one[5] + one[6] - one[7] \n",
    "one_scores['neuroticism_score'] =  one[0] - one[1] + one[2] - one[3] + one[4] + one[5] + one[6] + one[7] \n",
    "one_scores['agreeableness_score'] =  -one[0] + one[1] - one[2] + one[3] - one[4] - one[5] + one[6] - one[7]\n",
    "one_scores['conscientiousness_score'] =  one[0] - one[1] + one[2] - one[3] + one[4] - one[5] + one[6] - one[7] \n",
    "one_scores['openness_score'] =  one[0] - one[1] + one[2] - one[3] + one[4] - one[5] + one[6] + one[7] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 5.490594472070846,\n",
       " 'neuroticism_score': 15.274700546551296,\n",
       " 'agreeableness_score': -8.094658908457781,\n",
       " 'conscientiousness_score': 5.490594472070846,\n",
       " 'openness_score': 11.603633272696651}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#doing the same thing above for all personality traits\n",
    "\n",
    "all_types = {'one':one, 'two': two, 'three' :three, 'four':four, 'five':five}\n",
    "\n",
    "all_types_scores ={}\n",
    "\n",
    "for name, personality_type in all_types.items():\n",
    "    personality_trait = {}\n",
    "\n",
    "    personality_trait['extroversion_score'] =  personality_type[0] - personality_type[1] +personality_type[2] - personality_type[3] + personality_type[4] - personality_type[5] +personality_type[6] - personality_type[7] \n",
    "    personality_trait['neuroticism_score'] =  personality_type[0] - personality_type[1] + personality_type[2] -personality_type[3] + personality_type[4] + personality_type[5] + personality_type[6] + personality_type[7] \n",
    "    personality_trait['agreeableness_score'] =  -personality_type[0] +personality_type[1] - personality_type[2] + personality_type[3] - personality_type[4] - personality_type[5] + personality_type[6] - personality_type[7] \n",
    "    personality_trait['conscientiousness_score'] = personality_type[0] - personality_type[1] + personality_type[2] -personality_type[3] +personality_type[4] - personality_type[5] +personality_type[6] -personality_type[7] \n",
    "    personality_trait['openness_score'] =  personality_type[0] -personality_type[1] + personality_type[2] - personality_type[3] + personality_type[4] - personality_type[5] +personality_type[6] + personality_type[7]\n",
    "    \n",
    "    all_types_scores[name] = personality_trait"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': {'extroversion_score': 5.490594472070846,\n",
       "  'neuroticism_score': 15.274700546551296,\n",
       "  'agreeableness_score': -8.094658908457781,\n",
       "  'conscientiousness_score': 5.490594472070846,\n",
       "  'openness_score': 11.603633272696651},\n",
       " 'two': {'extroversion_score': -6.560870932376972,\n",
       "  'neuroticism_score': 7.988875385622134,\n",
       "  'agreeableness_score': -4.431624839589687,\n",
       "  'conscientiousness_score': -6.560870932376972,\n",
       "  'openness_score': 1.7033917750941185},\n",
       " 'three': {'extroversion_score': -3.7665939404844875,\n",
       "  'neuroticism_score': 9.425425673092066,\n",
       "  'agreeableness_score': -5.158177196967994,\n",
       "  'conscientiousness_score': -3.7665939404844875,\n",
       "  'openness_score': 3.6985762162891693},\n",
       " 'four': {'extroversion_score': 0.45111148253384364,\n",
       "  'neuroticism_score': 1.0367708507437718,\n",
       "  'agreeableness_score': -0.7730235667725198,\n",
       "  'conscientiousness_score': 0.45111148253384364,\n",
       "  'openness_score': 0.6917934146749101},\n",
       " 'five': {'extroversion_score': 5.847099756266622,\n",
       "  'neuroticism_score': 15.042696948972065,\n",
       "  'agreeableness_score': -7.775154800537971,\n",
       "  'conscientiousness_score': 5.847099756266622,\n",
       "  'openness_score': 11.414447744203883}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_types_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_extroversion = []\n",
    "all_neuroticism =[]\n",
    "all_agreeableness =[]\n",
    "all_conscientiousness =[]\n",
    "all_openness =[]\n",
    "\n",
    "for personality_type, personality_trait in all_types_scores.items():\n",
    "    all_extroversion.append(personality_trait['extroversion_score'])\n",
    "    all_neuroticism.append(personality_trait['neuroticism_score'])\n",
    "    all_agreeableness.append(personality_trait['agreeableness_score'])\n",
    "    all_conscientiousness.append(personality_trait['conscientiousness_score'])\n",
    "    all_openness.append(personality_trait['openness_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_extroversion_normalized = (all_extroversion-min(all_extroversion))/(max(all_extroversion)-min(all_extroversion))\n",
    "all_neuroticism_normalized = (all_neuroticism-min(all_neuroticism))/(max(all_neuroticism)-min(all_neuroticism))\n",
    "all_agreeableness_normalized = (all_agreeableness-min(all_agreeableness))/(max(all_agreeableness)-min(all_agreeableness))\n",
    "all_conscientiousness_normalized = (all_conscientiousness-min(all_conscientiousness))/(max(all_conscientiousness)-min(all_conscientiousness))\n",
    "all_openness_normalized = (all_openness-min(all_openness))/(max(all_openness)-min(all_openness))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97126804, 0.        , 0.22520016, 0.5651192 , 1.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_extroversion_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "\n",
