FA KMedoids Cluster 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e41ccc5",
   "metadata": {},
   "source": [
    "# Factor Analysis on K-Medoid clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470cbabc",
   "metadata": {},
   "source": [
    "## Factor Analysis on 5th cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a399e1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q 1</th>\n",
       "      <th>Q 2</th>\n",
       "      <th>Q 3</th>\n",
       "      <th>Q 4</th>\n",
       "      <th>Q 5</th>\n",
       "      <th>Q 6</th>\n",
       "      <th>Q 7</th>\n",
       "      <th>Q 8</th>\n",
       "      <th>Q 9</th>\n",
       "      <th>Q 10</th>\n",
       "      <th>...</th>\n",
       "      <th>Q 28</th>\n",
       "      <th>Q 29</th>\n",
       "      <th>Q 30</th>\n",
       "      <th>Q 31</th>\n",
       "      <th>Q 32</th>\n",
       "      <th>Q 33</th>\n",
       "      <th>Q 34</th>\n",
       "      <th>Q 35</th>\n",
       "      <th>Q 36</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>481 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q 1  Q 2  Q 3  Q 4  Q 5  Q 6  Q 7  Q 8  Q 9  Q 10  ...  Q 28  Q 29  Q 30  \\\n",
       "0      6    5    6    3    6    7    7    1    6     6  ...     2     1     6   \n",
       "1      4    4    3    2    2    5    2    1    1     3  ...     1     4     3   \n",
       "2      6    7    6    5    3    4    1    1    4     1  ...     1     5     5   \n",
       "3      1    1    2    6    2    1    1    1    2     1  ...     2     2     1   \n",
       "4      4    7    4    6    6    4    4    3    3     3  ...     7     1     7   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...   ...  ...   ...   ...   ...   \n",
       "476    5    1    5    3    5    3    1    1    5     2  ...     1     5     2   \n",
       "477    6    7    5    5    6    5    1    1    5     1  ...     1     6     2   \n",
       "478    2    4    3    3    6    2    1    1    4     2  ...     1     5     2   \n",
       "479    5    7    5    5    7    3    2    1    5     1  ...     2     6     2   \n",
       "480    6    1    3    4    4    2    2    1    4     2  ...     1     4     2   \n",
       "\n",
       "     Q 31  Q 32  Q 33  Q 34  Q 35  Q 36  Cluster  \n",
       "0       2     2     6     7     2     4        2  \n",
       "1       4     3     4     3     3     3        1  \n",
       "2       1     6     6     1     6     6        4  \n",
       "3       1     1     2     1     2     2        2  \n",
       "4       1     3     1     3     1     3        3  \n",
       "..    ...   ...   ...   ...   ...   ...      ...  \n",
       "476     1     5     3     1     3     3        4  \n",
       "477     1     5     1     1     2     2        4  \n",
       "478     1     2     2     1     1     1        0  \n",
       "479     1     5     2     1     3     2        1  \n",
       "480     1     4     2     1     1     1        1  \n",
       "\n",
       "[481 rows x 37 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from factor_analyzer import FactorAnalyzer\n",
    "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity, calculate_kmo\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#First the unnecessary columns are removed:\n",
    "\n",
    "df = pd.read_excel('Asansol MRBQ Data.xlsx')\n",
    "df = df.drop(columns = ['TOT2'])\n",
    "\n",
    "df_2 = pd.read_excel('clustered data KMedoids.xlsx')\n",
    "\n",
    "clusters = list(df_2['Cluster'])\n",
    "\n",
    "df['Cluster'] = clusters\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d66930fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q 1</th>\n",
       "      <th>Q 2</th>\n",
       "      <th>Q 3</th>\n",
       "      <th>Q 4</th>\n",
       "      <th>Q 5</th>\n",
       "      <th>Q 6</th>\n",
       "      <th>Q 7</th>\n",
       "      <th>Q 8</th>\n",
       "      <th>Q 9</th>\n",
       "      <th>Q 10</th>\n",
       "      <th>...</th>\n",
       "      <th>Q 28</th>\n",
       "      <th>Q 29</th>\n",
       "      <th>Q 30</th>\n",
       "      <th>Q 31</th>\n",
       "      <th>Q 32</th>\n",
       "      <th>Q 33</th>\n",
       "      <th>Q 34</th>\n",
       "      <th>Q 35</th>\n",
       "      <th>Q 36</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q 1  Q 2  Q 3  Q 4  Q 5  Q 6  Q 7  Q 8  Q 9  Q 10  ...  Q 28  Q 29  Q 30  \\\n",
       "2      6    7    6    5    3    4    1    1    4     1  ...     1     5     5   \n",
       "9      2    3    5    6    5    4    2    6    2     3  ...     4     5     5   \n",
       "11     1    2    1    7    1    7    5    1    1     1  ...     1     1     1   \n",
       "21     6    4    4    6    2    6    3    4    5     5  ...     3     5     5   \n",
       "25     4    3    3    6    4    4    3    4    5     5  ...     5     6     3   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...   ...  ...   ...   ...   ...   \n",
       "473    7    7    6    6    4    3    3    1    3     1  ...     1     7     3   \n",
       "474    6    1    2    3    5    2    1    1    5     1  ...     1     4     2   \n",
       "475    6    7    1    1    6    1    1    1    6     1  ...     1     5     3   \n",
       "476    5    1    5    3    5    3    1    1    5     2  ...     1     5     2   \n",
       "477    6    7    5    5    6    5    1    1    5     1  ...     1     6     2   \n",
       "\n",
       "     Q 31  Q 32  Q 33  Q 34  Q 35  Q 36  Cluster  \n",
       "2       1     6     6     1     6     6        4  \n",
       "9       6     5     2     2     2     3        4  \n",
       "11      2     2     2     1     2     3        4  \n",
       "21      6     3     5     3     4     4        4  \n",
       "25      6     6     3     5     7     2        4  \n",
       "..    ...   ...   ...   ...   ...   ...      ...  \n",
       "473     1     6     1     1     3     3        4  \n",
       "474     1     4     3     1     2     2        4  \n",
       "475     1     4     4     1     3     3        4  \n",
       "476     1     5     3     1     3     3        4  \n",
       "477     1     5     1     1     2     2        4  \n",
       "\n",
       "[117 rows x 37 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only the rows belionging to the 5th cluster are filtered out.\n",
    "\n",
    "cluster_5 = df.loc[df['Cluster']==4]\n",
    "cluster_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49b50ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q 1</th>\n",
       "      <th>Q 2</th>\n",
       "      <th>Q 3</th>\n",
       "      <th>Q 4</th>\n",
       "      <th>Q 5</th>\n",
       "      <th>Q 6</th>\n",
       "      <th>Q 7</th>\n",
       "      <th>Q 8</th>\n",
       "      <th>Q 9</th>\n",
       "      <th>Q 10</th>\n",
       "      <th>...</th>\n",
       "      <th>Q 27</th>\n",
       "      <th>Q 28</th>\n",
       "      <th>Q 29</th>\n",
       "      <th>Q 30</th>\n",
       "      <th>Q 31</th>\n",
       "      <th>Q 32</th>\n",
       "      <th>Q 33</th>\n",
       "      <th>Q 34</th>\n",
