TBF Personality System.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas library import \n",
    "import pandas as pd\n",
    "\n",
    "# dataframe creation \n",
    "df = pd.read_csv('data-final.csv', delimiter=\"\\t\")\n",
    "\n",
    "# (note: specify path of dataset file if no compiled directory)\n",
    "# df = pd.read_csv(r'/Users/.../data-final.csv', delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></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",
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       "      <td>54.9119</td>\n",
       "      <td>-1.3833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-1.25</td>\n",
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       "      <td>2</td>\n",
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       "      <td>US</td>\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",
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       "\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the 50 columns that represent the personality test questions (found in codebook.txt)\n",
    "columns = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXT1\n",
      "EXT2\n",
      "EXT3\n",
      "EXT4\n",
      "EXT5\n",
      "EXT6\n",
      "EXT7\n",
      "EXT8\n",
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      "EST2\n",
      "EST3\n",
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      "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy library import\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjusts the dataframe from 110 columns to 50 (number of rows remains the same)\n",
    "full_dataframe = df[df.columns[0:50]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# displays the new setup of the dataframe's columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EXT1</th>\n",
       "      <th>EXT2</th>\n",
       "      <th>EXT3</th>\n",
       "      <th>EXT4</th>\n",
       "      <th>EXT5</th>\n",
       "      <th>EXT6</th>\n",
       "      <th>EXT7</th>\n",
       "      <th>EXT8</th>\n",
       "      <th>EXT9</th>\n",
       "      <th>EXT10</th>\n",
       "      <th>EST1</th>\n",
       "      <th>EST2</th>\n",
       "      <th>EST3</th>\n",
       "      <th>EST4</th>\n",
       "      <th>EST5</th>\n",
       "      <th>EST6</th>\n",
       "      <th>EST7</th>\n",
       "      <th>EST8</th>\n",
       "      <th>EST9</th>\n",
       "      <th>EST10</th>\n",
       "      <th>AGR1</th>\n",
       "      <th>AGR2</th>\n",
       "      <th>AGR3</th>\n",
       "      <th>AGR4</th>\n",
       "      <th>AGR5</th>\n",
       "      <th>AGR6</th>\n",
       "      <th>AGR7</th>\n",
       "      <th>AGR8</th>\n",
       "      <th>AGR9</th>\n",
       "      <th>AGR10</th>\n",
       "      <th>CSN1</th>\n",
       "      <th>CSN2</th>\n",
       "      <th>CSN3</th>\n",
       "      <th>CSN4</th>\n",
       "      <th>CSN5</th>\n",
       "      <th>CSN6</th>\n",
       "      <th>CSN7</th>\n",
       "      <th>CSN8</th>\n",
       "      <th>CSN9</th>\n",
       "      <th>CSN10</th>\n",
       "      <th>OPN1</th>\n",
       "      <th>OPN2</th>\n",
       "      <th>OPN3</th>\n",
       "      <th>OPN4</th>\n",
       "      <th>OPN5</th>\n",
       "      <th>OPN6</th>\n",
       "      <th>OPN7</th>\n",
       "      <th>OPN8</th>\n",
       "      <th>OPN9</th>\n",
       "      <th>OPN10</th>\n",
       "    </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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outputs the complete dataframe\n",
    "full_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fills in any empty values in the dataframe's columns with a zero\n",
    "full_dataframe = full_dataframe.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the mini batch k-means algorithm (fro a scalar library)\n",
    "from sklearn.cluster import MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kmeans parameters\n",
    "# n_clusters = number of personality types (10)\n",
    "# batch_size = number of data rows fed into the kmeans algorithm/per iteration \n",
    "# (10,000 total batches out of 1,000,000 total rows)\n",
    "# max_iter = iterates through all the batches 100 times\n",
    "\n",
    "kmeans = MiniBatchKMeans(n_clusters = 10, random_state = 0, batch_size = 100, max_iter = 100).fit(full_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double checking that the total number of clusters matches our kmeans parameter\n",
    "len(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementing KMeans Clustering on the 10 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_one = kmeans.cluster_centers_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_two = kmeans.cluster_centers_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_three = kmeans.cluster_centers_[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_four = kmeans.cluster_centers_[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_five = kmeans.cluster_centers_[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_six = kmeans.cluster_centers_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_seven = kmeans.cluster_centers_[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_eight = kmeans.cluster_centers_[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_nine = kmeans.cluster_centers_[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_ten = kmeans.cluster_centers_[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outputs the KMeans Clustering on the 10 clusters, each array will represent that\n",
    "# cluster's Personality Type typical answer score for all 50 questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.51875306, 1.8936703 , 4.45234737, 2.11922512, 4.35885553,\n",
       "       1.56325379, 3.98647132, 2.76422671, 3.81405256, 2.3716646 ,\n",
       "       2.05604329, 3.97875709, 2.92127457, 3.4824271 , 1.8897844 ,\n",
       "       1.67228923, 1.87329092, 1.47554762, 1.86143174, 1.5434501 ,\n",
       "       1.79666676, 4.49912207, 1.70769408, 4.29999136, 1.7910538 ,\n",
       "       3.85187531, 1.50729685, 4.16565441, 4.14265565, 4.31282922,\n",
       "       3.87432716, 2.42716099, 4.31288679, 1.68691172, 3.30911603,\n",
       "       2.07210501, 3.97875709, 1.76252842, 3.69278374, 4.00077718,\n",
       "       4.0497395 , 1.58055324, 4.18154342, 1.64966466, 4.2821738 ,\n",
       "       1.53360583, 4.44892202, 3.33657638, 4.18433551, 4.42462796])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.48116676, 4.02453419, 2.02357631, 4.39140376, 1.97410623,\n",
       "       3.29533727, 1.44507617, 4.41009795, 1.77628774, 4.68572135,\n",
       "       4.16827859, 2.48407132, 4.53635324, 1.91178197, 3.32849241,\n",
       "       3.57843834, 3.6944659 , 3.41596885, 3.72026697, 3.97790687,\n",
       "       2.10348237, 3.41139573, 2.25572413, 4.05493928, 2.36856904,\n",
       "       3.94787257, 2.58226988, 3.53233631, 3.83889009, 3.06244786,\n",
       "       2.97222136, 3.2692272 , 4.01968297, 3.27590149, 2.1016284 ,\n",
       "       3.30352563, 3.66257763, 2.86092142, 2.84117665, 3.48635788,\n",
       "       3.88962704, 1.94685289, 4.33640268, 1.75360751, 3.6229954 ,\n",
       "       1.73877576, 3.92559404, 3.48592529, 4.51741186, 4.01288508])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83021807, 0.65420561, 0.54672897, 0.38369678, 0.2933541 ,\n",