    "normalized_all_types_scores ={}\n",
    "\n",
    "for personality_type, personality_trait in all_types_scores.items():\n",
    "    normalized_personality_trait ={}\n",
    "    normalized_personality_trait['extroversion_score'] = all_extroversion_normalized[counter]\n",
    "    normalized_personality_trait['neuroticism_score'] = all_neuroticism_normalized[counter]\n",
    "    normalized_personality_trait['agreeableness_score'] = all_agreeableness_normalized[counter]\n",
    "    normalized_personality_trait['conscientiousness_score'] = all_conscientiousness_normalized[counter]\n",
    "    normalized_personality_trait['openness_score'] = all_openness_normalized[counter]\n",
    "    \n",
    "    normalized_all_types_scores[personality_type] = normalized_personality_trait\n",
    "    \n",
    "    counter+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': {'extroversion_score': 0.9712680426846859,\n",
       "  'neuroticism_score': 1.0,\n",
       "  'agreeableness_score': 0.0,\n",
       "  'conscientiousness_score': 0.9712680426846859,\n",
       "  'openness_score': 1.0},\n",
       " 'two': {'extroversion_score': 0.0,\n",
       "  'neuroticism_score': 0.4882805775424967,\n",
       "  'agreeableness_score': 0.5003027189858589,\n",
       "  'conscientiousness_score': 0.0,\n",
       "  'openness_score': 0.09270648887644195},\n",
       " 'three': {'extroversion_score': 0.22520016060724166,\n",
       "  'neuroticism_score': 0.5891765868754362,\n",
       "  'agreeableness_score': 0.4010691019765921,\n",
       "  'conscientiousness_score': 0.22520016060724166,\n",
       "  'openness_score': 0.27555232121591755},\n",
       " 'four': {'extroversion_score': 0.5651191956254812,\n",
       "  'neuroticism_score': 0.0,\n",
       "  'agreeableness_score': 1.0,\n",
       "  'conscientiousness_score': 0.5651191956254812,\n",
       "  'openness_score': 0.0},\n",
       " 'five': {'extroversion_score': 1.0,\n",
       "  'neuroticism_score': 0.9837052434914364,\n",
       "  'agreeableness_score': 0.04363835304672087,\n",
       "  'conscientiousness_score': 1.0,\n",
       "  'openness_score': 0.9826623620806083}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_all_types_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_all_types_scores['one'].keys()), normalized_all_types_scores['one'].values(), color='darkgrey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the normalized scores for each cluster\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,4))\n",
    "\n",
    "all_types = ['one', 'two', 'three', 'four', 'five']\n",
    "xtick_labels = (\"Extroversion\",\"Neuroticism\",\"Agreeableness\",\"Conscientiousness\",\"Openess\")\n",
    "x_pos = np.arange(len(xtick_labels))\n",
    "\n",
    "n=1\n",
    "for type in all_types:\n",
    "    plt.subplot(1,5,n)\n",
    "    plt.ylim(0, 1)\n",
    "    plt.bar(list(normalized_all_types_scores[type].keys()), normalized_all_types_scores[type].values(), color='g',alpha=0.3)\n",
    "    plt.plot(list(normalized_all_types_scores[type].keys()), normalized_all_types_scores[type].values(), color='r')\n",
    "    n=n+1\n",
    "#     plt.xticks(rotation=90)\n",
    "    plt.title(f\"Personality type - {type}\")\n",
    "    plt.xticks(x_pos,xtick_labels)\n",
    "    plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graphs it's clearly evident that the clustered personalities - 'One' and 'Five' have less to no variations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 51 columns</p>\n",
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      ],
      "text/plain": [
       "   EXT1  EXT2  EXT3  EXT4  EXT5  ...  OPN7  OPN8  OPN9  OPN10  Clusters\n",
       "0   4.0   1.0   5.0   2.0   5.0  ...   5.0   3.0   4.0    5.0         0\n",
       "1   3.0   5.0   3.0   4.0   3.0  ...   4.0   2.0   5.0    3.0         2\n",
       "2   2.0   3.0   4.0   4.0   3.0  ...   5.0   3.0   4.0    4.0         2\n",
       "3   2.0   2.0   2.0   3.0   4.0  ...   4.0   4.0   3.0    3.0         2\n",
       "4   3.0   3.0   3.0   3.0   5.0  ...   5.0   3.0   5.0    5.0         0\n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_fit = kmeans.fit(X)\n",
    "pd.options.display.max_columns = 10\n",
    "predictions = k_fit.labels_\n",
    "X['Clusters'] = predictions\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    267326\n",
       "1    261844\n",
       "4    242756\n",
       "2    236946\n",
       "3      6469\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.Clusters.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.220727</td>\n",
       "      <td>2.176642</td>\n",
       "      <td>4.163617</td>\n",
       "      <td>2.474028</td>\n",
       "      <td>4.072705</td>\n",
       "      <td>1.827768</td>\n",
       "      <td>3.592191</td>\n",
       "      <td>3.056848</td>\n",
       "      <td>3.453536</td>\n",
       "      <td>2.814474</td>\n",
       "      <td>2.528063</td>\n",
       "      <td>3.689353</td>\n",
       "      <td>3.345825</td>\n",
       "      <td>3.182526</td>\n",
       "      <td>2.202517</td>\n",
       "      <td>2.022130</td>\n",
       "      <td>2.187172</td>\n",
       "      <td>1.749878</td>\n",
       "      <td>2.222492</td>\n",
       "      <td>1.828554</td>\n",
       "      <td>1.856703</td>\n",