       "      <th>Q 35</th>\n",
       "      <th>Q 36</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q 1  Q 2  Q 3  Q 4  Q 5  Q 6  Q 7  Q 8  Q 9  Q 10  ...  Q 27  Q 28  Q 29  \\\n",
       "2      6    7    6    5    3    4    1    1    4     1  ...     5     1     5   \n",
       "9      2    3    5    6    5    4    2    6    2     3  ...     6     4     5   \n",
       "11     1    2    1    7    1    7    5    1    1     1  ...     7     1     1   \n",
       "21     6    4    4    6    2    6    3    4    5     5  ...     5     3     5   \n",
       "25     4    3    3    6    4    4    3    4    5     5  ...     6     5     6   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...   ...  ...   ...   ...   ...   \n",
       "473    7    7    6    6    4    3    3    1    3     1  ...     5     1     7   \n",
       "474    6    1    2    3    5    2    1    1    5     1  ...     4     1     4   \n",
       "475    6    7    1    1    6    1    1    1    6     1  ...     6     1     5   \n",
       "476    5    1    5    3    5    3    1    1    5     2  ...     5     1     5   \n",
       "477    6    7    5    5    6    5    1    1    5     1  ...     6     1     6   \n",
       "\n",
       "     Q 30  Q 31  Q 32  Q 33  Q 34  Q 35  Q 36  \n",
       "2       5     1     6     6     1     6     6  \n",
       "9       5     6     5     2     2     2     3  \n",
       "11      1     2     2     2     1     2     3  \n",
       "21      5     6     3     5     3     4     4  \n",
       "25      3     6     6     3     5     7     2  \n",
       "..    ...   ...   ...   ...   ...   ...   ...  \n",
       "473     3     1     6     1     1     3     3  \n",
       "474     2     1     4     3     1     2     2  \n",
       "475     3     1     4     4     1     3     3  \n",
       "476     2     1     5     3     1     3     3  \n",
       "477     2     1     5     1     1     2     2  \n",
       "\n",
       "[117 rows x 36 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_5 = cluster_5.drop(columns=['Cluster'])\n",
    "cluster_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46e4fe90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMO per variable: [0.8753624  0.71022385 0.93372181 0.85828974 0.85034883 0.86167122\n",
      " 0.88889427 0.91029094 0.89244858 0.86228494 0.63324518 0.60895361\n",
      " 0.90776095 0.82460758 0.79200947 0.9073914  0.9015926  0.89589254\n",
      " 0.83896281 0.7610136  0.91539386 0.88522506 0.88711132 0.82239618\n",
      " 0.7937945  0.80342156 0.8742284  0.90747866 0.8272541  0.8778996\n",
      " 0.88076142 0.89549342 0.85383294 0.90660564 0.80319907 0.82362279]\n",
      "Overall KMO: 0.8622989636604216\n"
     ]
    }
   ],
   "source": [
    "# Next the KMO (Kiaser-Meyer-Olkin) test was used to determine whether the cluster is suitable for factor analysis\n",
    "# The closer the overall KMO is to 1, the better suited the dataset.\n",
    "\n",
    "kmo_all, kmo_model = calculate_kmo(cluster_5)\n",
    "\n",
    "print(\"KMO per variable:\", kmo_all)\n",
    "print(\"Overall KMO:\", kmo_model)\n",
    "\n",
    "# Since the cluster has a reasonable KMO value, going ahead with the Factor Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a60f039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q 1</th>\n",
       "      <th>Q 2</th>\n",
       "      <th>Q 3</th>\n",
       "      <th>Q 4</th>\n",
       "      <th>Q 5</th>\n",
       "      <th>Q 6</th>\n",
       "      <th>Q 7</th>\n",
       "      <th>Q 8</th>\n",
       "      <th>Q 9</th>\n",
       "      <th>Q 10</th>\n",
       "      <th>...</th>\n",
       "      <th>Q 27</th>\n",
       "      <th>Q 28</th>\n",
       "      <th>Q 29</th>\n",
       "      <th>Q 30</th>\n",
       "      <th>Q 31</th>\n",
       "      <th>Q 32</th>\n",
       "      <th>Q 33</th>\n",
       "      <th>Q 34</th>\n",
       "      <th>Q 35</th>\n",
       "      <th>Q 36</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q 1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.549596</td>\n",
       "      <td>0.455565</td>\n",
       "      <td>0.430469</td>\n",
       "      <td>0.661759</td>\n",
       "      <td>0.204717</td>\n",
       "      <td>-0.099678</td>\n",
       "      <td>-0.200118</td>\n",
       "      <td>0.580848</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.514901</td>\n",
       "      <td>-0.172580</td>\n",
       "      <td>0.706379</td>\n",
       "      <td>0.181452</td>\n",
       "      <td>-0.160434</td>\n",
       "      <td>0.606780</td>\n",
       "      <td>0.225443</td>\n",
       "      <td>-0.100133</td>\n",
       "      <td>0.351244</td>\n",
       "      <td>0.313080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 2</th>\n",
       "      <td>0.549596</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317573</td>\n",
       "      <td>0.238204</td>\n",
       "      <td>0.545300</td>\n",
       "      <td>0.228705</td>\n",
       "      <td>0.019986</td>\n",
       "      <td>-0.134238</td>\n",
       "      <td>0.262989</td>\n",
       "      <td>-0.113943</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205570</td>\n",
       "      <td>-0.129923</td>\n",
       "      <td>0.550601</td>\n",
       "      <td>0.110339</td>\n",
       "      <td>-0.134234</td>\n",
       "      <td>0.266028</td>\n",
       "      <td>-0.047341</td>\n",
       "      <td>-0.097992</td>\n",
       "      <td>0.164994</td>\n",
       "      <td>0.176416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 3</th>\n",
       "      <td>0.455565</td>\n",
       "      <td>0.317573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792832</td>\n",
       "      <td>0.524963</td>\n",
       "      <td>0.736146</td>\n",
       "      <td>0.364779</td>\n",
       "      <td>0.252652</td>\n",
       "      <td>0.431952</td>\n",
       "      <td>0.306397</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415121</td>\n",
       "      <td>0.255846</td>\n",
       "      <td>0.505488</td>\n",
       "      <td>0.509330</td>\n",
       "      <td>0.372167</td>\n",
       "      <td>0.606943</td>\n",
       "      <td>0.475279</td>\n",
       "      <td>0.297167</td>\n",
       "      <td>0.501885</td>\n",
       "      <td>0.543733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 4</th>\n",
       "      <td>0.430469</td>\n",
       "      <td>0.238204</td>\n",
       "      <td>0.792832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400708</td>\n",
       "      <td>0.758694</td>\n",
       "      <td>0.474953</td>\n",
       "      <td>0.282873</td>\n",
       "      <td>0.340266</td>\n",
       "      <td>0.304449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.374682</td>\n",
       "      <td>0.331698</td>\n",
       "      <td>0.406586</td>\n",
       "      <td>0.447028</td>\n",
       "      <td>0.388240</td>\n",
       "      <td>0.571392</td>\n",
       "      <td>0.422799</td>\n",
       "      <td>0.303419</td>\n",
       "      <td>0.467918</td>\n",