       "       0.17705088, 0.13862928, 0.13032191, 0.14485981, 0.16770509,\n",
       "       0.75233645, 0.5529595 , 0.5046729 , 0.30218069, 0.21079958,\n",
       "       0.13136033, 0.11007269, 0.12201454, 0.14537902, 0.16303219,\n",
       "       0.73727934, 0.72533749, 0.38110073, 0.41277259, 0.22897196,\n",
       "       0.19418484, 0.11370717, 0.13291796, 0.15160955, 0.14330218,\n",
       "       0.79023884, 0.56074766, 0.54932503, 0.30477674, 0.20560748,\n",
       "       0.13603323, 0.12253375, 0.11682243, 0.14953271, 0.192108  ,\n",
       "       0.69574247, 0.4854621 , 0.45482866, 0.26012461, 0.21858775,\n",
       "       0.12045691, 0.13343718, 0.13239875, 0.15212876, 0.24091381])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.43633717, 2.11564992, 3.9493735 , 2.44801291, 3.9587394 ,\n",
       "       2.10783445, 3.60868877, 2.87245285, 3.52101   , 2.86666245,\n",
       "       3.29787369, 3.15105683, 3.80904316, 2.76585242, 2.92190862,\n",
       "       2.85229718, 3.02680041, 2.5958423 , 3.20155676, 2.31980129,\n",
       "       2.69443741, 3.94598785, 2.29866473, 3.79002658, 2.35115808,\n",
       "       3.61808632, 2.09002025, 3.67542083, 3.71190356, 3.71291609,\n",
       "       3.70665106, 2.23867232, 3.99601316, 2.11239084, 3.31252373,\n",
       "       2.13643843, 3.96070118, 2.26604227, 3.77183268, 3.6715922 ,\n",
       "       3.13488799, 2.58508417, 3.51227693, 2.54888622, 3.67886976,\n",
       "       2.25405012, 3.78803316, 2.65191115, 3.64140615, 3.70456271])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_four"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.13030631, 3.22436219, 3.22019365, 3.61747091, 3.10785426,\n",
       "       2.46803683, 2.26657521, 4.01807245, 2.36403451, 4.08585072,\n",
       "       3.33907606, 3.17605467, 4.10273593, 2.67883809, 2.60628446,\n",
       "       2.54937604, 2.62427776, 2.15608263, 2.73374139, 2.51447115,\n",
       "       1.64818088, 4.10112656, 1.57660344, 4.46938237, 1.82333852,\n",
       "       4.19140965, 1.82779727, 4.10094188, 4.29082131, 3.86001108,\n",
       "       3.93755112, 2.17613382, 4.41846292, 1.86330897, 3.31287761,\n",
       "       1.94955544, 4.2356014 , 1.84220246, 3.88562911, 4.02071076,\n",
       "       3.86151492, 1.83993351, 4.10885682, 1.80370947, 3.92955703,\n",
       "       1.71614384, 4.18753133, 3.23488906, 4.42307997, 4.05456033])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.88937956, 2.32378089, 3.22794325, 3.09001221, 3.6427699 ,\n",
       "       2.02680948, 3.02173573, 3.28196937, 3.25666322, 3.68110495,\n",
       "       4.43693821, 2.35372232, 4.68743149, 2.06414232, 3.71812459,\n",
       "       4.14369382, 4.30267155, 4.10072348, 4.14447681, 3.94378152,\n",
       "       1.8712769 , 4.25472141, 2.53922766, 4.46932256, 1.81900467,\n",
       "       4.32578534, 1.84127282, 4.04510007, 4.42704125, 3.79767609,\n",
       "       3.09781077, 3.46988631, 4.1105578 , 3.37880923, 2.31404053,\n",
       "       3.4165179 , 3.77741238, 2.8217921 , 3.15872718, 3.65254158,\n",
       "       3.91427856, 2.06213787, 4.44708572, 1.83256601, 3.96573648,\n",
       "       1.65523505, 4.00538695, 3.56929437, 4.51758589, 4.28572771])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.59374497, 1.76441715, 4.12931173, 2.261869  , 4.19829945,\n",
       "       1.68563918, 3.9018293 , 2.6257175 , 3.8923341 , 2.64857036,\n",
       "       3.18893836, 3.43109275, 3.84056649, 2.67603669, 2.82243442,\n",
       "       2.75181053, 3.16780216, 2.68853602, 2.93769111, 2.56008261,\n",
       "       1.83265383, 4.49120219, 2.35628453, 4.35464836, 1.77149831,\n",
       "       4.05109704, 1.59189421, 4.04034118, 4.23609248, 4.04782469,\n",
       "       2.74191299, 3.9467035 , 3.65672442, 3.19580495, 1.98487206,\n",
       "       3.80038625, 3.13432756, 2.76428303, 2.65790462, 3.34252454,\n",
       "       3.8028271 , 1.90464567, 4.29266134, 1.78954992, 4.01461831,\n",
       "       1.64409098, 4.0398852 , 3.35620407, 4.15262057, 4.27911593])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_seven"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.17928221, 3.36957675, 2.6700942 , 3.63155101, 2.5923776 ,\n",
       "       3.38184954, 2.12518243, 3.74326655, 2.42719252, 4.15918137,\n",
       "       3.99625182, 2.73776038, 4.21629959, 2.65719119, 3.58202866,\n",
       "       3.72960064, 3.77636991, 3.49767812, 3.85710495, 3.34423511,\n",
       "       2.88785326, 3.23404538, 2.52567334, 3.62873159, 2.73122595,\n",
       "       3.77202468, 2.8175335 , 3.35819955, 3.48096059, 3.09788377,\n",
       "       2.90921454, 2.94404272, 3.49469285, 3.13659281, 2.50928751,\n",
       "       3.21649861, 3.50918801, 2.91770598, 3.03943877, 3.1310203 ,\n",
       "       2.61546371, 3.2365331 , 3.27185883, 2.94682898, 2.91156959,\n",
       "       2.88825129, 3.16485339, 2.31408385, 3.68110654, 2.92579939])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_eight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.89171773, 3.74429844, 2.82029819, 3.79866069, 2.40514246,\n",
       "       3.17073094, 1.89178091, 3.97997347, 2.35476025, 4.17351065,\n",
       "       2.19862278, 3.91019647, 3.01203487, 3.18823046, 2.20219218,\n",
       "       1.81815023, 2.01402489, 1.61548424, 2.2772443 , 2.06510203,\n",
       "       2.72780972, 3.10297555, 2.15582159, 3.22793607, 2.98537494,\n",
       "       3.07780024, 2.91354476, 3.18175501, 2.97801504, 3.18314486,\n",
       "       3.41913576, 2.62540274, 3.90946996, 2.15042011, 2.64530293,\n",
       "       2.49946301, 3.69751722, 2.31789753, 3.11718997, 3.55916356,\n",
       "       3.52997663, 2.02056352, 3.60139617, 2.12897214, 3.65102028,\n",
       "       2.15951734, 4.04804473, 2.99121865, 3.8816097 , 3.64088066])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_nine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.59113193, 2.82211328, 2.80578657, 3.20997111, 3.04797339,\n",
       "       2.24879629, 2.4862558 , 3.20992734, 3.32959818, 3.73260089,\n",
       "       3.10702092, 3.24183665, 3.63779217, 2.5325221 , 2.79755756,\n",
       "       2.82080014, 3.3772214 , 3.01667688, 3.56552569, 3.11152937,\n",
       "       3.37744025, 3.06898363, 3.48831305, 2.58172109, 3.47535674,\n",
       "       2.50590913, 3.22791736, 2.7660422 , 2.53260965, 3.0155826 ,\n",
       "       2.9187604 , 3.53024599, 3.83730193, 3.15249934, 2.00240742,\n",
       "       3.48279786, 3.31983717, 3.05834719, 2.49483498, 3.45408387,\n",
       "       4.18970498, 1.69101812, 4.34237941, 1.73921037, 4.17644227,\n",
       "       1.6324083 , 4.27203887, 3.91919811, 4.1898363 , 4.37043684])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_ten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to make this data more comprehensible, we'll compile the 50 scores into the \n",
    "# 5 Factor Model (FFM) of each Personality Cluster Type.\n",
    "\n",
    "# NOTE: the subtraction and addition of specific questions follows the following logic:\n",
    "\n",
    "# subtraction: if a True (+1) answer to this question deters from one of the FFM traits\n",
    "# ie. if person does not talk a lot, extroversion score specifically decreases\n",
    "\n",
    "# addition: if a True (+1) answer to this question compliments one of the FFM traits\n",
