Personality_system_8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
       "      <th>EXT4</th>\n",
       "      <th>EXT5</th>\n",
       "      <th>EXT6</th>\n",
       "      <th>EXT7</th>\n",
       "      <th>EXT8</th>\n",
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       "      <th>EXT10</th>\n",
       "      <th>...</th>\n",
       "      <th>dateload</th>\n",
       "      <th>screenw</th>\n",
       "      <th>screenh</th>\n",
       "      <th>introelapse</th>\n",
       "      <th>testelapse</th>\n",
       "      <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",
       "  </thead>\n",
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       "      <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",
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       "    <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>...</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>...</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>...</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>...</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",
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       "      <th>1015336</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>...</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>...</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",
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       "      <td>2018-11-08 12:07:49</td>\n",
       "      <td>1920.0</td>\n",
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       "      <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>...</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",
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       "    <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>...</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",
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       "  </tbody>\n",
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       "<p>1015341 rows × 110 columns</p>\n",
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       "         EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  ...  \\\n",
       "0         4.0   1.0   5.0   2.0   5.0   1.0   5.0   2.0   4.0    1.0  ...   \n",
       "1         3.0   5.0   3.0   4.0   3.0   3.0   2.0   5.0   1.0    5.0  ...   \n",
       "2         2.0   3.0   4.0   4.0   3.0   2.0   1.0   3.0   2.0    5.0  ...   \n",
       "3         2.0   2.0   2.0   3.0   4.0   2.0   2.0   4.0   1.0    4.0  ...   \n",
       "4         3.0   3.0   3.0   3.0   5.0   3.0   3.0   5.0   3.0    4.0  ...   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...   \n",
       "1015336   4.0   2.0   4.0   3.0   4.0   3.0   3.0   3.0   3.0    3.0  ...   \n",
       "1015337   4.0   3.0   4.0   3.0   3.0   3.0   4.0   4.0   3.0    3.0  ...   \n",
       "1015338   4.0   2.0   4.0   3.0   5.0   1.0   4.0   2.0   4.0    4.0  ...   \n",
       "1015339   2.0   4.0   3.0   4.0   2.0   2.0   1.0   4.0   2.0    4.0  ...   \n",
       "1015340   4.0   2.0   4.0   2.0   4.0   1.0   4.0   2.0   4.0    4.0  ...   \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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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></th>\n",
       "      <th>EXT1</th>\n",
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       "      <th>EXT7</th>\n",
       "      <th>EXT8</th>\n",
       "      <th>EXT9</th>\n",
       "      <th>EXT10</th>\n",
       "      <th>EST1</th>\n",
       "      <th>EST2</th>\n",
       "      <th>EST3</th>\n",
       "      <th>EST4</th>\n",
       "      <th>EST5</th>\n",
       "      <th>EST6</th>\n",
       "      <th>EST7</th>\n",
       "      <th>EST8</th>\n",
       "      <th>EST9</th>\n",
       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
       "      <th>AGR2</th>\n",
       "      <th>AGR3</th>\n",
       "      <th>AGR4</th>\n",
       "      <th>AGR5</th>\n",
       "      <th>AGR6</th>\n",
       "      <th>AGR7</th>\n",
       "      <th>AGR8</th>\n",
       "      <th>AGR9</th>\n",
       "      <th>AGR10</th>\n",
       "      <th>CSN1</th>\n",
       "      <th>CSN2</th>\n",
       "      <th>CSN3</th>\n",
       "      <th>CSN4</th>\n",
       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <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)"
   ]
  },
  {