       "      <td>0.455045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 5</th>\n",
       "      <td>0.661759</td>\n",
       "      <td>0.545300</td>\n",
       "      <td>0.524963</td>\n",
       "      <td>0.400708</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318334</td>\n",
       "      <td>-0.011559</td>\n",
       "      <td>-0.096733</td>\n",
       "      <td>0.586682</td>\n",
       "      <td>0.031859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487347</td>\n",
       "      <td>-0.069026</td>\n",
       "      <td>0.756054</td>\n",
       "      <td>0.181720</td>\n",
       "      <td>-0.052887</td>\n",
       "      <td>0.539596</td>\n",
       "      <td>0.195562</td>\n",
       "      <td>-0.057710</td>\n",
       "      <td>0.256214</td>\n",
       "      <td>0.284927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 6</th>\n",
       "      <td>0.204717</td>\n",
       "      <td>0.228705</td>\n",
       "      <td>0.736146</td>\n",
       "      <td>0.758694</td>\n",
       "      <td>0.318334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.550445</td>\n",
       "      <td>0.376906</td>\n",
       "      <td>0.242402</td>\n",
       "      <td>0.506419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.320262</td>\n",
       "      <td>0.396051</td>\n",
       "      <td>0.278704</td>\n",
       "      <td>0.460704</td>\n",
       "      <td>0.553147</td>\n",
       "      <td>0.413676</td>\n",
       "      <td>0.413179</td>\n",
       "      <td>0.442916</td>\n",
       "      <td>0.418414</td>\n",
       "      <td>0.478940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 7</th>\n",
       "      <td>-0.099678</td>\n",
       "      <td>0.019986</td>\n",
       "      <td>0.364779</td>\n",
       "      <td>0.474953</td>\n",
       "      <td>-0.011559</td>\n",
       "      <td>0.550445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.630134</td>\n",
       "      <td>0.021620</td>\n",
       "      <td>0.436633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002610</td>\n",
       "      <td>0.573789</td>\n",
       "      <td>-0.186565</td>\n",
       "      <td>0.473245</td>\n",
       "      <td>0.619036</td>\n",
       "      <td>0.076680</td>\n",
       "      <td>0.421652</td>\n",
       "      <td>0.571728</td>\n",
       "      <td>0.297134</td>\n",
       "      <td>0.275859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 8</th>\n",
       "      <td>-0.200118</td>\n",
       "      <td>-0.134238</td>\n",
       "      <td>0.252652</td>\n",
       "      <td>0.282873</td>\n",
       "      <td>-0.096733</td>\n",
       "      <td>0.376906</td>\n",
       "      <td>0.630134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007126</td>\n",
       "      <td>0.605115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012610</td>\n",
       "      <td>0.728812</td>\n",
       "      <td>-0.181167</td>\n",
       "      <td>0.520657</td>\n",
       "      <td>0.666529</td>\n",
       "      <td>0.118412</td>\n",
       "      <td>0.419423</td>\n",
       "      <td>0.668646</td>\n",
       "      <td>0.282779</td>\n",
       "      <td>0.302398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 9</th>\n",
       "      <td>0.580848</td>\n",
       "      <td>0.262989</td>\n",
       "      <td>0.431952</td>\n",
       "      <td>0.340266</td>\n",
       "      <td>0.586682</td>\n",
       "      <td>0.242402</td>\n",
       "      <td>0.021620</td>\n",
       "      <td>-0.007126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.147352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.504755</td>\n",
       "      <td>0.037797</td>\n",
       "      <td>0.539855</td>\n",
       "      <td>0.284533</td>\n",
       "      <td>0.094327</td>\n",
       "      <td>0.526853</td>\n",
       "      <td>0.393403</td>\n",
       "      <td>-0.004165</td>\n",
       "      <td>0.405021</td>\n",
       "      <td>0.360979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 10</th>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.113943</td>\n",
       "      <td>0.306397</td>\n",
       "      <td>0.304449</td>\n",
       "      <td>0.031859</td>\n",
       "      <td>0.506419</td>\n",
       "      <td>0.436633</td>\n",
       "      <td>0.605115</td>\n",
       "      <td>0.147352</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015188</td>\n",
       "      <td>0.579217</td>\n",
       "      <td>0.007247</td>\n",
       "      <td>0.357953</td>\n",
       "      <td>0.638239</td>\n",
       "      <td>0.135948</td>\n",
       "      <td>0.441113</td>\n",
       "      <td>0.527677</td>\n",
       "      <td>0.416696</td>\n",
       "      <td>0.394537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 11</th>\n",
       "      <td>0.148237</td>\n",
       "      <td>0.276292</td>\n",
       "      <td>0.072018</td>\n",
       "      <td>0.124567</td>\n",
       "      <td>0.214306</td>\n",
       "      <td>0.025936</td>\n",
       "      <td>0.240867</td>\n",
       "      <td>0.282218</td>\n",
       "      <td>0.221657</td>\n",
       "      <td>0.166466</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146010</td>\n",
       "      <td>0.202029</td>\n",
       "      <td>0.165838</td>\n",
       "      <td>0.129177</td>\n",
       "      <td>0.105890</td>\n",
       "      <td>0.156099</td>\n",
       "      <td>0.060334</td>\n",
       "      <td>0.128229</td>\n",
       "      <td>0.243628</td>\n",
       "      <td>0.103865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 12</th>\n",
       "      <td>0.021801</td>\n",
       "      <td>0.001922</td>\n",
       "      <td>0.095299</td>\n",
       "      <td>0.064396</td>\n",
       "      <td>0.034803</td>\n",
       "      <td>0.206357</td>\n",
       "      <td>0.078850</td>\n",
       "      <td>0.173455</td>\n",
       "      <td>0.145391</td>\n",
       "      <td>0.375477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159923</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>0.138509</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>0.120610</td>\n",
       "      <td>0.088896</td>\n",
       "      <td>-0.042631</td>\n",
       "      <td>0.103088</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.055648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 13</th>\n",
       "      <td>0.486696</td>\n",
       "      <td>0.321482</td>\n",
       "      <td>0.467029</td>\n",
       "      <td>0.404974</td>\n",
       "      <td>0.539106</td>\n",
       "      <td>0.443985</td>\n",
       "      <td>0.197499</td>\n",
       "      <td>0.046384</td>\n",
       "      <td>0.446520</td>\n",
       "      <td>0.229283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386758</td>\n",
       "      <td>0.071273</td>\n",
       "      <td>0.413056</td>\n",
       "      <td>0.285125</td>\n",
       "      <td>0.221324</td>\n",
       "      <td>0.353399</td>\n",
       "      <td>0.366070</td>\n",
       "      <td>0.167372</td>\n",
       "      <td>0.331694</td>\n",
       "      <td>0.414556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 14</th>\n",