    "# ie. if person is the life of the party, extroversion score specifically increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_one_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_one_scores['extroversion_score'] =  cluster_one[0] - cluster_one[1] + cluster_one[2] - cluster_one[3] + cluster_one[4] - cluster_one[5] + cluster_one[6] - cluster_one[7] + cluster_one[8] - cluster_one[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_one_scores['neuroticism_score'] =  cluster_one[0] - cluster_one[1] + cluster_one[2] - cluster_one[3] + cluster_one[4] + cluster_one[5] + cluster_one[6] + cluster_one[7] + cluster_one[8] + cluster_one[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_one_scores['agreeableness_score'] =  -cluster_one[0] + cluster_one[1] - cluster_one[2] + cluster_one[3] - cluster_one[4] - cluster_one[5] + cluster_one[6] - cluster_one[7] + cluster_one[8] + cluster_one[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_one_scores['conscientiousness_score'] =  cluster_one[0] - cluster_one[1] + cluster_one[2] - cluster_one[3] + cluster_one[4] - cluster_one[5] + cluster_one[6] - cluster_one[7] + cluster_one[8] + cluster_one[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_one_scores['openness_score'] =  cluster_one[0] - cluster_one[1] + cluster_one[2] - cluster_one[3] + cluster_one[4] - cluster_one[5] + cluster_one[6] + cluster_one[7] + cluster_one[8] + cluster_one[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 9.418439308022236,\n",
       " 'neuroticism_score': 22.81672951267956,\n",
       " 'agreeableness_score': -2.472352551740031,\n",
       " 'conscientiousness_score': 14.16176851558679,\n",
       " 'openness_score': 19.690221927981366}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_one_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_two_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_two_scores['extroversion_score'] =  cluster_two[0] - cluster_two[1] + cluster_two[2] - cluster_two[3] + cluster_two[4] - cluster_two[5] + cluster_two[6] - cluster_two[7] + cluster_two[8] - cluster_two[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_two_scores['neuroticism_score'] =  cluster_two[0] - cluster_two[1] + cluster_two[2] - cluster_two[3] + cluster_two[4] + cluster_two[5] + cluster_two[6] + cluster_two[7] + cluster_two[8] + cluster_two[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_two_scores['agreeableness_score'] =  -cluster_two[0] + cluster_two[1] - cluster_two[2] + cluster_two[3] - cluster_two[4] - cluster_two[5] + cluster_two[6] - cluster_two[7] + cluster_two[8] + cluster_two[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_two_scores['conscientiousness_score'] =  cluster_two[0] - cluster_two[1] + cluster_two[2] - cluster_two[3] + cluster_two[4] - cluster_two[5] + cluster_two[6] - cluster_two[7] + cluster_two[8] + cluster_two[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_two_scores['openness_score'] =  cluster_two[0] - cluster_two[1] + cluster_two[2] - cluster_two[3] + cluster_two[4] - cluster_two[5] + cluster_two[6] + cluster_two[7] + cluster_two[8] + cluster_two[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': -12.106881315082008,\n",
       " 'neuroticism_score': 12.675431820288606,\n",
       " 'agreeableness_score': 3.1387386830639694,\n",
       " 'conscientiousness_score': -2.735438618175065,\n",
       " 'openness_score': 6.084757284553353}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_two_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_three_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_three_scores['extroversion_score'] =  cluster_three[0] - cluster_three[1] + cluster_three[2] - cluster_three[3] + cluster_three[4] - cluster_three[5] + cluster_three[6] - cluster_three[7] + cluster_three[8] - cluster_three[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_three_scores['neuroticism_score'] =  cluster_three[0] - cluster_three[1] + cluster_three[2] - cluster_three[3] + cluster_three[4] + cluster_three[5] + cluster_three[6] + cluster_three[7] + cluster_three[8] + cluster_three[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_three_scores['agreeableness_score'] =  -cluster_three[0] + cluster_three[1] - cluster_three[2] + cluster_three[3] - cluster_three[4] - cluster_three[5] + cluster_three[6] - cluster_three[7] + cluster_three[8] + cluster_three[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_three_scores['conscientiousness_score'] =  cluster_three[0] - cluster_three[1] + cluster_three[2] - cluster_three[3] + cluster_three[4] - cluster_three[5] + cluster_three[6] - cluster_three[7] + cluster_three[8] + cluster_three[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_three_scores['openness_score'] =  cluster_three[0] - cluster_three[1] + cluster_three[2] - cluster_three[3] + cluster_three[4] - cluster_three[5] + cluster_three[6] + cluster_three[7] + cluster_three[8] + cluster_three[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.4408099688473518,\n",
       " 'neuroticism_score': 1.3909657320872277,\n",
       " 'agreeableness_score': -0.4885773624091373,\n",
       " 'conscientiousness_score': 0.776220145379024,\n",
       " 'openness_score': 1.036863966770509}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_three_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_four_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_four_scores['extroversion_score'] =  cluster_four[0] - cluster_four[1] + cluster_four[2] - cluster_four[3] + cluster_four[4] - cluster_four[5] + cluster_four[6] - cluster_four[7] + cluster_four[8] - cluster_four[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_four_scores['neuroticism_score'] =  cluster_four[0] - cluster_four[1] + cluster_four[2] - cluster_four[3] + cluster_four[4] + cluster_four[5] + cluster_four[6] + cluster_four[7] + cluster_four[8] + cluster_four[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_four_scores['agreeableness_score'] =  -cluster_four[0] + cluster_four[1] - cluster_four[2] + cluster_four[3] - cluster_four[4] - cluster_four[5] + cluster_four[6] - cluster_four[7] + cluster_four[8] + cluster_four[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_four_scores['conscientiousness_score'] =  cluster_four[0] - cluster_four[1] + cluster_four[2] - cluster_four[3] + cluster_four[4] - cluster_four[5] + cluster_four[6] - cluster_four[7] + cluster_four[8] + cluster_four[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_four_scores['openness_score'] =  cluster_four[0] - cluster_four[1] + cluster_four[2] - cluster_four[3] + cluster_four[4] - cluster_four[5] + cluster_four[6] + cluster_four[7] + cluster_four[8] + cluster_four[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 6.063536261232744,\n",
       " 'neuroticism_score': 21.75743576762436,\n",
       " 'agreeableness_score': -1.7647133274269127,\n",
       " 'conscientiousness_score': 11.796861156815591,\n",