   "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=8, random_state=0, batch_size=100, max_iter=100).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "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": [],
   "source": [
    "six = kmeans.cluster_centers_[5] #personality 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "seven = kmeans.cluster_centers_[6] #personality 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "eight = kmeans.cluster_centers_[7] #personality 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.9273008 , 3.43251539, 2.87866413, 3.84947675, 2.80142347,\n",
       "       2.69509415, 1.99177871, 4.15594428, 2.13715019, 4.29303165,\n",
       "       3.73717891, 2.85820963, 4.33397656, 2.40822847, 2.9060834 ,\n",
       "       2.97093827, 3.01967366, 2.59012009, 3.11987291, 2.98336894,\n",
       "       1.67861566, 3.9647723 , 1.65499291, 4.4530417 , 1.88771114,\n",
       "       4.21991958, 1.960536  , 4.04550432, 4.27859053, 3.70647472,\n",
       "       3.89031395, 2.20653754, 4.40350751, 2.04629414, 3.22602273,\n",
       "       2.02212389, 4.26076576, 1.94747707, 3.87045181, 3.99143765,\n",
       "       3.81397979, 1.92503904, 4.10244305, 1.86423199, 3.82521675,\n",
       "       1.78926206, 4.12075248, 3.23394784, 4.44693856, 3.96952019])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': -4.533863469098309,\n",
       " 'neuroticism_score': 9.168213394604091,\n",
       " 'agreeableness_score': -5.184655980182713,\n",
       " 'conscientiousness_score': -4.533863469098309,\n",
       " 'openness_score': 3.7780250946884646}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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, 'six': six, 'seven': seven, 'eight': eight}\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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': {'extroversion_score': -4.533863469098309,\n",
       "  'neuroticism_score': 9.168213394604091,\n",
       "  'agreeableness_score': -5.184655980182713,\n",
       "  'conscientiousness_score': -4.533863469098309,\n",
       "  'openness_score': 3.7780250946884646},\n",
       " 'two': {'extroversion_score': -7.437051484009709,\n",
       "  'neuroticism_score': 7.161336567232594,\n",
       "  'agreeableness_score': -3.8568416139491037,\n",
       "  'conscientiousness_score': -7.437051484009709,\n",
       "  'openness_score': 0.787787862876002},\n",
       " 'three': {'extroversion_score': -3.5131922874054906,\n",
       "  'neuroticism_score': 9.175877629959581,\n",
       "  'agreeableness_score': -4.917611205532437,\n",
       "  'conscientiousness_score': -3.5131922874054906,\n",
       "  'openness_score': 3.9083631248901227},\n",
       " 'four': {'extroversion_score': 7.165471153986873,\n",
       "  'neuroticism_score': 16.287574475560277,\n",
       "  'agreeableness_score': -8.537783080323786,\n",
       "  'conscientiousness_score': 7.165471153986873,\n",
       "  'openness_score': 12.920122674239844},\n",
       " 'five': {'extroversion_score': 6.534194836377379,\n",
       "  'neuroticism_score': 15.188721324448517,\n",
       "  'agreeableness_score': -7.823715614068215,\n",
       "  'conscientiousness_score': 6.534194836377379,\n",
       "  'openness_score': 11.870567710722023},\n",
       " 'six': {'extroversion_score': -2.509782482486372,\n",
       "  'neuroticism_score': 10.86520300344465,\n",
       "  'agreeableness_score': -6.082565700352233,\n",
       "  'conscientiousness_score': -2.509782482486372,\n",
       "  'openness_score': 4.6935886519332515},\n",
       " 'seven': {'extroversion_score': 0.426787512588119,\n",
       "  'neuroticism_score': 0.9713997985901327,\n",
       "  'agreeableness_score': -0.7228600201409886,\n",
       "  'conscientiousness_score': 0.426787512588119,\n",
       "  'openness_score': 0.6700906344410898},\n",
       " 'eight': {'extroversion_score': 4.579531787832292,\n",
       "  'neuroticism_score': 14.766982540538326,\n",
       "  'agreeableness_score': -7.7214469373367285,\n",
       "  'conscientiousness_score': 4.579531787832292,\n",
       "  'openness_score': 10.531342014680884}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_types_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19881414, 0.        , 0.26871105, 1.        , 0.95676937,\n",