       "      <td>0.468119</td>\n",
       "      <td>0.471484</td>\n",
       "      <td>0.430450</td>\n",
       "      <td>0.321120</td>\n",
       "      <td>0.630385</td>\n",
       "      <td>0.227147</td>\n",
       "      <td>0.004110</td>\n",
       "      <td>-0.079610</td>\n",
       "      <td>0.392438</td>\n",
       "      <td>0.081625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228510</td>\n",
       "      <td>-0.058132</td>\n",
       "      <td>0.506464</td>\n",
       "      <td>0.206954</td>\n",
       "      <td>-0.037176</td>\n",
       "      <td>0.306078</td>\n",
       "      <td>0.162282</td>\n",
       "      <td>-0.013406</td>\n",
       "      <td>0.205052</td>\n",
       "      <td>0.176817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 15</th>\n",
       "      <td>0.029757</td>\n",
       "      <td>0.054173</td>\n",
       "      <td>0.315195</td>\n",
       "      <td>0.295027</td>\n",
       "      <td>0.154048</td>\n",
       "      <td>0.365463</td>\n",
       "      <td>0.369771</td>\n",
       "      <td>0.459505</td>\n",
       "      <td>0.278613</td>\n",
       "      <td>0.577794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075582</td>\n",
       "      <td>0.360261</td>\n",
       "      <td>0.193011</td>\n",
       "      <td>0.285436</td>\n",
       "      <td>0.467437</td>\n",
       "      <td>0.179769</td>\n",
       "      <td>0.189077</td>\n",
       "      <td>0.306373</td>\n",
       "      <td>0.252300</td>\n",
       "      <td>0.221829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 16</th>\n",
       "      <td>-0.137052</td>\n",
       "      <td>-0.053562</td>\n",
       "      <td>0.342519</td>\n",
       "      <td>0.326162</td>\n",
       "      <td>-0.071698</td>\n",
       "      <td>0.493178</td>\n",
       "      <td>0.698744</td>\n",
       "      <td>0.712298</td>\n",
       "      <td>-0.014390</td>\n",
       "      <td>0.498484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007461</td>\n",
       "      <td>0.679246</td>\n",
       "      <td>-0.228407</td>\n",
       "      <td>0.532880</td>\n",
       "      <td>0.747343</td>\n",
       "      <td>0.118056</td>\n",
       "      <td>0.496481</td>\n",
       "      <td>0.682087</td>\n",
       "      <td>0.327469</td>\n",
       "      <td>0.405469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 17</th>\n",
       "      <td>-0.052695</td>\n",
       "      <td>-0.050990</td>\n",
       "      <td>0.372955</td>\n",
       "      <td>0.424624</td>\n",
       "      <td>-0.063581</td>\n",
       "      <td>0.550282</td>\n",
       "      <td>0.568591</td>\n",
       "      <td>0.543926</td>\n",
       "      <td>0.082754</td>\n",
       "      <td>0.488300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150150</td>\n",
       "      <td>0.549876</td>\n",
       "      <td>-0.132640</td>\n",
       "      <td>0.450386</td>\n",
       "      <td>0.605775</td>\n",
       "      <td>0.144487</td>\n",
       "      <td>0.556404</td>\n",
       "      <td>0.520904</td>\n",
       "      <td>0.355073</td>\n",
       "      <td>0.418862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 18</th>\n",
       "      <td>0.003144</td>\n",
       "      <td>-0.088390</td>\n",
       "      <td>0.319888</td>\n",
       "      <td>0.385959</td>\n",
       "      <td>-0.027609</td>\n",
       "      <td>0.532209</td>\n",
       "      <td>0.569139</td>\n",
       "      <td>0.606632</td>\n",
       "      <td>0.163727</td>\n",
       "      <td>0.503300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202839</td>\n",
       "      <td>0.553500</td>\n",
       "      <td>-0.102112</td>\n",
       "      <td>0.383598</td>\n",
       "      <td>0.658039</td>\n",
       "      <td>0.228859</td>\n",
       "      <td>0.417508</td>\n",
       "      <td>0.667734</td>\n",
       "      <td>0.322911</td>\n",
       "      <td>0.385417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 19</th>\n",
       "      <td>0.517930</td>\n",
       "      <td>0.323722</td>\n",
       "      <td>0.348623</td>\n",
       "      <td>0.249058</td>\n",
       "      <td>0.591855</td>\n",
       "      <td>0.200058</td>\n",
       "      <td>-0.114469</td>\n",
       "      <td>-0.096060</td>\n",
       "      <td>0.516444</td>\n",
       "      <td>-0.080272</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501712</td>\n",
       "      <td>0.045767</td>\n",
       "      <td>0.606326</td>\n",
       "      <td>0.312730</td>\n",
       "      <td>0.015497</td>\n",
       "      <td>0.479940</td>\n",
       "      <td>0.258799</td>\n",
       "      <td>-0.066631</td>\n",
       "      <td>0.248525</td>\n",
       "      <td>0.222936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 20</th>\n",
       "      <td>0.379978</td>\n",
       "      <td>0.416729</td>\n",
       "      <td>0.326301</td>\n",
       "      <td>0.239785</td>\n",
       "      <td>0.497703</td>\n",
       "      <td>0.218258</td>\n",
       "      <td>0.036729</td>\n",
       "      <td>-0.047361</td>\n",
       "      <td>0.359785</td>\n",
       "      <td>0.054143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.276639</td>\n",
       "      <td>-0.094222</td>\n",
       "      <td>0.381392</td>\n",
       "      <td>0.195988</td>\n",
       "      <td>-0.128698</td>\n",
       "      <td>0.354719</td>\n",
       "      <td>0.081894</td>\n",
       "      <td>-0.190607</td>\n",
       "      <td>0.087868</td>\n",
       "      <td>0.166569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 21</th>\n",
       "      <td>-0.101512</td>\n",
       "      <td>-0.072628</td>\n",
       "      <td>0.390386</td>\n",
       "      <td>0.408711</td>\n",
       "      <td>0.089007</td>\n",
       "      <td>0.476268</td>\n",
       "      <td>0.608079</td>\n",
       "      <td>0.708364</td>\n",
       "      <td>0.146457</td>\n",
       "      <td>0.629588</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020519</td>\n",
       "      <td>0.748766</td>\n",
       "      <td>-0.091719</td>\n",
       "      <td>0.537970</td>\n",
       "      <td>0.763195</td>\n",
       "      <td>0.242544</td>\n",
       "      <td>0.495262</td>\n",
       "      <td>0.655857</td>\n",
       "      <td>0.481110</td>\n",
       "      <td>0.432936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 22</th>\n",
       "      <td>0.629101</td>\n",
       "      <td>0.418886</td>\n",
       "      <td>0.478828</td>\n",
       "      <td>0.454709</td>\n",
       "      <td>0.628581</td>\n",
       "      <td>0.429232</td>\n",
       "      <td>-0.007304</td>\n",
       "      <td>-0.089384</td>\n",
       "      <td>0.582652</td>\n",
       "      <td>0.097442</td>\n",
       "      <td>...</td>\n",
       "      <td>0.583716</td>\n",
       "      <td>-0.043928</td>\n",
       "      <td>0.640141</td>\n",
       "      <td>0.227787</td>\n",
       "      <td>0.038928</td>\n",
       "      <td>0.569528</td>\n",
       "      <td>0.244713</td>\n",