       " 'openness_score': 17.5417668649538}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_four_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_five_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_five_scores['extroversion_score'] =  cluster_five[0] - cluster_five[1] + cluster_five[2] - cluster_five[3] + cluster_five[4] - cluster_five[5] + cluster_five[6] - cluster_five[7] + cluster_five[8] - cluster_five[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_five_scores['neuroticism_score'] =  cluster_five[0] - cluster_five[1] + cluster_five[2] - cluster_five[3] + cluster_five[4] + cluster_five[5] + cluster_five[6] + cluster_five[7] + cluster_five[8] + cluster_five[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_five_scores['agreeableness_score'] =  -cluster_five[0] + cluster_five[1] - cluster_five[2] + cluster_five[3] - cluster_five[4] - cluster_five[5] + cluster_five[6] - cluster_five[7] + cluster_five[8] + cluster_five[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_five_scores['conscientiousness_score'] =  cluster_five[0] - cluster_five[1] + cluster_five[2] - cluster_five[3] + cluster_five[4] - cluster_five[5] + cluster_five[6] - cluster_five[7] + cluster_five[8] + cluster_five[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_five_scores['openness_score'] =  cluster_five[0] - cluster_five[1] + cluster_five[2] - cluster_five[3] + cluster_five[4] - cluster_five[5] + cluster_five[6] + cluster_five[7] + cluster_five[8] + cluster_five[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': -4.32482916919505,\n",
       " 'neuroticism_score': 16.819090837136894,\n",
       " 'agreeableness_score': 0.6138300398385099,\n",
       " 'conscientiousness_score': 3.8468722792390917,\n",
       " 'openness_score': 11.883017175421466}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_five_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_six_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_six_scores['extroversion_score'] =  cluster_six[0] - cluster_six[1] + cluster_six[2] - cluster_six[3] + cluster_six[4] - cluster_six[5] + cluster_six[6] - cluster_six[7] + cluster_six[8] - cluster_six[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_six_scores['neuroticism_score'] =  cluster_six[0] - cluster_six[1] + cluster_six[2] - cluster_six[3] + cluster_six[4] + cluster_six[5] + cluster_six[6] + cluster_six[7] + cluster_six[8] + cluster_six[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_six_scores['agreeableness_score'] =  -cluster_six[0] + cluster_six[1] - cluster_six[2] + cluster_six[3] - cluster_six[4] - cluster_six[5] + cluster_six[6] - cluster_six[7] + cluster_six[8] + cluster_six[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_six_scores['conscientiousness_score'] =  cluster_six[0] - cluster_six[1] + cluster_six[2] - cluster_six[3] + cluster_six[4] - cluster_six[5] + cluster_six[6] - cluster_six[7] + cluster_six[8] + cluster_six[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_six_scores['openness_score'] =  cluster_six[0] - cluster_six[1] + cluster_six[2] - cluster_six[3] + cluster_six[4] - cluster_six[5] + cluster_six[6] + cluster_six[7] + cluster_six[8] + cluster_six[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 1.6348147452159485,\n",
       " 'neuroticism_score': 19.614582354599264,\n",
       " 'agreeableness_score': 0.3044254439537828,\n",
       " 'conscientiousness_score': 8.997024648438725,\n",
       " 'openness_score': 15.560963387516054}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_six_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_seven_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_seven_scores['extroversion_score'] =  cluster_seven[0] - cluster_seven[1] + cluster_seven[2] - cluster_seven[3] + cluster_seven[4] - cluster_seven[5] + cluster_seven[6] - cluster_seven[7] + cluster_seven[8] - cluster_seven[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_seven_scores['neuroticism_score'] =  cluster_seven[0] - cluster_seven[1] + cluster_seven[2] - cluster_seven[3] + cluster_seven[4] + cluster_seven[5] + cluster_seven[6] + cluster_seven[7] + cluster_seven[8] + cluster_seven[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_seven_scores['agreeableness_score'] =  -cluster_seven[0] + cluster_seven[1] - cluster_seven[2] + cluster_seven[3] - cluster_seven[4] - cluster_seven[5] + cluster_seven[6] - cluster_seven[7] + cluster_seven[8] + cluster_seven[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_seven_scores['conscientiousness_score'] =  cluster_seven[0] - cluster_seven[1] + cluster_seven[2] - cluster_seven[3] + cluster_seven[4] - cluster_seven[5] + cluster_seven[6] - cluster_seven[7] + cluster_seven[8] + cluster_seven[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_seven_scores['openness_score'] =  cluster_seven[0] - cluster_seven[1] + cluster_seven[2] - cluster_seven[3] + cluster_seven[4] - cluster_seven[5] + cluster_seven[6] + cluster_seven[7] + cluster_seven[8] + cluster_seven[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 8.72930636768416,\n",
       " 'neuroticism_score': 22.649160452765436,\n",
       " 'agreeableness_score': -1.7636929349284136,\n",
       " 'conscientiousness_score': 14.026447079019384,\n",
       " 'openness_score': 19.277882087870847}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_seven_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_eight_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_eight_scores['extroversion_score'] =  cluster_eight[0] - cluster_eight[1] + cluster_eight[2] - cluster_eight[3] + cluster_eight[4] - cluster_eight[5] + cluster_eight[6] - cluster_eight[7] + cluster_eight[8] - cluster_eight[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_eight_scores['neuroticism_score'] =  cluster_eight[0] - cluster_eight[1] + cluster_eight[2] - cluster_eight[3] + cluster_eight[4] + cluster_eight[5] + cluster_eight[6] + cluster_eight[7] + cluster_eight[8] + cluster_eight[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_eight_scores['agreeableness_score'] =  -cluster_eight[0] + cluster_eight[1] - cluster_eight[2] + cluster_eight[3] - cluster_eight[4] - cluster_eight[5] + cluster_eight[6] - cluster_eight[7] + cluster_eight[8] + cluster_eight[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_eight_scores['conscientiousness_score'] =  cluster_eight[0] - cluster_eight[1] + cluster_eight[2] - cluster_eight[3] + cluster_eight[4] - cluster_eight[5] + cluster_eight[6] - cluster_eight[7] + cluster_eight[8] + cluster_eight[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_eight_scores['openness_score'] =  cluster_eight[0] - cluster_eight[1] + cluster_eight[2] - cluster_eight[3] + cluster_eight[4] - cluster_eight[5] + cluster_eight[6] + cluster_eight[7] + cluster_eight[8] + cluster_eight[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': -6.291296271726161,\n",
       " 'neuroticism_score': 16.277298659944307,\n",
       " 'agreeableness_score': 1.145813984343937,\n",
       " 'conscientiousness_score': 2.02706647207115,\n",
       " 'openness_score': 9.513599575427923}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_eight_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_nine_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_nine_scores['extroversion_score'] =  cluster_nine[0] - cluster_nine[1] + cluster_nine[2] - cluster_nine[3] + cluster_nine[4] - cluster_nine[5] + cluster_nine[6] - cluster_nine[7] + cluster_nine[8] - cluster_nine[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_nine_scores['neuroticism_score'] =  cluster_nine[0] - cluster_nine[1] + cluster_nine[2] - cluster_nine[3] + cluster_nine[4] + cluster_nine[5] + cluster_nine[6] + cluster_nine[7] + cluster_nine[8] + cluster_nine[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_nine_scores['agreeableness_score'] =  -cluster_nine[0] + cluster_nine[1] - cluster_nine[2] + cluster_nine[3] - cluster_nine[4] - cluster_nine[5] + cluster_nine[6] - cluster_nine[7] + cluster_nine[8] + cluster_nine[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_nine_scores['conscientiousness_score'] =  cluster_nine[0] - cluster_nine[1] + cluster_nine[2] - cluster_nine[3] + cluster_nine[4] - cluster_nine[5] + cluster_nine[6] - cluster_nine[7] + cluster_nine[8] + cluster_nine[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_nine_scores['openness_score'] =  cluster_nine[0] - cluster_nine[1] + cluster_nine[2] - cluster_nine[3] + cluster_nine[4] - cluster_nine[5] + cluster_nine[6] + cluster_nine[7] + cluster_nine[8] + cluster_nine[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': -7.503474635163293,\n",