       "       0.33742588, 0.53852606, 0.82291146])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_extroversion_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': {'extroversion_score': 0.19881414238366885,\n",
       "  'neuroticism_score': 0.5351736819990002,\n",
       "  'agreeableness_score': 0.429067192897308,\n",
       "  'conscientiousness_score': 0.19881414238366885,\n",
       "  'openness_score': 0.2537082719579917},\n",
       " 'two': {'extroversion_score': 0.0,\n",
       "  'neuroticism_score': 0.4041437825823333,\n",
       "  'agreeableness_score': 0.5989747346617115,\n",
       "  'conscientiousness_score': 0.0,\n",
       "  'openness_score': 0.009607911885661133},\n",
       " 'three': {'extroversion_score': 0.2687110504039978,\n",
       "  'neuroticism_score': 0.5356740834058223,\n",
       "  'agreeableness_score': 0.46323832581746105,\n",
       "  'conscientiousness_score': 0.2687110504039978,\n",
       "  'openness_score': 0.26434808332976667},\n",
       " 'four': {'extroversion_score': 1.0,\n",
       "  'neuroticism_score': 1.0,\n",
       "  'agreeableness_score': 0.0,\n",
       "  'conscientiousness_score': 1.0,\n",
       "  'openness_score': 1.0},\n",
       " 'five': {'extroversion_score': 0.9567693655911974,\n",
       "  'neuroticism_score': 0.9282553787556349,\n",
       "  'agreeableness_score': 0.09137229640733896,\n",
       "  'conscientiousness_score': 0.9567693655911974,\n",
       "  'openness_score': 0.9143222678840387},\n",
       " 'six': {'extroversion_score': 0.33742587658808393,\n",
       "  'neuroticism_score': 0.6459709041926203,\n",
       "  'agreeableness_score': 0.3141703841565554,\n",
       "  'conscientiousness_score': 0.33742587658808393,\n",
       "  'openness_score': 0.3284479587008705},\n",
       " 'seven': {'extroversion_score': 0.5385260609790584,\n",
       "  'neuroticism_score': 0.0,\n",
       "  'agreeableness_score': 1.0,\n",
       "  'conscientiousness_score': 0.5385260609790584,\n",
       "  'openness_score': 0.0},\n",
       " 'eight': {'extroversion_score': 0.822911463295676,\n",
       "  'neuroticism_score': 0.9007198620352406,\n",
       "  'agreeableness_score': 0.10445862828084743,\n",
       "  'conscientiousness_score': 0.822911463295676,\n",
       "  'openness_score': 0.8049980072053589}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_all_types_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(17,15))\n",
    "\n",
    "all_types = ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight']\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(5,2,n)\n",
    "    plt.ylim(0, 1)\n",
    "    plt.bar(list(normalized_all_types_scores[type].keys()), normalized_all_types_scores[type].values(), color='darkred')\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.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graphs it's clearly evident that the clustered personalities - 'One, Three' and 'Four, Five, Eight' have less to no variations. Hence we can say that the theory behind having 5 personality traits in the Big Five test is quite justified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "scatter() missing 1 required positional argument: 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-f19243c560ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \t\t\t\tcluster_std = 1, n_features = 2)\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkmeans\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;34m'b'\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     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m# label the axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: scatter() missing 1 required positional argument: 'y'"
     ]
    }
   ],
   "source": [
    "#Getting unique labels\n",
    " \n",
    "u_labels = np.unique(label)\n",
    " \n",
    "#plotting the results:\n",
    " \n",
    "for i in u_labels:\n",
    "    plt.scatter(df[label == i , 0] , df[label == i , 1] , label = i)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 6 0 1 6 2 4 3 7 3 4 3 3 0 3 5 2 6 1 1 2 7 2 2 7 3 7 0 6 6 4 4 4 6 4 6 3\n",
      " 7 2 2 0 6 2 0 6 1 7 6 6 4 2 1 7 7 6 5 4 2 0 4 6 0 6 0 3 1 0 3 6 6 4 7 7 6\n",
      " 4 3 7 2 2 1 7 2 5 7 7 1 4 7 1 3 6 7 1 2 7 5 1 1 3 2]\n"
     ]
    }
   ],
   "source": [
    "label = kmeans.fit_predict(X)\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2230072a8e0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(one)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}