       "      <td>-0.088300</td>\n",
       "      <td>0.289844</td>\n",
       "      <td>0.260732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 23</th>\n",
       "      <td>-0.017275</td>\n",
       "      <td>0.020882</td>\n",
       "      <td>0.343457</td>\n",
       "      <td>0.267648</td>\n",
       "      <td>-0.024093</td>\n",
       "      <td>0.398119</td>\n",
       "      <td>0.571434</td>\n",
       "      <td>0.508295</td>\n",
       "      <td>0.125208</td>\n",
       "      <td>0.421687</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043879</td>\n",
       "      <td>0.572043</td>\n",
       "      <td>-0.133212</td>\n",
       "      <td>0.453575</td>\n",
       "      <td>0.678138</td>\n",
       "      <td>0.041324</td>\n",
       "      <td>0.413378</td>\n",
       "      <td>0.738117</td>\n",
       "      <td>0.397335</td>\n",
       "      <td>0.412932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 24</th>\n",
       "      <td>0.041720</td>\n",
       "      <td>-0.067512</td>\n",
       "      <td>0.269165</td>\n",
       "      <td>0.253294</td>\n",
       "      <td>-0.089518</td>\n",
       "      <td>0.389190</td>\n",
       "      <td>0.420389</td>\n",
       "      <td>0.328246</td>\n",
       "      <td>0.184364</td>\n",
       "      <td>0.271735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252759</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>-0.112175</td>\n",
       "      <td>0.207894</td>\n",
       "      <td>0.385127</td>\n",
       "      <td>0.083609</td>\n",
       "      <td>0.281198</td>\n",
       "      <td>0.348688</td>\n",
       "      <td>0.190508</td>\n",
       "      <td>0.277315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 25</th>\n",
       "      <td>-0.176460</td>\n",
       "      <td>-0.030078</td>\n",
       "      <td>0.215616</td>\n",
       "      <td>0.135805</td>\n",
       "      <td>-0.074689</td>\n",
       "      <td>0.285925</td>\n",
       "      <td>0.398337</td>\n",
       "      <td>0.477140</td>\n",
       "      <td>-0.035665</td>\n",
       "      <td>0.398396</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.186422</td>\n",
       "      <td>0.536759</td>\n",
       "      <td>-0.215221</td>\n",
       "      <td>0.244400</td>\n",
       "      <td>0.459107</td>\n",
       "      <td>-0.030526</td>\n",
       "      <td>0.310113</td>\n",
       "      <td>0.574643</td>\n",
       "      <td>0.342997</td>\n",
       "      <td>0.305184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 26</th>\n",
       "      <td>-0.116310</td>\n",
       "      <td>0.034225</td>\n",
       "      <td>0.263305</td>\n",
       "      <td>0.204791</td>\n",
       "      <td>0.022293</td>\n",
       "      <td>0.272588</td>\n",
       "      <td>0.399203</td>\n",
       "      <td>0.496278</td>\n",
       "      <td>0.057570</td>\n",
       "      <td>0.431354</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174604</td>\n",
       "      <td>0.548810</td>\n",
       "      <td>-0.164557</td>\n",
       "      <td>0.287890</td>\n",
       "      <td>0.548549</td>\n",
       "      <td>0.054399</td>\n",
       "      <td>0.323101</td>\n",
       "      <td>0.534658</td>\n",
       "      <td>0.322272</td>\n",
       "      <td>0.404736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 27</th>\n",
       "      <td>0.514901</td>\n",
       "      <td>0.205570</td>\n",
       "      <td>0.415121</td>\n",
       "      <td>0.374682</td>\n",
       "      <td>0.487347</td>\n",
       "      <td>0.320262</td>\n",
       "      <td>-0.002610</td>\n",
       "      <td>-0.012610</td>\n",
       "      <td>0.504755</td>\n",
       "      <td>-0.015188</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.052087</td>\n",
       "      <td>0.582642</td>\n",
       "      <td>0.306043</td>\n",
       "      <td>0.042470</td>\n",
       "      <td>0.558214</td>\n",
       "      <td>0.312844</td>\n",
       "      <td>-0.030077</td>\n",
       "      <td>0.369089</td>\n",
       "      <td>0.410087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 28</th>\n",
       "      <td>-0.172580</td>\n",
       "      <td>-0.129923</td>\n",
       "      <td>0.255846</td>\n",
       "      <td>0.331698</td>\n",
       "      <td>-0.069026</td>\n",
       "      <td>0.396051</td>\n",
       "      <td>0.573789</td>\n",
       "      <td>0.728812</td>\n",
       "      <td>0.037797</td>\n",
       "      <td>0.579217</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.181606</td>\n",
       "      <td>0.570820</td>\n",
       "      <td>0.815864</td>\n",
       "      <td>0.188606</td>\n",
       "      <td>0.495023</td>\n",
       "      <td>0.724151</td>\n",
       "      <td>0.382746</td>\n",
       "      <td>0.322371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 29</th>\n",
       "      <td>0.706379</td>\n",
       "      <td>0.550601</td>\n",
       "      <td>0.505488</td>\n",
       "      <td>0.406586</td>\n",
       "      <td>0.756054</td>\n",
       "      <td>0.278704</td>\n",
       "      <td>-0.186565</td>\n",
       "      <td>-0.181167</td>\n",
       "      <td>0.539855</td>\n",
       "      <td>0.007247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.582642</td>\n",
       "      <td>-0.181606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195845</td>\n",
       "      <td>-0.098875</td>\n",
       "      <td>0.548835</td>\n",
       "      <td>0.118248</td>\n",
       "      <td>-0.142238</td>\n",
       "      <td>0.310377</td>\n",
       "      <td>0.305811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 30</th>\n",
       "      <td>0.181452</td>\n",
       "      <td>0.110339</td>\n",
       "      <td>0.509330</td>\n",
       "      <td>0.447028</td>\n",
       "      <td>0.181720</td>\n",
       "      <td>0.460704</td>\n",
       "      <td>0.473245</td>\n",
       "      <td>0.520657</td>\n",
       "      <td>0.284533</td>\n",
       "      <td>0.357953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.306043</td>\n",
       "      <td>0.570820</td>\n",
       "      <td>0.195845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.618772</td>\n",
       "      <td>0.451918</td>\n",
       "      <td>0.702871</td>\n",
       "      <td>0.521452</td>\n",
       "      <td>0.539953</td>\n",
       "      <td>0.576429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 31</th>\n",
       "      <td>-0.160434</td>\n",
       "      <td>-0.134234</td>\n",
       "      <td>0.372167</td>\n",
       "      <td>0.388240</td>\n",
       "      <td>-0.052887</td>\n",
       "      <td>0.553147</td>\n",
       "      <td>0.619036</td>\n",
       "      <td>0.666529</td>\n",
       "      <td>0.094327</td>\n",
       "      <td>0.638239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042470</td>\n",
       "      <td>0.815864</td>\n",
       "      <td>-0.098875</td>\n",
       "      <td>0.618772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170919</td>\n",
       "      <td>0.553498</td>\n",
       "      <td>0.732762</td>\n",