       " 'neuroticism_score': 15.144955461494725,\n",
       " 'agreeableness_score': 1.6951481458083189,\n",
       " 'conscientiousness_score': 0.8435466548739594,\n",
       " 'openness_score': 8.80349358771874}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_nine_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_ten_scores = {}\n",
    "\n",
    "# EXTROVERSION SCORE\n",
    "cluster_ten_scores['extroversion_score'] =  cluster_ten[0] - cluster_ten[1] + cluster_ten[2] - cluster_ten[3] + cluster_ten[4] - cluster_ten[5] + cluster_ten[6] - cluster_ten[7] + cluster_ten[8] - cluster_ten[9]\n",
    "\n",
    "# NEUROTICISM SCORE\n",
    "cluster_ten_scores['neuroticism_score'] =  cluster_ten[0] - cluster_ten[1] + cluster_ten[2] - cluster_ten[3] + cluster_ten[4] + cluster_ten[5] + cluster_ten[6] + cluster_ten[7] + cluster_ten[8] + cluster_ten[9]\n",
    "\n",
    "# AGREEABLENESS SCORE\n",
    "cluster_ten_scores['agreeableness_score'] =  -cluster_ten[0] + cluster_ten[1] - cluster_ten[2] + cluster_ten[3] - cluster_ten[4] - cluster_ten[5] + cluster_ten[6] - cluster_ten[7] + cluster_ten[8] + cluster_ten[9]\n",
    "\n",
    "# CONSCIENTIOUSNESS SCORE\n",
    "cluster_ten_scores['conscientiousness_score'] =  cluster_ten[0] - cluster_ten[1] + cluster_ten[2] - cluster_ten[3] + cluster_ten[4] - cluster_ten[5] + cluster_ten[6] - cluster_ten[7] + cluster_ten[8] + cluster_ten[9]\n",
    "\n",
    "# OPENNESS SCORE\n",
    "cluster_ten_scores['openness_score'] =  cluster_ten[0] - cluster_ten[1] + cluster_ten[2] - cluster_ten[3] + cluster_ten[4] - cluster_ten[5] + cluster_ten[6] + cluster_ten[7] + cluster_ten[8] + cluster_ten[9]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': -0.9626630482359997,\n",
       " 'neuroticism_score': 17.41998599317168,\n",
       " 'agreeableness_score': 1.6769237503282786,\n",
       " 'conscientiousness_score': 6.502538737634612,\n",
       " 'openness_score': 12.922393416790703}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_ten_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instead of manually calculating each cluster's Personality Type's FFM scores, we can automate it as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dictionary of clusters/personality types\n",
    "all_personality_types = {'cluster_one':cluster_one, 'cluster_two': cluster_two, 'cluster_three' :cluster_three, 'cluster_four':cluster_four, 'cluster_five':cluster_five, \n",
    "'cluster_six': cluster_six, 'cluster_seven': cluster_seven, 'cluster_eight': cluster_eight, 'cluster_nine': cluster_nine, 'cluster_ten': cluster_ten}\n",
    "\n",
    "all_personality_types_scores ={}\n",
    "\n",
    "for name, personality_type in all_personality_types.items():\n",
    "    personality_trait = {}\n",
    "    # PERSONALITY TRAIT'S SCORES\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] + personality_type[8] -personality_type[9]\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] + personality_type[8] + personality_type[9]\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] + personality_type[8] + personality_type[9]\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] + personality_type[8] + personality_type[9]\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] + personality_type[8] + personality_type[9]\n",
    "    \n",
    "    # initiates key-value pairs of cluster & scores\n",
    "    all_personality_types_scores[name] = personality_trait\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cluster_one': {'extroversion_score': 9.418439308022236,\n",
       "  'neuroticism_score': 22.81672951267956,\n",
       "  'agreeableness_score': -2.472352551740031,\n",
       "  'conscientiousness_score': 14.16176851558679,\n",
       "  'openness_score': 19.690221927981366},\n",
       " 'cluster_two': {'extroversion_score': -12.106881315082008,\n",
       "  'neuroticism_score': 12.675431820288606,\n",
       "  'agreeableness_score': 3.1387386830639694,\n",
       "  'conscientiousness_score': -2.735438618175065,\n",
       "  'openness_score': 6.084757284553353},\n",
       " 'cluster_three': {'extroversion_score': 0.4408099688473518,\n",
       "  'neuroticism_score': 1.3909657320872277,\n",
       "  'agreeableness_score': -0.4885773624091373,\n",
       "  'conscientiousness_score': 0.776220145379024,\n",
       "  'openness_score': 1.036863966770509},\n",
       " 'cluster_four': {'extroversion_score': 6.063536261232744,\n",
       "  'neuroticism_score': 21.75743576762436,\n",
       "  'agreeableness_score': -1.7647133274269127,\n",
       "  'conscientiousness_score': 11.796861156815591,\n",
       "  'openness_score': 17.5417668649538},\n",
       " 'cluster_five': {'extroversion_score': -4.32482916919505,\n",
       "  'neuroticism_score': 16.819090837136894,\n",
       "  'agreeableness_score': 0.6138300398385099,\n",
       "  'conscientiousness_score': 3.8468722792390917,\n",
       "  'openness_score': 11.883017175421466},\n",
       " 'cluster_six': {'extroversion_score': 1.6348147452159485,\n",
       "  'neuroticism_score': 19.614582354599264,\n",
       "  'agreeableness_score': 0.3044254439537828,\n",
       "  'conscientiousness_score': 8.997024648438725,\n",
       "  'openness_score': 15.560963387516054},\n",
       " 'cluster_seven': {'extroversion_score': 8.72930636768416,\n",
       "  'neuroticism_score': 22.649160452765436,\n",
       "  'agreeableness_score': -1.7636929349284136,\n",
       "  'conscientiousness_score': 14.026447079019384,\n",
       "  'openness_score': 19.277882087870847},\n",
       " 'cluster_eight': {'extroversion_score': -6.291296271726161,\n",
       "  'neuroticism_score': 16.277298659944307,\n",
       "  'agreeableness_score': 1.145813984343937,\n",
       "  'conscientiousness_score': 2.02706647207115,\n",
       "  'openness_score': 9.513599575427923},\n",
       " 'cluster_nine': {'extroversion_score': -7.503474635163293,\n",
       "  'neuroticism_score': 15.144955461494725,\n",
       "  'agreeableness_score': 1.6951481458083189,\n",
       "  'conscientiousness_score': 0.8435466548739594,\n",
       "  'openness_score': 8.80349358771874},\n",
       " 'cluster_ten': {'extroversion_score': -0.9626630482359997,\n",
       "  'neuroticism_score': 17.41998599317168,\n",
       "  'agreeableness_score': 1.6769237503282786,\n",
       "  'conscientiousness_score': 6.502538737634612,\n",
       "  'openness_score': 12.922393416790703}}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_personality_types_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lastly, we normalize all of these clusters' data scores to make them relative to each other & thus, yield \n",