       "      <td>4.374251</td>\n",
       "      <td>1.739647</td>\n",
       "      <td>4.297423</td>\n",
       "      <td>1.839469</td>\n",
       "      <td>3.933276</td>\n",
       "      <td>1.622300</td>\n",
       "      <td>4.111953</td>\n",
       "      <td>4.145021</td>\n",
       "      <td>4.145179</td>\n",
       "      <td>3.815626</td>\n",
       "      <td>2.387837</td>\n",
       "      <td>4.257371</td>\n",
       "      <td>1.814833</td>\n",
       "      <td>3.278738</td>\n",
       "      <td>2.110640</td>\n",
       "      <td>3.992010</td>\n",
       "      <td>1.876555</td>\n",
       "      <td>3.723544</td>\n",
       "      <td>3.919110</td>\n",
       "      <td>3.806424</td>\n",
       "      <td>1.834311</td>\n",
       "      <td>4.016081</td>\n",
       "      <td>1.858439</td>\n",
       "      <td>4.079442</td>\n",
       "      <td>1.715400</td>\n",
       "      <td>4.261385</td>\n",
       "      <td>3.142758</td>\n",
       "      <td>4.121137</td>\n",
       "      <td>4.196636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.820317</td>\n",
       "      <td>3.594766</td>\n",
       "      <td>2.376743</td>\n",
       "      <td>4.024045</td>\n",
       "      <td>2.397141</td>\n",
       "      <td>3.122210</td>\n",
       "      <td>1.783879</td>\n",
       "      <td>4.120965</td>\n",
       "      <td>2.107858</td>\n",
       "      <td>4.455706</td>\n",
       "      <td>4.252146</td>\n",
       "      <td>2.465071</td>\n",
       "      <td>4.536869</td>\n",
       "      <td>2.182257</td>\n",
       "      <td>3.494462</td>\n",
       "      <td>3.770008</td>\n",
       "      <td>3.840978</td>\n",
       "      <td>3.564955</td>\n",
       "      <td>3.878664</td>\n",
       "      <td>3.749038</td>\n",
       "      <td>2.267835</td>\n",
       "      <td>3.502868</td>\n",
       "      <td>2.302856</td>\n",
       "      <td>4.028238</td>\n",
       "      <td>2.358973</td>\n",
       "      <td>3.999771</td>\n",
       "      <td>2.513779</td>\n",
       "      <td>3.612529</td>\n",
       "      <td>3.871611</td>\n",
       "      <td>3.223836</td>\n",
       "      <td>3.150066</td>\n",
       "      <td>2.951070</td>\n",
       "      <td>3.980481</td>\n",
       "      <td>3.059028</td>\n",
       "      <td>2.474470</td>\n",
       "      <td>3.021727</td>\n",
       "      <td>3.808386</td>\n",
       "      <td>2.720356</td>\n",
       "      <td>3.169826</td>\n",
       "      <td>3.501879</td>\n",
       "      <td>3.481409</td>\n",
       "      <td>2.374719</td>\n",
       "      <td>4.006443</td>\n",
       "      <td>2.150830</td>\n",
       "      <td>3.463012</td>\n",
       "      <td>2.089523</td>\n",
       "      <td>3.729182</td>\n",
       "      <td>3.086166</td>\n",
       "      <td>4.300480</td>\n",
       "      <td>3.694570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.146396</td>\n",
       "      <td>3.447866</td>\n",
       "      <td>2.855803</td>\n",
       "      <td>3.610645</td>\n",
       "      <td>2.638158</td>\n",
       "      <td>2.886953</td>\n",
       "      <td>2.111169</td>\n",
       "      <td>3.753589</td>\n",
       "      <td>2.609666</td>\n",
       "      <td>4.026318</td>\n",
       "      <td>2.587598</td>\n",
       "      <td>3.641391</td>\n",
       "      <td>3.312075</td>\n",
       "      <td>2.916411</td>\n",
       "      <td>2.454846</td>\n",
       "      <td>2.162721</td>\n",
       "      <td>2.448457</td>\n",
       "      <td>2.039076</td>\n",
       "      <td>2.683776</td>\n",
       "      <td>2.408827</td>\n",
       "      <td>2.837739</td>\n",
       "      <td>3.185004</td>\n",
       "      <td>2.445473</td>\n",
       "      <td>3.201033</td>\n",
       "      <td>2.982983</td>\n",
       "      <td>3.052307</td>\n",
       "      <td>2.886472</td>\n",
       "      <td>3.153077</td>\n",
       "      <td>3.016822</td>\n",
       "      <td>3.181717</td>\n",
       "      <td>3.273974</td>\n",
       "      <td>2.875959</td>\n",
       "      <td>3.850780</td>\n",
       "      <td>2.458151</td>\n",
       "      <td>2.503866</td>\n",
       "      <td>2.788559</td>\n",
       "      <td>3.564816</td>\n",
       "      <td>2.531805</td>\n",
       "      <td>2.973374</td>\n",
       "      <td>3.499443</td>\n",
       "      <td>3.623260</td>\n",
       "      <td>2.028576</td>\n",
       "      <td>3.777599</td>\n",
       "      <td>2.080259</td>\n",
       "      <td>3.727512</td>\n",
       "      <td>2.058427</td>\n",
       "      <td>4.005883</td>\n",
       "      <td>3.180484</td>\n",
       "      <td>3.928777</td>\n",
       "      <td>3.794274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.828567</td>\n",
       "      <td>0.614469</td>\n",
       "      <td>0.515226</td>\n",
       "      <td>0.358015</td>\n",
       "      <td>0.264956</td>\n",
       "      <td>0.160303</td>\n",
       "      <td>0.119029</td>\n",
       "      <td>0.118875</td>\n",
       "      <td>0.120111</td>\n",
       "      <td>0.149946</td>\n",
       "      <td>0.722214</td>\n",
       "      <td>0.545679</td>\n",
       "      <td>0.466224</td>\n",
       "      <td>0.272685</td>\n",
       "      <td>0.191838</td>\n",
       "      <td>0.122739</td>\n",
       "      <td>0.114701</td>\n",
       "      <td>0.109754</td>\n",
       "      <td>0.125213</td>\n",