       "      <td>0.445986</td>\n",
       "      <td>0.408650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 32</th>\n",
       "      <td>0.606780</td>\n",
       "      <td>0.266028</td>\n",
       "      <td>0.606943</td>\n",
       "      <td>0.571392</td>\n",
       "      <td>0.539596</td>\n",
       "      <td>0.413676</td>\n",
       "      <td>0.076680</td>\n",
       "      <td>0.118412</td>\n",
       "      <td>0.526853</td>\n",
       "      <td>0.135948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558214</td>\n",
       "      <td>0.188606</td>\n",
       "      <td>0.548835</td>\n",
       "      <td>0.451918</td>\n",
       "      <td>0.170919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.481921</td>\n",
       "      <td>0.107653</td>\n",
       "      <td>0.534646</td>\n",
       "      <td>0.506619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 33</th>\n",
       "      <td>0.225443</td>\n",
       "      <td>-0.047341</td>\n",
       "      <td>0.475279</td>\n",
       "      <td>0.422799</td>\n",
       "      <td>0.195562</td>\n",
       "      <td>0.413179</td>\n",
       "      <td>0.421652</td>\n",
       "      <td>0.419423</td>\n",
       "      <td>0.393403</td>\n",
       "      <td>0.441113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.312844</td>\n",
       "      <td>0.495023</td>\n",
       "      <td>0.118248</td>\n",
       "      <td>0.702871</td>\n",
       "      <td>0.553498</td>\n",
       "      <td>0.481921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386140</td>\n",
       "      <td>0.595249</td>\n",
       "      <td>0.619911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 34</th>\n",
       "      <td>-0.100133</td>\n",
       "      <td>-0.097992</td>\n",
       "      <td>0.297167</td>\n",
       "      <td>0.303419</td>\n",
       "      <td>-0.057710</td>\n",
       "      <td>0.442916</td>\n",
       "      <td>0.571728</td>\n",
       "      <td>0.668646</td>\n",
       "      <td>-0.004165</td>\n",
       "      <td>0.527677</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030077</td>\n",
       "      <td>0.724151</td>\n",
       "      <td>-0.142238</td>\n",
       "      <td>0.521452</td>\n",
       "      <td>0.732762</td>\n",
       "      <td>0.107653</td>\n",
       "      <td>0.386140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433835</td>\n",
       "      <td>0.414294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 35</th>\n",
       "      <td>0.351244</td>\n",
       "      <td>0.164994</td>\n",
       "      <td>0.501885</td>\n",
       "      <td>0.467918</td>\n",
       "      <td>0.256214</td>\n",
       "      <td>0.418414</td>\n",
       "      <td>0.297134</td>\n",
       "      <td>0.282779</td>\n",
       "      <td>0.405021</td>\n",
       "      <td>0.416696</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369089</td>\n",
       "      <td>0.382746</td>\n",
       "      <td>0.310377</td>\n",
       "      <td>0.539953</td>\n",
       "      <td>0.445986</td>\n",
       "      <td>0.534646</td>\n",
       "      <td>0.595249</td>\n",
       "      <td>0.433835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q 36</th>\n",
       "      <td>0.313080</td>\n",
       "      <td>0.176416</td>\n",
       "      <td>0.543733</td>\n",
       "      <td>0.455045</td>\n",
       "      <td>0.284927</td>\n",
       "      <td>0.478940</td>\n",
       "      <td>0.275859</td>\n",
       "      <td>0.302398</td>\n",
       "      <td>0.360979</td>\n",
       "      <td>0.394537</td>\n",
       "      <td>...</td>\n",
       "      <td>0.410087</td>\n",
       "      <td>0.322371</td>\n",
       "      <td>0.305811</td>\n",
       "      <td>0.576429</td>\n",
       "      <td>0.408650</td>\n",
       "      <td>0.506619</td>\n",
       "      <td>0.619911</td>\n",
       "      <td>0.414294</td>\n",
       "      <td>0.791418</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Q 1       Q 2       Q 3       Q 4       Q 5       Q 6       Q 7  \\\n",
       "Q 1   1.000000  0.549596  0.455565  0.430469  0.661759  0.204717 -0.099678   \n",
       "Q 2   0.549596  1.000000  0.317573  0.238204  0.545300  0.228705  0.019986   \n",
       "Q 3   0.455565  0.317573  1.000000  0.792832  0.524963  0.736146  0.364779   \n",
       "Q 4   0.430469  0.238204  0.792832  1.000000  0.400708  0.758694  0.474953   \n",
       "Q 5   0.661759  0.545300  0.524963  0.400708  1.000000  0.318334 -0.011559   \n",
       "Q 6   0.204717  0.228705  0.736146  0.758694  0.318334  1.000000  0.550445   \n",
       "Q 7  -0.099678  0.019986  0.364779  0.474953 -0.011559  0.550445  1.000000   \n",
       "Q 8  -0.200118 -0.134238  0.252652  0.282873 -0.096733  0.376906  0.630134   \n",
       "Q 9   0.580848  0.262989  0.431952  0.340266  0.586682  0.242402  0.021620   \n",
       "Q 10 -0.069881 -0.113943  0.306397  0.304449  0.031859  0.506419  0.436633   \n",
       "Q 11  0.148237  0.276292  0.072018  0.124567  0.214306  0.025936  0.240867   \n",
       "Q 12  0.021801  0.001922  0.095299  0.064396  0.034803  0.206357  0.078850   \n",
       "Q 13  0.486696  0.321482  0.467029  0.404974  0.539106  0.443985  0.197499   \n",
       "Q 14  0.468119  0.471484  0.430450  0.321120  0.630385  0.227147  0.004110   \n",
       "Q 15  0.029757  0.054173  0.315195  0.295027  0.154048  0.365463  0.369771   \n",
       "Q 16 -0.137052 -0.053562  0.342519  0.326162 -0.071698  0.493178  0.698744   \n",
       "Q 17 -0.052695 -0.050990  0.372955  0.424624 -0.063581  0.550282  0.568591   \n",
       "Q 18  0.003144 -0.088390  0.319888  0.385959 -0.027609  0.532209  0.569139   \n",
       "Q 19  0.517930  0.323722  0.348623  0.249058  0.591855  0.200058 -0.114469   \n",
       "Q 20  0.379978  0.416729  0.326301  0.239785  0.497703  0.218258  0.036729   \n",
       "Q 21 -0.101512 -0.072628  0.390386  0.408711  0.089007  0.476268  0.608079   \n",
       "Q 22  0.629101  0.418886  0.478828  0.454709  0.628581  0.429232 -0.007304   \n",
       "Q 23 -0.017275  0.020882  0.343457  0.267648 -0.024093  0.398119  0.571434   \n",
       "Q 24  0.041720 -0.067512  0.269165  0.253294 -0.089518  0.389190  0.420389   \n",
       "Q 25 -0.176460 -0.030078  0.215616  0.135805 -0.074689  0.285925  0.398337   \n",
       "Q 26 -0.116310  0.034225  0.263305  0.204791  0.022293  0.272588  0.399203   \n",
       "Q 27  0.514901  0.205570  0.415121  0.374682  0.487347  0.320262 -0.002610   \n",
       "Q 28 -0.172580 -0.129923  0.255846  0.331698 -0.069026  0.396051  0.573789   \n",
       "Q 29  0.706379  0.550601  0.505488  0.406586  0.756054  0.278704 -0.186565   \n",
       "Q 30  0.181452  0.110339  0.509330  0.447028  0.181720  0.460704  0.473245   \n",
       "Q 31 -0.160434 -0.134234  0.372167  0.388240 -0.052887  0.553147  0.619036   \n",
       "Q 32  0.606780  0.266028  0.606943  0.571392  0.539596  0.413676  0.076680   \n",
       "Q 33  0.225443 -0.047341  0.475279  0.422799  0.195562  0.413179  0.421652   \n",
       "Q 34 -0.100133 -0.097992  0.297167  0.303419 -0.057710  0.442916  0.571728   \n",