    "# an informative comparison between all of the Personality Types with each type being guided by the FFM.\n",
    "# MIN-MAX NORMALIZATION EQUATION IS USED [v’=(v-min)/(max-min)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total scores for each trait\n",
    "total_extroversion = []\n",
    "total_neuroticism = []\n",
    "total_agreeableness = []\n",
    "total_conscientiousness = []\n",
    "total_openness = []\n",
    "\n",
    "# adds each trait's score to the totalized array\n",
    "for personality_type, personality_trait in all_personality_types_scores.items():\n",
    "    total_extroversion.append(personality_trait['extroversion_score'])\n",
    "    total_neuroticism.append(personality_trait['neuroticism_score'])\n",
    "    total_agreeableness.append(personality_trait['agreeableness_score'])\n",
    "    total_conscientiousness.append(personality_trait['conscientiousness_score'])\n",
    "    total_openness.append(personality_trait['openness_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The total scores for each personality trait (filtered clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9.418439308022236,\n",
       " -12.106881315082008,\n",
       " 0.4408099688473518,\n",
       " 6.063536261232744,\n",
       " -4.32482916919505,\n",
       " 1.6348147452159485,\n",
       " 8.72930636768416,\n",
       " -6.291296271726161,\n",
       " -7.503474635163293,\n",
       " -0.9626630482359997]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_extroversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[22.81672951267956,\n",
       " 12.675431820288606,\n",
       " 1.3909657320872277,\n",
       " 21.75743576762436,\n",
       " 16.819090837136894,\n",
       " 19.614582354599264,\n",
       " 22.649160452765436,\n",
       " 16.277298659944307,\n",
       " 15.144955461494725,\n",
       " 17.41998599317168]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_neuroticism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-2.472352551740031,\n",
       " 3.1387386830639694,\n",
       " -0.4885773624091373,\n",
       " -1.7647133274269127,\n",
       " 0.6138300398385099,\n",
       " 0.3044254439537828,\n",
       " -1.7636929349284136,\n",
       " 1.145813984343937,\n",
       " 1.6951481458083189,\n",
       " 1.6769237503282786]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_agreeableness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14.16176851558679,\n",
       " -2.735438618175065,\n",
       " 0.776220145379024,\n",
       " 11.796861156815591,\n",
       " 3.8468722792390917,\n",
       " 8.997024648438725,\n",
       " 14.026447079019384,\n",
       " 2.02706647207115,\n",
       " 0.8435466548739594,\n",
       " 6.502538737634612]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_conscientiousness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19.690221927981366,\n",
       " 6.084757284553353,\n",
       " 1.036863966770509,\n",
       " 17.5417668649538,\n",
       " 11.883017175421466,\n",
       " 15.560963387516054,\n",
       " 19.277882087870847,\n",
       " 9.513599575427923,\n",
       " 8.80349358771874,\n",
       " 12.922393416790703]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_openness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MIN-MAX NORMALIZATION EQUATION is used [v’=(v-min)/(max-min)] for each personality trait score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_extroversion_normalized = (total_extroversion-min(total_extroversion))/(max(total_extroversion)-min(total_extroversion))\n",
    "total_neuroticism_normalized = (total_neuroticism-min(total_neuroticism))/(max(total_neuroticism)-min(total_neuroticism))\n",
    "total_agreeableness_normalized = (total_agreeableness-min(total_agreeableness))/(max(total_agreeableness)-min(total_agreeableness))\n",
    "total_conscientiousness_normalized = (total_conscientiousness-min(total_conscientiousness))/(max(total_conscientiousness)-min(total_conscientiousness))\n",
    "total_openness_normalized = (total_openness-min(total_openness))/(max(total_openness)-min(total_openness))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# each personality trait's normalized scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.        , 0.58292703, 0.84414155, 0.36153014,\n",
       "       0.63839681, 0.96798501, 0.27017414, 0.21386007, 0.51772601])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_extroversion_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.52667742, 0.        , 0.95055981, 0.72007352,\n",
       "       0.85054688, 0.99217909, 0.69478657, 0.64193696, 0.74811897])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_neuroticism_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.        , 0.35354535, 0.12611437, 0.55001469,\n",
       "       0.49487308, 0.12629622, 0.64482404, 0.74272553, 0.7394776 ])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_agreeableness_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.        , 0.2078248 , 0.86004152, 0.38955023,\n",
       "       0.69434334, 0.99199149, 0.28185161, 0.21180928, 0.54671623])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_conscientiousness_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.2706158 , 0.        , 0.88482208, 0.58145848,\n",
       "       0.77863189, 0.97789461, 0.45443483, 0.4163663 , 0.63717908])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_openness_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# amalgamating all personality traot scores into a normalized_total_types_scores dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_total_types_scores = {}\n",
    "\n",
    "counter = 0\n",
    "\n",
    "# iterates through each personality cluster and adds personality type and normalized traits scores into a dictionary\n",
    "for personality_type, personality_trait in all_personality_types_scores.items():\n",
    "    \n",
    "    normalized_personality_trait ={}\n",
    "    \n",
    "    normalized_personality_trait['extroversion_score'] = total_extroversion_normalized[counter]\n",
    "    normalized_personality_trait['neuroticism_score'] = total_neuroticism_normalized[counter]\n",
    "    normalized_personality_trait['agreeableness_score'] = total_agreeableness_normalized[counter]\n",
    "    normalized_personality_trait['conscientiousness_score'] = total_conscientiousness_normalized[counter]\n",
    "    normalized_personality_trait['openness_score'] = total_openness_normalized[counter]\n",
    "    \n",
    "    # pairs each personality type to its normalized personality traits to a total dict\n",
    "    normalized_total_types_scores[personality_type] = normalized_personality_trait\n",
    "    \n",
    "    # counter incrementer in loop\n",
    "    counter+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# each personality type's final personality traits scores(normalized)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 1.0,\n",
       " 'neuroticism_score': 1.0,\n",
       " 'agreeableness_score': 0.0,\n",
       " 'conscientiousness_score': 1.0,\n",
       " 'openness_score': 1.0}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.0,\n",
       " 'neuroticism_score': 0.5266774246070499,\n",
       " 'agreeableness_score': 1.0,\n",
       " 'conscientiousness_score': 0.0,\n",
       " 'openness_score': 0.2706157962700227}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.5829270329409761,\n",