       "      <td>0.150100</td>\n",
       "      <td>0.747874</td>\n",
       "      <td>0.688514</td>\n",
       "      <td>0.371773</td>\n",
       "      <td>0.387231</td>\n",
       "      <td>0.212088</td>\n",
       "      <td>0.166023</td>\n",
       "      <td>0.109445</td>\n",
       "      <td>0.111609</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>0.132323</td>\n",
       "      <td>0.777245</td>\n",
       "      <td>0.529139</td>\n",
       "      <td>0.496367</td>\n",
       "      <td>0.288453</td>\n",
       "      <td>0.192456</td>\n",
       "      <td>0.123976</td>\n",
       "      <td>0.111609</td>\n",
       "      <td>0.105426</td>\n",
       "      <td>0.122430</td>\n",
       "      <td>0.147318</td>\n",
       "      <td>0.686350</td>\n",
       "      <td>0.439481</td>\n",
       "      <td>0.420621</td>\n",
       "      <td>0.233576</td>\n",
       "      <td>0.193693</td>\n",
       "      <td>0.109600</td>\n",
       "      <td>0.114546</td>\n",
       "      <td>0.111764</td>\n",
       "      <td>0.128150</td>\n",
       "      <td>0.216726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.428982</td>\n",
       "      <td>1.922255</td>\n",
       "      <td>3.779717</td>\n",
       "      <td>2.513993</td>\n",
       "      <td>4.029383</td>\n",
       "      <td>1.822501</td>\n",
       "      <td>3.628895</td>\n",
       "      <td>2.779433</td>\n",
       "      <td>3.747158</td>\n",
       "      <td>3.009672</td>\n",
       "      <td>3.804277</td>\n",
       "      <td>2.924406</td>\n",
       "      <td>4.236513</td>\n",
       "      <td>2.409382</td>\n",
       "      <td>3.274613</td>\n",
       "      <td>3.454951</td>\n",
       "      <td>3.791593</td>\n",
       "      <td>3.438667</td>\n",
       "      <td>3.641228</td>\n",
       "      <td>3.167341</td>\n",
       "      <td>2.135894</td>\n",
       "      <td>4.273266</td>\n",
       "      <td>2.639300</td>\n",
       "      <td>4.186039</td>\n",
       "      <td>1.993961</td>\n",
       "      <td>3.998810</td>\n",
       "      <td>1.849717</td>\n",
       "      <td>3.898680</td>\n",
       "      <td>4.131696</td>\n",
       "      <td>3.847687</td>\n",
       "      <td>2.953946</td>\n",
       "      <td>3.600747</td>\n",
       "      <td>3.844057</td>\n",
       "      <td>3.225065</td>\n",
       "      <td>2.234713</td>\n",
       "      <td>3.514743</td>\n",
       "      <td>3.454353</td>\n",
       "      <td>2.818414</td>\n",
       "      <td>2.938626</td>\n",
       "      <td>3.478880</td>\n",
       "      <td>3.757217</td>\n",
       "      <td>2.094317</td>\n",
       "      <td>4.258708</td>\n",
       "      <td>1.946790</td>\n",
       "      <td>3.965080</td>\n",
       "      <td>1.755454</td>\n",
       "      <td>3.974052</td>\n",
       "      <td>3.390400</td>\n",
       "      <td>4.199336</td>\n",
       "      <td>4.211632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              EXT1      EXT2      EXT3      EXT4      EXT5      EXT6  \\\n",
       "Clusters                                                               \n",
       "0         3.220727  2.176642  4.163617  2.474028  4.072705  1.827768   \n",
       "1         1.820317  3.594766  2.376743  4.024045  2.397141  3.122210   \n",
       "2         2.146396  3.447866  2.855803  3.610645  2.638158  2.886953   \n",
       "3         0.828567  0.614469  0.515226  0.358015  0.264956  0.160303   \n",
       "4         3.428982  1.922255  3.779717  2.513993  4.029383  1.822501   \n",
       "\n",
       "              EXT7      EXT8      EXT9     EXT10      EST1      EST2  \\\n",
       "Clusters                                                               \n",
       "0         3.592191  3.056848  3.453536  2.814474  2.528063  3.689353   \n",
       "1         1.783879  4.120965  2.107858  4.455706  4.252146  2.465071   \n",
       "2         2.111169  3.753589  2.609666  4.026318  2.587598  3.641391   \n",
       "3         0.119029  0.118875  0.120111  0.149946  0.722214  0.545679   \n",
       "4         3.628895  2.779433  3.747158  3.009672  3.804277  2.924406   \n",
       "\n",
       "              EST3      EST4      EST5      EST6      EST7      EST8  \\\n",
       "Clusters                                                               \n",
       "0         3.345825  3.182526  2.202517  2.022130  2.187172  1.749878   \n",
       "1         4.536869  2.182257  3.494462  3.770008  3.840978  3.564955   \n",
       "2         3.312075  2.916411  2.454846  2.162721  2.448457  2.039076   \n",
       "3         0.466224  0.272685  0.191838  0.122739  0.114701  0.109754   \n",
       "4         4.236513  2.409382  3.274613  3.454951  3.791593  3.438667   \n",
       "\n",
       "              EST9     EST10      AGR1      AGR2      AGR3      AGR4  \\\n",
       "Clusters                                                               \n",
       "0         2.222492  1.828554  1.856703  4.374251  1.739647  4.297423   \n",
       "1         3.878664  3.749038  2.267835  3.502868  2.302856  4.028238   \n",
       "2         2.683776  2.408827  2.837739  3.185004  2.445473  3.201033   \n",
       "3         0.125213  0.150100  0.747874  0.688514  0.371773  0.387231   \n",
       "4         3.641228  3.167341  2.135894  4.273266  2.639300  4.186039   \n",
       "\n",