       "Q 35  0.351244  0.164994  0.501885  0.467918  0.256214  0.418414  0.297134   \n",
       "Q 36  0.313080  0.176416  0.543733  0.455045  0.284927  0.478940  0.275859   \n",
       "\n",
       "           Q 8       Q 9      Q 10  ...      Q 27      Q 28      Q 29  \\\n",
       "Q 1  -0.200118  0.580848 -0.069881  ...  0.514901 -0.172580  0.706379   \n",
       "Q 2  -0.134238  0.262989 -0.113943  ...  0.205570 -0.129923  0.550601   \n",
       "Q 3   0.252652  0.431952  0.306397  ...  0.415121  0.255846  0.505488   \n",
       "Q 4   0.282873  0.340266  0.304449  ...  0.374682  0.331698  0.406586   \n",
       "Q 5  -0.096733  0.586682  0.031859  ...  0.487347 -0.069026  0.756054   \n",
       "Q 6   0.376906  0.242402  0.506419  ...  0.320262  0.396051  0.278704   \n",
       "Q 7   0.630134  0.021620  0.436633  ... -0.002610  0.573789 -0.186565   \n",
       "Q 8   1.000000 -0.007126  0.605115  ... -0.012610  0.728812 -0.181167   \n",
       "Q 9  -0.007126  1.000000  0.147352  ...  0.504755  0.037797  0.539855   \n",
       "Q 10  0.605115  0.147352  1.000000  ... -0.015188  0.579217  0.007247   \n",
       "Q 11  0.282218  0.221657  0.166466  ...  0.146010  0.202029  0.165838   \n",
       "Q 12  0.173455  0.145391  0.375477  ...  0.159923  0.021329  0.138509   \n",
       "Q 13  0.046384  0.446520  0.229283  ...  0.386758  0.071273  0.413056   \n",
       "Q 14 -0.079610  0.392438  0.081625  ...  0.228510 -0.058132  0.506464   \n",
       "Q 15  0.459505  0.278613  0.577794  ...  0.075582  0.360261  0.193011   \n",
       "Q 16  0.712298 -0.014390  0.498484  ...  0.007461  0.679246 -0.228407   \n",
       "Q 17  0.543926  0.082754  0.488300  ...  0.150150  0.549876 -0.132640   \n",
       "Q 18  0.606632  0.163727  0.503300  ...  0.202839  0.553500 -0.102112   \n",
       "Q 19 -0.096060  0.516444 -0.080272  ...  0.501712  0.045767  0.606326   \n",
       "Q 20 -0.047361  0.359785  0.054143  ...  0.276639 -0.094222  0.381392   \n",
       "Q 21  0.708364  0.146457  0.629588  ... -0.020519  0.748766 -0.091719   \n",
       "Q 22 -0.089384  0.582652  0.097442  ...  0.583716 -0.043928  0.640141   \n",
       "Q 23  0.508295  0.125208  0.421687  ... -0.043879  0.572043 -0.133212   \n",
       "Q 24  0.328246  0.184364  0.271735  ...  0.252759  0.284611 -0.112175   \n",
       "Q 25  0.477140 -0.035665  0.398396  ... -0.186422  0.536759 -0.215221   \n",
       "Q 26  0.496278  0.057570  0.431354  ... -0.174604  0.548810 -0.164557   \n",
       "Q 27 -0.012610  0.504755 -0.015188  ...  1.000000 -0.052087  0.582642   \n",
       "Q 28  0.728812  0.037797  0.579217  ... -0.052087  1.000000 -0.181606   \n",
       "Q 29 -0.181167  0.539855  0.007247  ...  0.582642 -0.181606  1.000000   \n",
       "Q 30  0.520657  0.284533  0.357953  ...  0.306043  0.570820  0.195845   \n",
       "Q 31  0.666529  0.094327  0.638239  ...  0.042470  0.815864 -0.098875   \n",
       "Q 32  0.118412  0.526853  0.135948  ...  0.558214  0.188606  0.548835   \n",
       "Q 33  0.419423  0.393403  0.441113  ...  0.312844  0.495023  0.118248   \n",
       "Q 34  0.668646 -0.004165  0.527677  ... -0.030077  0.724151 -0.142238   \n",
       "Q 35  0.282779  0.405021  0.416696  ...  0.369089  0.382746  0.310377   \n",
       "Q 36  0.302398  0.360979  0.394537  ...  0.410087  0.322371  0.305811   \n",
       "\n",
       "          Q 30      Q 31      Q 32      Q 33      Q 34      Q 35      Q 36  \n",
       "Q 1   0.181452 -0.160434  0.606780  0.225443 -0.100133  0.351244  0.313080  \n",
       "Q 2   0.110339 -0.134234  0.266028 -0.047341 -0.097992  0.164994  0.176416  \n",
       "Q 3   0.509330  0.372167  0.606943  0.475279  0.297167  0.501885  0.543733  \n",
       "Q 4   0.447028  0.388240  0.571392  0.422799  0.303419  0.467918  0.455045  \n",
       "Q 5   0.181720 -0.052887  0.539596  0.195562 -0.057710  0.256214  0.284927  \n",
       "Q 6   0.460704  0.553147  0.413676  0.413179  0.442916  0.418414  0.478940  \n",
       "Q 7   0.473245  0.619036  0.076680  0.421652  0.571728  0.297134  0.275859  \n",
       "Q 8   0.520657  0.666529  0.118412  0.419423  0.668646  0.282779  0.302398  \n",
       "Q 9   0.284533  0.094327  0.526853  0.393403 -0.004165  0.405021  0.360979  \n",
       "Q 10  0.357953  0.638239  0.135948  0.441113  0.527677  0.416696  0.394537  \n",
       "Q 11  0.129177  0.105890  0.156099  0.060334  0.128229  0.243628  0.103865  \n",
       "Q 12  0.001852  0.120610  0.088896 -0.042631  0.103088  0.149560  0.055648  \n",
       "Q 13  0.285125  0.221324  0.353399  0.366070  0.167372  0.331694  0.414556  \n",
       "Q 14  0.206954 -0.037176  0.306078  0.162282 -0.013406  0.205052  0.176817  \n",
       "Q 15  0.285436  0.467437  0.179769  0.189077  0.306373  0.252300  0.221829  \n",
       "Q 16  0.532880  0.747343  0.118056  0.496481  0.682087  0.327469  0.405469  \n",
       "Q 17  0.450386  0.605775  0.144487  0.556404  0.520904  0.355073  0.418862  \n",
       "Q 18  0.383598  0.658039  0.228859  0.417508  0.667734  0.322911  0.385417  \n",
       "Q 19  0.312730  0.015497  0.479940  0.258799 -0.066631  0.248525  0.222936  \n",
       "Q 20  0.195988 -0.128698  0.354719  0.081894 -0.190607  0.087868  0.166569  \n",
       "Q 21  0.537970  0.763195  0.242544  0.495262  0.655857  0.481110  0.432936  \n",
       "Q 22  0.227787  0.038928  0.569528  0.244713 -0.088300  0.289844  0.260732  \n",
       "Q 23  0.453575  0.678138  0.041324  0.413378  0.738117  0.397335  0.412932  \n",
       "Q 24  0.207894  0.385127  0.083609  0.281198  0.348688  0.190508  0.277315  \n",
       "Q 25  0.244400  0.459107 -0.030526  0.310113  0.574643  0.342997  0.305184  \n",
       "Q 26  0.287890  0.548549  0.054399  0.323101  0.534658  0.322272  0.404736  \n",
       "Q 27  0.306043  0.042470  0.558214  0.312844 -0.030077  0.369089  0.410087  \n",
       "Q 28  0.570820  0.815864  0.188606  0.495023  0.724151  0.382746  0.322371  \n",
       "Q 29  0.195845 -0.098875  0.548835  0.118248 -0.142238  0.310377  0.305811  \n",
       "Q 30  1.000000  0.618772  0.451918  0.702871  0.521452  0.539953  0.576429  \n",
       "Q 31  0.618772  1.000000  0.170919  0.553498  0.732762  0.445986  0.408650  \n",
       "Q 32  0.451918  0.170919  1.000000  0.481921  0.107653  0.534646  0.506619  \n",
       "Q 33  0.702871  0.553498  0.481921  1.000000  0.386140  0.595249  0.619911  \n",
       "Q 34  0.521452  0.732762  0.107653  0.386140  1.000000  0.433835  0.414294  \n",
       "Q 35  0.539953  0.445986  0.534646  0.595249  0.433835  1.000000  0.791418  \n",
       "Q 36  0.576429  0.408650  0.506619  0.619911  0.414294  0.791418  1.000000  \n",
       "\n",
       "[36 rows x 36 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The original correlation matrix for the third cluster:\n",