       " 'neuroticism_score': 0.0,\n",
       " 'agreeableness_score': 0.35354534551605604,\n",
       " 'conscientiousness_score': 0.20782480416763896,\n",
       " 'openness_score': 0.0}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.8441415528469063,\n",
       " 'neuroticism_score': 0.9505598140676451,\n",
       " 'agreeableness_score': 0.1261143679011729,\n",
       " 'conscientiousness_score': 0.8600415240193192,\n",
       " 'openness_score': 0.8848220750657754}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_four']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.361530138488812,\n",
       " 'neuroticism_score': 0.7200735181736958,\n",
       " 'agreeableness_score': 0.5500146874169198,\n",
       " 'conscientiousness_score': 0.3895502283488151,\n",
       " 'openness_score': 0.5814584822317371}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_five']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.6383968118713308,\n",
       " 'neuroticism_score': 0.8505468840751043,\n",
       " 'agreeableness_score': 0.4948730789601586,\n",
       " 'conscientiousness_score': 0.6943433416976627,\n",
       " 'openness_score': 0.7786318930322362}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_six']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.9679850092640017,\n",
       " 'neuroticism_score': 0.992179085813225,\n",
       " 'agreeableness_score': 0.12629622067379756,\n",
       " 'conscientiousness_score': 0.9919914909312427,\n",
       " 'openness_score': 0.9778946053054914}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_seven']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.2701741425915709,\n",
       " 'neuroticism_score': 0.6947865700517648,\n",
       " 'agreeableness_score': 0.6448240430741015,\n",
       " 'conscientiousness_score': 0.2818516132604176,\n",
       " 'openness_score': 0.45443483292844916}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_eight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.2138600748635372,\n",
       " 'neuroticism_score': 0.6419369629131259,\n",
       " 'agreeableness_score': 0.7427255275584419,\n",
       " 'conscientiousness_score': 0.21180927976540873,\n",
       " 'openness_score': 0.41636629914564033}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_nine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'extroversion_score': 0.5177260056644333,\n",
       " 'neuroticism_score': 0.7481189667368449,\n",
       " 'agreeableness_score': 0.7394776039875329,\n",
       " 'conscientiousness_score': 0.5467162284678114,\n",
       " 'openness_score': 0.6371790792165047}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores['cluster_ten']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ALL of the personality type's final personality traits scores combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cluster_one': {'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",
       " 'cluster_two': {'extroversion_score': 0.0,\n",
       "  'neuroticism_score': 0.5266774246070499,\n",
       "  'agreeableness_score': 1.0,\n",
       "  'conscientiousness_score': 0.0,\n",
       "  'openness_score': 0.2706157962700227},\n",
       " 'cluster_three': {'extroversion_score': 0.5829270329409761,\n",
       "  'neuroticism_score': 0.0,\n",
       "  'agreeableness_score': 0.35354534551605604,\n",
       "  'conscientiousness_score': 0.20782480416763896,\n",
       "  'openness_score': 0.0},\n",
       " 'cluster_four': {'extroversion_score': 0.8441415528469063,\n",
       "  'neuroticism_score': 0.9505598140676451,\n",
       "  'agreeableness_score': 0.1261143679011729,\n",
       "  'conscientiousness_score': 0.8600415240193192,\n",
       "  'openness_score': 0.8848220750657754},\n",
       " 'cluster_five': {'extroversion_score': 0.361530138488812,\n",
       "  'neuroticism_score': 0.7200735181736958,\n",
       "  'agreeableness_score': 0.5500146874169198,\n",
       "  'conscientiousness_score': 0.3895502283488151,\n",
       "  'openness_score': 0.5814584822317371},\n",
       " 'cluster_six': {'extroversion_score': 0.6383968118713308,\n",
       "  'neuroticism_score': 0.8505468840751043,\n",
       "  'agreeableness_score': 0.4948730789601586,\n",
       "  'conscientiousness_score': 0.6943433416976627,\n",
       "  'openness_score': 0.7786318930322362},\n",
       " 'cluster_seven': {'extroversion_score': 0.9679850092640017,\n",
       "  'neuroticism_score': 0.992179085813225,\n",
       "  'agreeableness_score': 0.12629622067379756,\n",
       "  'conscientiousness_score': 0.9919914909312427,\n",
       "  'openness_score': 0.9778946053054914},\n",
       " 'cluster_eight': {'extroversion_score': 0.2701741425915709,\n",
       "  'neuroticism_score': 0.6947865700517648,\n",
       "  'agreeableness_score': 0.6448240430741015,\n",
       "  'conscientiousness_score': 0.2818516132604176,\n",
       "  'openness_score': 0.45443483292844916},\n",
       " 'cluster_nine': {'extroversion_score': 0.2138600748635372,\n",
       "  'neuroticism_score': 0.6419369629131259,\n",
       "  'agreeableness_score': 0.7427255275584419,\n",
       "  'conscientiousness_score': 0.21180927976540873,\n",
       "  'openness_score': 0.41636629914564033},\n",
       " 'cluster_ten': {'extroversion_score': 0.5177260056644333,\n",
       "  'neuroticism_score': 0.7481189667368449,\n",
       "  'agreeableness_score': 0.7394776039875329,\n",
       "  'conscientiousness_score': 0.5467162284678114,\n",
       "  'openness_score': 0.6371790792165047}}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_total_types_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we can visually represent each personality type into a graph!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing Numpy and MatPlotLib libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1st personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_one'].keys()), normalized_total_types_scores['cluster_one'].values(), color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2nd personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_two'].keys()), normalized_total_types_scores['cluster_two'].values(), color='orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3rd personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_three'].keys()), normalized_total_types_scores['cluster_three'].values(), color='yellow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zv6rqZeP8c6vqbouvKgAAAADbaoshUFVtSPLKJA9OckCSx1bVAcuKPTjJ/uPriCSvXnA9AQAAANgOa+kJdHCSTd19UXd/P8lxSQ5bVuawJG/swRlJdquq2y64rgAAAABso7WEQHsm+dLM+CXjtK0tAwAAAMA62WkNZWrOtN6GMqmqIzLcLpYkl1fVhWv4fKZn9ySXrXcldiT17HlNlInQnhZIW5o0bWnBtKdJ054WSFuaNG1pgXawtnT7lWasJQS6JMneM+N7JfnKNpRJdx+d5Og1fCYTVlVndffG9a4H7Ai0J1gMbQkWR3uCxdCW2BZruR3szCT7V9V+VXWjJI9JctKyMiclefz4K2H3SvKt7v7qgusKAAAAwDbaYk+g7r6iqp6a5JQkG5Ic090XVNWR4/yjkpyc5CFJNiX5bpInXnNVBgAAAGBrreV2sHT3yRmCntlpR80Md5KnLLZqTJhbBmFxtCdYDG0JFkd7gsXQlthqNeQ3AAAAAOzI1vJMIAAAAACu54RAAAAAwFarqjU9YobrDiEQSZKq2reqfmO967Gkqj6y3nWA9VZVd62qh8yMP6yqnrGF92g7MKOqDq+qV6ww7/Jruz6w3tZyLFnlvbtV1X+fGb9dVb1tcbWD66aq+sOqOn98/f74t9O/VtUbqurcqnpbVd1sLHv3qvpgVZ1dVadU1W3H6adV1Qur6uNV9dmquu84/fCqOr6q3lNVn6uqv5r53AdW1Uer6hNV9daq2mWc/oKq+vT42S8ep/3aWL9zqur0VdblTmMdPjW+f/9x+uPH8XOq6k3jtNtX1anj9FOrap9x+rFV9TdV9YEkL6yqnxjrf3ZVfaiq7niN/EewEJ4JRJKkqg5J8sfd/ctz5u3U3VdcA595jSz3mlZVlaHt/HC968L1X1Vt6O4rV5h3eJKN3f3Ua7dW17zra/vn2rHI7WO1dlRVl3f3Lov4nOsi7YxFq6p9k7yru++83nW5rlrtuM71U1XdPcmxSe6VpJJ8LMlvJvlEkvt094er6pgkn07y0iQfTHJYd19aVY9O8qDuflJVnZbk7O7+o/Ei3x929wPG49RfJPmZJN9LcmGS+yT5jyTHJ3lwd3+nqp6e5MZJXpHko0nu2N1dVbt1979X1XlJDu3uLy9NW2F9Xp7kjO5+c1XdKMMvgP/4+Fk/192XVdWtuvsbVfXOJG/r7jdU1ZOSPKy7H15VxybZfVzPK6vq1CRHdvfnquqeSZ7f3fdbyH8AC6cn0A6uqn5zJul9TVXdc0xyb1JVO1fVBVV15yQvSHLfsdwfjIn0W8eG/96qulVVnTi+94yqOrCqblBVn6+q3WY+b1NV3aaq9qiqt1fVmePr58b5z66qo6vqvUneuEoSffn4b1XVi8ZU+7xxR5qqOqSGNP1tNaTwbx7DmZW+h3lp+W2q6oQx7T6nqn52nH61pH+ctm9VfaaqXpVhh793Vf3JuG7nVtVzFv1/x3XPzHbw2rHtvLeqbrrS1Y8arpI8aub9S9v1IVX1gap6S5Lzxvb4+nEb/2RV/eJ4UH5ukkeP7ePRNdOjYZXtd+kzbltVp4/vPb+uutp0eQ1Xoc6uqn+uqoPHtnRRVT1slXV31YitVsNx4+yxvRwxTvvtGq6Anja2paVtek3bR618fDm4qj4ytqGPVNVPzVRl73FZF1bVs1ao62b79JXa/DhvpfptdiV2pfYzpw47V9W7x/eeX1cd8+4xrtM543JuPm+/MZZdfvzeuaqOGdftk1V12Hb+t3INWL4v3cJ+9GXj9nBRjceYWnmff2gNPQjOqeGPtKv1jlulPT173G6Wjg9PG6v6giQ/MX7Oi8Y2cv74ntW2yVfMrOu7ajgObhjXZ+kc7w/G+Sv1ltgwfuZSO/1vK637Sste4bt/Wl11jnjcOG2XmXU5t6oeOU5/7Djt/Kp64cwyLq+q51bVx5LcuzY//96wnZsI6+s+SU7o7u909+UZwpL7JvlSd394LPN3Y7mfSnLnJO+rqk8leWaSvWaWdfz479lJ9p2Zfmp3f6u7/zNDmHT7DKHTAUk+PC7rCeP0byf5zySvq6pfTfLdcRkfTnJsVf3XDMHOSj6a5H/UECrdvrv/I8n9MoQ9lyVJd39jLHvvJG8Zh980ruOSt44B0C5JfjbJW8d6vibJbVf5fNZbd3vtoK8kP53knUluOI6/Ksnjk/xlkhcneWWSPxvnHZLhys7Sew9PckmSW43jL0/yrHH4fkk+NQ6/NMkTx+F7JvnncfgtGZLxJNknyWfG4Wdn2OnddGa5jxuHbzQz/fLx30cmeV+GHdltknwxw07lkCTfyrBTvUGGndl9VvgebpUhUV/q+bbb+O8/JPn9cXhDkl2T3D3JeUl2TrJLkgsypPL7JvlhknuN5R+Y4ScZa/z8dyX5+fX+P/e6xtvUvkmuSHLXcfwfM1wJOjXJ/uO0eyZ5/zh8bJJHzbx/abs+JMl3kuw3jv9RktePw3cct/ObjO3wFTPv/9H4vO132Wf8UZI/n5l/83G4M1xRSpITkrw3yQ2THJSxXa+w7pu11SR3GtvW7uP0pf3FO5M8YRx+UpITZ76PdyXZMI7P/d68dpzXzDZx0yTnJ9kzyefH/fINk3xoZpte0/aRlY8vt0iy0zj8gCRvH4cPT/LVJLeeqcfGcd5Se5m7T88KbX4L9TsvyZ7j8G7jv3OPdXO+r0cmee3M+K5j+YuS3GN2PbP6fmP2+P2/Z+q8W5LPJtl5vbcNr6v9v2+2L93CfvSt43Z6QJJN4/TN9vlJ9kjypVx1rFnaJg6faXerna99JEOvg92TfD1Dm903yfkzdf/R+Ba2ydlj2bsyHAfvnuR9M9OX2stpSf56HH5Irjq3PCLJM8fhGyc5K8l+K6z73GWv8P1/JcmNl9XhhUleMlPmlkluN67THmMbfH+Sh4/zO8mvj8Nzz7/Xezvz2q42+vtJnjsz/rwkT0vyhZlp98twXnWXJB9dYTmn5arjz+5JPj8Or9RGfiXJ36+wrBuP7eONmTl/ynA8em6Gtn/rVdbpJ8Z1uGis+9OS/OWccpfNbMs3THLpOHxsxnPcDMelr673/5PX2l8e4rRju3+Gg+CZNXSSuWmSr2XYMZyZIUF+2orvHg6eSynwfTKcnKa7319Vt66qXTP8IfoXSV6f5DHjeDKcgB9QV3XOuUVV3XwcPqmHxDkZwps/r6q9khzf3Z9bVof7ZNj5XZnk/1bVB5PcI0MC/vHuviRJxtR53yT/Mmc9ZtPyd2fYsSbDDu/x4zpdmeRbVfWjpH9c7lLSf1KGHf0Z43sfOL4+OY7vkmT/JCvef8sO4+Lu/tQ4vHQVZ+nqx1KZG69hOR/v7ovH4ftk+CMx3f2vVfWFJHfYwvs3236XzT8zyTFVdcMMfzws1fn7Sd4zDp+X5Hvd/YMauhDvu8rnbdZWq2q1q0a/Og6/KclfzSxn3lWjpXlr+d64fnlaVT1iHN47yW8l+eDStlJVb83Vt/W1bB8rHV92TfKGGnrZdIaT1SXv6+6vj595fIY2d9bM/JX26V/MnDa/hfotXYn9x1x1xXdLx7ol5yV58djD4F3d/aGqukuGk+szk6S7vz2ux2r7jdnj9wOTPKyq/ngcv0nGP/ZXqAPXvs32pVW12n70xB5uSf90Vd1mnLbZPr+GW/1PXzrWzGwTs1Y7X3t3d38vyfeq6msZLsatZmuPZRcl+fEabk15d4aLEkvm9ZZ4YJID66oetrtmaKfz1n21ZS93bpI3V9WJSU4cpz0gw3ltxvX5ZlX9fJLTuvvSJKmqN2cIi09McmWSt4/FVzr/5vrr9Az79RdkuFjwiAzHs5dW1b27+6NJHpvh75ALk+yxNH3cLu/Q3Rdsw+eekeSVVfWT3b2phmcO7ZUhuLxZd59cVWck2ZQMPVS7+2NJPlZVv5LhuPv15Qutqh9PclF3v2wcPjDDRfcTqupvu/vrNd4OliEMfkyG/dDjMudvre7+dlVdXFW/1t1vrWHDP7C7z9mGdeZaIATasVWSN3T3n11tYtV/yXCCe8MMJ4PfWeH9s9Pn3WrVGU5sf7Kq9kjy8Ay9jJLhCtW9Z8Kepc++2nK7+y1j19mHJjmlqn6nu9+/hc9d8r2Z4Suzwvbc3VdU1cEZDsqPSfLUDCdc86z2ecu/j+d392tWKc+Oafl2d5sk/97dd51T9oqMt92OB8QbzczbUvvaLt19+njC+tAkb6qqF3X3G5P8oHu4bJOhd9v3xvI/rFV+3WFeWx3r3Su9Z/btM8NL632DrPy9sQMY/wB9QIZjwXdreBbChRmukq9kLdvHSseXlyf5QHc/oobnlpw2M3v5drp8fO4+fVzO8jZ/09Xq191H1vA8hIcm+VRV3XUNx7ql9362hmdPPCTJ82u4dfrEOfVdqvNKlu9fHtndF65SnvW1ln3p7PzZbbKS+fv8JP++huWudr62pvOs5XWZ40fHwtFNxjp/s6oOSvKgJE9J8usZej3Nfvbs51aS3+3uUzb74DnHu1WWvdxDM4Q5D0vyP6vqTpn/f7Jam/vPvuo5QHPPv7n+6u5P1PAMnI+Pk16X5JsZwvQnVNVrknwuyau7+/tjUPmy8YL5TklekuHugq393EtreF7Q31fV0oWGZyb5f0neUVU3ybC9Ld3u+KLxQkhl6K26Ugjz6CS/WVU/SPJvGXo5faOq/leSD1bVlRkuihyeocPAMVX1J0kuTfLEFZb5uCSvrqpnZvgb87hVPp915plAO7ZTkzyqqn4sSWp4rs/tM3R5/59J3pyhu2sy7ExuPncpg9MzNO6lE/vLuvvb4x+TJyT5mwxdiJfS5vdmCFsyvueu8xY6m0Rn6G1z4JzPfXQN93bvkeEg/fFshfGK7a7dfXKG7pxLdTk1yZPHMhuq6hbj5z28qm5WVTtnSPo/NGexpyR5Ul31hP49l75nJufbSS6uql9LfvQcq4PGeZ/PcDUwSQ7L1XsmzJptX3fIcJX+wqzeLudtvz8ytvWvdfdrk/yfJHfb6jW7+vLmtdVTk/x6Vd16LHOrsfjSVaNklatGWfl7Y8ewa5JvjgHQHTM82+BmSX6hqm45ho6PnPfGLWwfKx1fdk3y5XH48GWL/KXxGHjTDBcsPrxs/lbt01er39KV2O7+iwzd6Pdew7FuaV1ul+S73f13GW7bvluSf01yu6q6x1jm5uN3t9J+Y7lTkvzuGESnqn5mpfVi3czbl25xPzprhX3+RzO0t/1mlrvcms7XZqx2XFppm/x8krvW8CzJvZMcPJbZPckNuvvtGc5Lt3ScOiXJk2voWZGqukMNz7zabN3XuuyqukGSvbv7A0n+NMMtk7tk8+/llhkeBvwLVbV7Dc/4eWyGBwAvt9L5N9dj3f033X3n8fWScfIPu/vI7j6wux/Z3d8dy36qu3++uw/q7juN22a6+5DuPmscvqy79x2Hj+2ZHy/o7l/u7tPG4fd39z3Gzziwu0/q7q9298Hj+F26+w1j2V8dx+/c3b83c9Fv+bo8f6zXXbv70KVegt39hvG9B3X34eO0z3f3/cbPun93f3Gcfnh3v21mmRePyzqouw/o7ucu7Mtn4fQE2oF196fHNPa940HuB0nekeSK8arkhiQfqeGWjg8luaKqzslwj+c3ly3u2UleX1XnZnj42BNm5v1Dhq64h89Me1qG7ovnZtjOTk9y5JxqbpZEL5t/QoZbS87JcEXmT7v732rrHiB788xPy38vydFV9dsZrjQ9uYdum8dmJunv7k/WcDX4R7r7vVX100k+Op5XX57h2TC6+07TSlc/Xpth2/t4hpPClXrdvSrJUTXcknVFksO7+3s1PCD3GTXc7vj8Ze/ZbPvNcMK/5JAkfzK2rcsz3jq2HVw1Ymu9J8mR43Hgwgzd2r+c4Rk1H8vQnf3T2fxWxiUrbR8rHV/+KsPtYH+Y4Vkds/4lQ1f2n0zylqWT8CWr7NNX+4Wfleo370rsM7L6sW7JXcb3/zDDMfvJ41XlRyd5+Rhi/UeGHlYr7TeWL/N5Ga5CnzsGQZ9PstkvgbJ+uvuCOfvSte5HlxySZfv8sRfBEUmOH88Dv5bkl5a9b63na0t1/XpVfbiGh0H/U4bnSy5ZaZv8cJKLM9zueH6GH9hIhmeEvX6sW5JsqefM6zLcGvaJcVu+NEOou9m6b8WyNyT5uxp6bFSSv+3hV5b+cvxezs+wH3hOdx9fVX+W5ANj2ZO7+x1zvqN5599PSfKFLawfwLXCT8QDANeaqtqluy8fe7OckOSY7j5hvesFADuSqnpQrrrrY8nF3f2IeeWZDiEQAHCtqaoXZ+jJcpMMt1ys2GUdAIDFEgKxQ6mqEzL8XOisp897iCCwOVeN4Jo3Pvvl1Dmz7j/zbD1ggarqlUl+btnkl3b369ejPgDrRQgEAAAAMAF+HQwAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwAQIgQAAAAAm4P8DaRXP+FPEGEwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_four'].keys()), normalized_total_types_scores['cluster_four'].values(), color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_five'].keys()), normalized_total_types_scores['cluster_five'].values(), color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_six'].keys()), normalized_total_types_scores['cluster_six'].values(), color='indigo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_seven'].keys()), normalized_total_types_scores['cluster_seven'].values(), color='violet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_eight'].keys()), normalized_total_types_scores['cluster_eight'].values(), color='pink')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_nine'].keys()), normalized_total_types_scores['cluster_nine'].values(), color='grey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10th personality type\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim(0, 1)\n",
    "plt.bar(list(normalized_total_types_scores['cluster_ten'].keys()), normalized_total_types_scores['cluster_ten'].values(), color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
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   "execution_count": null,
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   "outputs": [],
   "source": []
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   "execution_count": null,
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   "execution_count": null,
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   "execution_count": null,
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   "execution_count": null,
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   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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