       "              AGR5      AGR6      AGR7      AGR8      AGR9     AGR10  \\\n",
       "Clusters                                                               \n",
       "0         1.839469  3.933276  1.622300  4.111953  4.145021  4.145179   \n",
       "1         2.358973  3.999771  2.513779  3.612529  3.871611  3.223836   \n",
       "2         2.982983  3.052307  2.886472  3.153077  3.016822  3.181717   \n",
       "3         0.212088  0.166023  0.109445  0.111609  0.130005  0.132323   \n",
       "4         1.993961  3.998810  1.849717  3.898680  4.131696  3.847687   \n",
       "\n",
       "              CSN1      CSN2      CSN3      CSN4      CSN5      CSN6  \\\n",
       "Clusters                                                               \n",
       "0         3.815626  2.387837  4.257371  1.814833  3.278738  2.110640   \n",
       "1         3.150066  2.951070  3.980481  3.059028  2.474470  3.021727   \n",
       "2         3.273974  2.875959  3.850780  2.458151  2.503866  2.788559   \n",
       "3         0.777245  0.529139  0.496367  0.288453  0.192456  0.123976   \n",
       "4         2.953946  3.600747  3.844057  3.225065  2.234713  3.514743   \n",
       "\n",
       "              CSN7      CSN8      CSN9     CSN10      OPN1      OPN2  \\\n",
       "Clusters                                                               \n",
       "0         3.992010  1.876555  3.723544  3.919110  3.806424  1.834311   \n",
       "1         3.808386  2.720356  3.169826  3.501879  3.481409  2.374719   \n",
       "2         3.564816  2.531805  2.973374  3.499443  3.623260  2.028576   \n",
       "3         0.111609  0.105426  0.122430  0.147318  0.686350  0.439481   \n",
       "4         3.454353  2.818414  2.938626  3.478880  3.757217  2.094317   \n",
       "\n",
       "              OPN3      OPN4      OPN5      OPN6      OPN7      OPN8  \\\n",
       "Clusters                                                               \n",
       "0         4.016081  1.858439  4.079442  1.715400  4.261385  3.142758   \n",
       "1         4.006443  2.150830  3.463012  2.089523  3.729182  3.086166   \n",
       "2         3.777599  2.080259  3.727512  2.058427  4.005883  3.180484   \n",
       "3         0.420621  0.233576  0.193693  0.109600  0.114546  0.111764   \n",
       "4         4.258708  1.946790  3.965080  1.755454  3.974052  3.390400   \n",
       "\n",
       "              OPN9     OPN10  \n",
       "Clusters                      \n",
       "0         4.121137  4.196636  \n",
       "1         4.300480  3.694570  \n",
       "2         3.928777  3.794274  \n",
       "3         0.128150  0.216726  \n",
       "4         4.199336  4.211632  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = 150\n",
    "X.groupby('Clusters').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>3.085254</td>\n",
       "      <td>2.495851</td>\n",
       "      <td>3.206522</td>\n",
       "      <td>3.117626</td>\n",
       "      <td>3.303201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.980363</td>\n",
       "      <td>3.573445</td>\n",
       "      <td>3.168230</td>\n",
       "      <td>3.183729</td>\n",
       "      <td>3.237633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.008656</td>\n",
       "      <td>2.665518</td>\n",
       "      <td>2.994263</td>\n",
       "      <td>3.032073</td>\n",
       "      <td>3.220505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.324950</td>\n",
       "      <td>0.282115</td>\n",
       "      <td>0.305689</td>\n",
       "      <td>0.289442</td>\n",
       "      <td>0.265451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.066199</td>\n",
       "      <td>3.414297</td>\n",
       "      <td>3.295505</td>\n",
       "      <td>3.206355</td>\n",
       "      <td>3.355299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          extroversion  neurotic  agreeable  conscientious      open\n",
       "clusters                                                            \n",
       "0             3.085254  2.495851   3.206522       3.117626  3.303201\n",
       "1             2.980363  3.573445   3.168230       3.183729  3.237633\n",
       "2             3.008656  2.665518   2.994263       3.032073  3.220505\n",
       "3             0.324950  0.282115   0.305689       0.289442  0.265451\n",
       "4             3.066199  3.414297   3.295505       3.206355  3.355299"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summing up the different questions groups\n",
    "col_list = list(X)\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_sums = pd.DataFrame()\n",
    "data_sums['extroversion'] = X[ext].sum(axis=1)/10\n",
    "data_sums['neurotic'] = X[est].sum(axis=1)/10\n",
    "data_sums['agreeable'] = X[agr].sum(axis=1)/10\n",
    "data_sums['conscientious'] = X[csn].sum(axis=1)/10\n",
    "data_sums['open'] = X[opn].sum(axis=1)/10\n",
    "data_sums['clusters'] = predictions\n",
    "data_sums.groupby('clusters').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the means for each cluster\n",
    "dataclusters = data_sums.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(dataclusters.columns, dataclusters.iloc[:, i], color='green', alpha=0.2)\n",
    "    plt.plot(dataclusters.columns, dataclusters.iloc[:, i], color='red')\n",
    "    plt.title('Cluster ' + str(i))\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylim(0,4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.445848</td>\n",