    "cluster_5.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b44a2160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eigenvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.622003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.266139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.990538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.758093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.429721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.245185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.065921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.996036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.877576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.711040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.622060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.573227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.535024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.506471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.461963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.410649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.389377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.372894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.332059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.323559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.287605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.258200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.233161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.225169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.215713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.178115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.164256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.162852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.148412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.119574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.113307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.105637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.089093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.078384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.075462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.055524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Eigenvalues\n",
       "0     11.622003\n",
       "1      7.266139\n",
       "2      1.990538\n",
       "3      1.758093\n",
       "4      1.429721\n",
       "5      1.245185\n",
       "6      1.065921\n",
       "7      0.996036\n",
       "8      0.877576\n",
       "9      0.711040\n",
       "10     0.622060\n",
       "11     0.573227\n",
       "12     0.535024\n",
       "13     0.506471\n",
       "14     0.461963\n",
       "15     0.410649\n",
       "16     0.389377\n",
       "17     0.372894\n",
       "18     0.332059\n",
       "19     0.323559\n",
       "20     0.287605\n",
       "21     0.258200\n",
       "22     0.233161\n",
       "23     0.225169\n",
       "24     0.215713\n",
       "25     0.178115\n",
       "26     0.164256\n",
       "27     0.162852\n",
       "28     0.148412\n",
       "29     0.119574\n",
       "30     0.113307\n",
       "31     0.105637\n",
       "32     0.089093\n",
       "33     0.078384\n",
       "34     0.075462\n",
       "35     0.055524"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The eigenvalues are calculated for the third cluster\n",
    "\n",
    "fa_1 = FactorAnalyzer(n_factors=3, rotation='oblimin').fit(cluster_5)\n",
    "fa_1.loadings_\n",
    "\n",
    "eigenvalues, _ = fa_1.get_eigenvalues()\n",
    "eigen_values = list(eigenvalues)\n",
    "\n",
    "data_ev = pd.DataFrame(eigen_values,columns=['Eigenvalues'])\n",
    "\n",
    "data_ev\n",
    "\n",
    "# In this case, since the first 7 eigenvalues are greater than 1, the no. of factors is take to be 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2546ab41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The factor loadings for the 7 Factors are presented in the form of a heatmap:\n",
    "\n",
    "fa_2 = FactorAnalyzer(n_factors=7, rotation='oblimin').fit(cluster_5)\n",
    "loadings = fa_2.loadings_\n",
    "\n",
    "plt.figure(figsize=(12, 12)) \n",
    "sns.heatmap(loadings, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Heatmap of Factor Loadings')\n",
    "plt.xlabel('Factors')\n",
    "plt.ylabel('Variables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab6be465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.074657</td>\n",
       "      <td>0.216908</td>\n",
       "      <td>0.377905</td>\n",
       "      <td>0.301032</td>\n",
       "      <td>0.410532</td>\n",
       "      <td>0.402661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.074657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090221</td>\n",
       "      <td>-0.255720</td>\n",
       "      <td>0.393148</td>\n",
       "      <td>-0.020930</td>\n",
       "      <td>0.340310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.216908</td>\n",
       "      <td>0.090221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.104705</td>\n",
       "      <td>0.078038</td>\n",
       "      <td>0.178342</td>\n",
       "      <td>0.223915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377905</td>\n",
       "      <td>-0.255720</td>\n",
       "      <td>0.104705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061454</td>\n",
       "      <td>0.180937</td>\n",
       "      <td>0.059020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.301032</td>\n",
       "      <td>0.393148</td>\n",
       "      <td>0.078038</td>\n",
       "      <td>-0.061454</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.256808</td>\n",
       "      <td>0.423330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.410532</td>\n",
       "      <td>-0.020930</td>\n",
       "      <td>0.178342</td>\n",
       "      <td>0.180937</td>\n",
       "      <td>0.256808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.379578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.402661</td>\n",
       "      <td>0.340310</td>\n",
       "      <td>0.223915</td>\n",
       "      <td>0.059020</td>\n",
       "      <td>0.423330</td>\n",
       "      <td>0.379578</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6\n",
       "0  1.000000 -0.074657  0.216908  0.377905  0.301032  0.410532  0.402661\n",
       "1 -0.074657  1.000000  0.090221 -0.255720  0.393148 -0.020930  0.340310\n",
       "2  0.216908  0.090221  1.000000  0.104705  0.078038  0.178342  0.223915\n",
       "3  0.377905 -0.255720  0.104705  1.000000 -0.061454  0.180937  0.059020\n",
       "4  0.301032  0.393148  0.078038 -0.061454  1.000000  0.256808  0.423330\n",
       "5  0.410532 -0.020930  0.178342  0.180937  0.256808  1.000000  0.379578\n",
       "6  0.402661  0.340310  0.223915  0.059020  0.423330  0.379578  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat_factors_5 = pd.DataFrame(fa_2.phi_)\n",
    "corr_mat_factors_5\n",
    "\n",
    "# The correlation matrix between the 7 Factors themselves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc57c01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting to Excel\n",
    "\n",
    "corr_mat_factors_5.to_excel('FA5_KMedoids.xlsx')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}