       "      <td>-1.625106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.045281</td>\n",
       "      <td>3.121060</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.744772</td>\n",
       "      <td>2.094775</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.156755</td>\n",
       "      <td>0.250545</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.035091</td>\n",
       "      <td>2.490591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCA1      PCA2  Clusters\n",
       "0 -5.445848 -1.625106         0\n",
       "1  0.045281  3.121060         2\n",
       "2 -0.744772  2.094775         2\n",
       "3  1.156755  0.250545         2\n",
       "4 -5.035091  2.490591         0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to visualize in 2D graph I will use PCA\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pca_fit = pca.fit_transform(X)\n",
    "\n",
    "df_pca = pd.DataFrame(data=pca_fit, columns=['PCA1', 'PCA2'])\n",
    "df_pca['Clusters'] = predictions\n",
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.scatterplot(data=df_pca, x='PCA1', y='PCA2', hue='Clusters', palette='Set2', alpha=0.8)\n",
    "plt.title('Personality Clusters after PCA');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#questions in order\n",
    "questions=['I am the life of the party',\n",
    "          'I dont talk a lot',\n",
    "          'I feel comfortable around people',\n",
    "          'I keep in the background',\n",
    "          'I start conversations',\n",
    "          'I have little to say',\n",
    "          'I talk to a lot of different people at parties',\n",
    "          'I dont like to draw attention to myself',\n",
    "          'I dont mind being the center of attention',\n",
    "          'I am quiet around strangers',\n",
    "          'I get stressed out easily',\n",
    "          'I am relaxed most of the time',\n",
    "          'I worry about things',\n",
    "          'I seldom feel blue',\n",
    "          'I am easily disturbed',\n",
    "          'I get upset easily',\n",
    "          'I change my mood a lot',\n",
    "          'I have frequent mood swings',\n",
    "          'I get irritated easily',\n",
    "          'I often feel blue',\n",
    "          'I feel little concern for others',\n",
    "          'I am interested in people',\n",
    "          'I insult people',\n",
    "          'I sympathize with others feelings',\n",
    "          'I am not interested in other peoples problems',\n",
    "          'I have a soft heart',\n",
    "          'I am not really interested in others',\n",
    "          'I take time out for others',\n",
    "          'I feel others emotions',\n",
    "          'I make people feel at ease',\n",
    "          'I am always prepared',\n",
    "          'I leave my belongings around',\n",
    "          'I pay attention to details',\n",
    "          'I make a mess of things',\n",
    "          'I get chores done right away',\n",
    "          'I often forget to put things back in their proper place',\n",
    "          'I like order',\n",
    "          'I shirk my duties',\n",
    "          'I follow a schedule',\n",
    "          'I am exacting in my work',\n",
    "          'I have a rich vocabulary',\n",
    "          'I have difficulty understanding abstract ideas',\n",
    "          'I have a vivid imagination',\n",
    "          'I am not interested in abstract ideas',\n",
    "          'I have excellent ideas',\n",
    "          'I do not have a good imagination',\n",
    "          'I am quick to understand things',\n",
    "          'I use difficult words',\n",
    "          'I spend time reflecting on things',\n",
    "          'I am full of ideas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am the life of the party :  2\n",
      "I dont talk a lot :  3\n",
      "I feel comfortable around people :  2\n",
      "I keep in the background :  1\n",
      "I start conversations :  4\n",
      "I have little to say :  5\n",
      "I talk to a lot of different people at parties :  3\n",
      "I dont like to draw attention to myself :  2\n",
      "I dont mind being the center of attention :  4\n",
      "I am quiet around strangers :  3\n",
      "I get stressed out easily :  1\n",
      "I am relaxed most of the time :  5\n",
      "I worry about things :  2\n",
      "I seldom feel blue :  5\n",
      "I am easily disturbed :  4\n",
      "I get upset easily :  5\n",
      "I change my mood a lot :  1\n",
      "I have frequent mood swings :  2\n",
      "I get irritated easily :  1\n",
      "I often feel blue :  4\n",
      "I feel little concern for others :  5\n",
      "I am interested in people :  3\n",
      "I insult people :  1\n",
      "I sympathize with others feelings :  2\n",
      "I am not interested in other peoples problems :  5\n",
      "I have a soft heart :  4\n",
      "I am not really interested in others :  2\n",
      "I take time out for others :  1\n",
      "I feel others emotions :  4\n",
      "I make people feel at ease :  1\n",
      "I am always prepared :  2\n",
      "I leave my belongings around :  5\n",
      "I pay attention to details :  2\n",
      "I make a mess of things :  4\n",
      "I get chores done right away :  1\n",
      "I often forget to put things back in their proper place :  2\n",
      "I like order :  5\n",
      "I shirk my duties :  1\n",
      "I follow a schedule :  4\n",
      "I am exacting in my work :  3\n",
      "I have a rich vocabulary :  2\n",
      "I have difficulty understanding abstract ideas :  5\n",
      "I have a vivid imagination :  3\n",
      "I am not interested in abstract ideas :  1\n",
      "I have excellent ideas :  2\n",
      "I do not have a good imagination :  5\n",
      "I am quick to understand things :  1\n",
      "I use difficult words :  5\n",
      "I spend time reflecting on things :  4\n",
      "I am full of ideas :  2\n"
     ]
    }
   ],
   "source": [
    "test_response={}\n",
    "\n",
    "for q_num in range(len(questions)):\n",
    "    user_input=input(f\"{questions[q_num]} :  \")\n",
    "#     print(user_input)\n",
    "    test_response[X.columns[q_num]]=user_input\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EXT1': '2',\n",
       " 'EXT2': '3',\n",
       " 'EXT3': '2',\n",
       " 'EXT4': '1',\n",
       " 'EXT5': '4',\n",
       " 'EXT6': '5',\n",
       " 'EXT7': '3',\n",
       " 'EXT8': '2',\n",
       " 'EXT9': '4',\n",
       " 'EXT10': '3',\n",
       " 'EST1': '1',\n",
       " 'EST2': '5',\n",
       " 'EST3': '2',\n",
       " 'EST4': '5',\n",
       " 'EST5': '4',\n",
       " 'EST6': '5',\n",
       " 'EST7': '1',\n",
       " 'EST8': '2',\n",
       " 'EST9': '1',\n",
       " 'EST10': '4',\n",
       " 'AGR1': '5',\n",
       " 'AGR2': '3',\n",
       " 'AGR3': '1',\n",
       " 'AGR4': '2',\n",
       " 'AGR5': '5',\n",
       " 'AGR6': '4',\n",
       " 'AGR7': '2',\n",
       " 'AGR8': '1',\n",
       " 'AGR9': '4',\n",
       " 'AGR10': '1',\n",
       " 'CSN1': '2',\n",
       " 'CSN2': '5',\n",
       " 'CSN3': '2',\n",
       " 'CSN4': '4',\n",
       " 'CSN5': '1',\n",
       " 'CSN6': '2',\n",
       " 'CSN7': '5',\n",
       " 'CSN8': '1',\n",
       " 'CSN9': '4',\n",
       " 'CSN10': '3',\n",
       " 'OPN1': '2',\n",
       " 'OPN2': '5',\n",
       " 'OPN3': '3',\n",
       " 'OPN4': '1',\n",
       " 'OPN5': '2',\n",
       " 'OPN6': '5',\n",
       " 'OPN7': '1',\n",
       " 'OPN8': '5',\n",
       " 'OPN9': '4',\n",
       " 'OPN10': '2'}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "0  EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  EST2  \\\n",
       "0     2     3     2     1     4     5     3     2     4      3     1     5   \n",
       "\n",
       "0  EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  AGR3  AGR4  \\\n",
       "0     2     5     4     5     1     2     1      4     5     3     1     2   \n",
       "\n",
       "0  AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  CSN4  CSN5  CSN6  \\\n",
       "0     5     4     2     1     4      1     2     5     2     4     1     2   \n",
       "\n",
       "0  CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  \\\n",
       "0     5     1     4      3     2     5     3     1     2     5     1     5   \n",
       "\n",
       "0  OPN9  OPN10  \n",
       "0     4      2  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.DataFrame(test_response.items()).T\n",
    "new_header = test_df.iloc[0] #grab the first row for the header\n",
    "test_df = test_df[1:] #take the data less the header row\n",
    "test_df.columns = new_header #set the header row as the df header\n",
    "test_df.reset_index(drop=True, inplace=True)\n",
    "test_df= test_df.apply(pd.to_numeric)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Personality Cluster:  [2]\n"
     ]
    }
   ],
   "source": [
    "my_personality = k_fit.predict(test_df)\n",
    "print('My Personality Cluster: ', my_personality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of my question groups\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\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>extroversion</th>\n",
       "      <th>neurotic</th>\n",
       "      <th>agreeable</th>\n",
       "      <th>conscientious</th>\n",
       "      <th>open</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.9</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "   extroversion  neurotic  agreeable  conscientious  open  cluster\n",
       "0           2.9       3.0        2.8            2.9   3.0        2"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summing up the my question groups\n",
    "col_list = list(test_df)\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",
    "my_sums = pd.DataFrame()\n",
    "my_sums['extroversion'] = test_df[ext].sum(axis=1)/10\n",
    "my_sums['neurotic'] = test_df[est].sum(axis=1)/10\n",
    "my_sums['agreeable'] = test_df[agr].sum(axis=1)/10\n",
    "my_sums['conscientious'] = test_df[csn].sum(axis=1)/10\n",
    "my_sums['open'] = test_df[opn].sum(axis=1)/10\n",
    "my_sums['cluster'] = my_personality\n",
    "print('Sum of my question groups')\n",
    "my_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_sum = my_sums.drop('cluster', axis=1)\n",
    "plt.bar(my_sum.columns, my_sum.iloc[0,:], color='green', alpha=0.3)\n",
    "plt.plot(my_sum.columns, my_sum.iloc[0,:], color='red')\n",
    "plt.title('Cluster 2')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0,4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-181-7914252092e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;31m# This gives a perspective into the density and separation of the formed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[1;31m# clusters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m     \u001b[0msilhouette_avg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msilhouette_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcluster_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m     print(\"For n_clusters =\", n_clusters,\n\u001b[0;32m     41\u001b[0m           \"The average silhouette_score is :\", silhouette_avg)\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py\u001b[0m in \u001b[0;36msilhouette_score\u001b[1;34m(X, labels, metric, sample_size, random_state, **kwds)\u001b[0m\n\u001b[0;32m    115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msilhouette_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py\u001b[0m in \u001b[0;36msilhouette_samples\u001b[1;34m(X, labels, metric, **kwds)\u001b[0m\n\u001b[0;32m    232\u001b[0m     reduce_func = functools.partial(_silhouette_reduce,\n\u001b[0;32m    233\u001b[0m                                     labels=labels, label_freqs=label_freqs)\n\u001b[1;32m--> 234\u001b[1;33m     results = zip(*pairwise_distances_chunked(X, reduce_func=reduce_func,\n\u001b[0m\u001b[0;32m    235\u001b[0m                                               **kwds))\n\u001b[0;32m    236\u001b[0m     \u001b[0mintra_clust_dists\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minter_clust_dists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36mpairwise_distances_chunked\u001b[1;34m(X, Y, reduce_func, metric, n_jobs, working_memory, **kwds)\u001b[0m\n\u001b[0;32m   1614\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1615\u001b[0m             \u001b[0mX_chunk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msl\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1616\u001b[1;33m         D_chunk = pairwise_distances(X_chunk, Y, metric=metric,\n\u001b[0m\u001b[0;32m   1617\u001b[0m                                      n_jobs=n_jobs, **kwds)\n\u001b[0;32m   1618\u001b[0m         if ((X is Y or Y is None)\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36mpairwise_distances\u001b[1;34m(X, Y, metric, n_jobs, force_all_finite, **kwds)\u001b[0m\n\u001b[0;32m   1777\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcdist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1778\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1779\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_parallel_pairwise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1780\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36m_parallel_pairwise\u001b[1;34m(X, Y, func, n_jobs, **kwds)\u001b[0m\n\u001b[0;32m   1358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1359\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0meffective_n_jobs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1360\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1362\u001b[0m     \u001b[1;31m# enforce a threading backend to prevent data communication overhead\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36meuclidean_distances\u001b[1;34m(X, Y, Y_norm_squared, squared, X_norm_squared)\u001b[0m\n\u001b[0;32m    310\u001b[0m         \u001b[0mdistances\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0msafe_sparse_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdense_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m         \u001b[0mdistances\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[0mdistances\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mYY\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m     \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistances\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdistances\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "# Generating the sample data from make_blobs\n",
    "# This particular setting has one distinct cluster and 3 clusters placed close\n",
    "# together.\n",
    "range_n_clusters=[2,3,4,5]\n",
    "\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    # Create a subplot with 1 row and 2 columns\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(18, 7)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax1.set_xlim([-0.1, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(X)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "    # 2nd Plot showing the actual clusters formed\n",
    "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
    "    ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n",
    "                c=colors, edgecolor='k')\n",
    "\n",
    "    # Labeling the clusters\n",
    "    centers = clusterer.cluster_centers_\n",
    "    # Draw white circles at cluster centers\n",
    "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
    "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
    "\n",
    "    for i, c in enumerate(centers):\n",
    "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
    "                    s=50, edgecolor='k')\n",
    "\n",
    "    ax2.set_title(\"The visualization of the clustered data.\")\n",
    "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
    "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % n_clusters),\n",
    "                 fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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