Factor_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Factor Analysis of Participants' Demographic and Personality Variables**\n",
    "Lindsay Warrenburg  \n",
    "lindsay.a.warrenburg@gmail.com  \n",
    "Final version: December 22, 2020\n",
    "\n",
    "### This script describes *Step 2* of investigating individual differences in lockdown situations and associated musical behaviors.\n",
    "\n",
    "- Preliminary tests showed that correlation matrix of the variables contained redundancies that could be explained through latent variables.\n",
    "- Exploratory Factor Analysis with 4-7 latent was conducted. \n",
    "- The 6-factor solution was selected and the possible meanings of the latent factors are explored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd, seaborn as sns, os, sys, itertools, re, warnings, math, scipy as sp, sklearn, matplotlib.pyplot as plt, matplotlib.cm as cm, statsmodels.stats.api as sms, statsmodels.api as sm\n",
    "from factor_analyzer import FactorAnalyzer, calculate_bartlett_sphericity, calculate_kmo\n",
    "from sklearn import preprocessing, metrics, manifold\n",
    "from sklearn.model_selection import GridSearchCV, KFold, cross_val_score, train_test_split, RandomizedSearchCV\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, make_scorer, confusion_matrix, classification_report \n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, minmax_scale\n",
    "from IPython.display import Image, HTML\n",
    "from matplotlib.ticker import NullFormatter\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (14,4)\n",
    "warnings.filterwarnings(action='ignore')\n",
    "%run Coronavirus_Music_Functions.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5113, 234)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('cleaned_data.csv')    \n",
    "original_df = data.rename(columns={\"Country_Country Name\": \"Country\"})\n",
    "original_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "both               3003\n",
       "listening only     1669\n",
       "neither             414\n",
       "performing only      27\n",
       "Name: Survey Info_Group, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of participants who completed the music listening and music making portions of the survey\n",
    "original_df['Survey Info_Group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Preprocessing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the amount of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Demographics_Health_Bored</th>\n",
       "      <td>47.897516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "Demographics_Health_Bored  47.897516"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find columns with 20% or more missing data\n",
    "missing = pd.DataFrame(original_df.isnull().sum()/original_df.shape[0])*100\n",
    "missing = missing[missing[0] >= 20]\n",
    "missing = missing.sort_values(by=[0], ascending=False)\n",
    "\n",
    "cols = [i for i in missing.index if not i.startswith('Making Music') \n",
    "        and not i.startswith('Music Listening') \n",
    "        and not i.startswith('Activities') ]\n",
    "missing.loc[cols,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete column with more than 20% missing data and other columns we don't need\n",
    "delete_cols = ['Demographics_Health_Bored', \n",
    "               'Demographics_Health_COVID Contact Binary', 'Demographics_COVID_Work Changed Binary', \n",
    "               'Country_Confirmed Cases', 'Country_Confirmed Deaths', 'Country_Containment Health Index',\n",
    "               'Country_Economic Support Index','Country_Government Response Index', 'Country_Stringency Index',\n",
    "               'ESM_ID','date', 'Demographics_General_Gender'] + [col for col in original_df.columns if '-No Response' in col or '-Prefer Not To Say' in col]\n",
    "data = original_df.drop(columns = delete_cols, axis = 1).reset_index(drop=True)\n",
    "data.shape\n",
    "\n",
    "original_df.rename(columns={'Demographics_General_Gender_binary': 'Demographics_General_Gender'}, inplace=True, errors=\"raise\")                                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get list of demographic columns\n",
    "demographics = ['ID','Country'] + [col for col in data.columns if col.startswith('Demographics_')]\n",
    "dem = data[demographics]\n",
    "dem.columns = dem.columns.str.lstrip('Demographics_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Health_Lonely</th>\n",
       "      <td>4.107178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health_Arousal</th>\n",
       "      <td>4.068062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health_Depressed</th>\n",
       "      <td>4.068062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health_Anxious</th>\n",
       "      <td>4.009388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health_Negative Valence</th>\n",
       "      <td>3.950714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health_Stressed</th>\n",
       "      <td>3.852924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health_Positive Valence</th>\n",
       "      <td>3.539996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality_Openness</th>\n",
       "      <td>3.363974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality_Agreeableness</th>\n",
       "      <td>3.363974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality_Conscientiousness</th>\n",
       "      <td>3.363974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0\n",
       "Health_Lonely                  4.107178\n",
       "Health_Arousal                 4.068062\n",
       "Health_Depressed               4.068062\n",
       "Health_Anxious                 4.009388\n",
       "Health_Negative Valence        3.950714\n",
       "Health_Stressed                3.852924\n",
       "Health_Positive Valence        3.539996\n",
       "Personality_Openness           3.363974\n",
       "Personality_Agreeableness      3.363974\n",
       "Personality_Conscientiousness  3.363974"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the amount of missing data for the demographic columns\n",
    "missing = pd.DataFrame(dem.isnull().sum()/dem.shape[0])*100\n",
    "missing = missing[missing[0] > 0]\n",
    "missing = missing.sort_values(by=[0], ascending=False)\n",
    "missing.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imputing missing variables\n",
    "continuous = []\n",
    "for column in missing.index:\n",
    "    if len(dem[column].unique()) > 3:\n",
    "        continuous.append(column)\n",
    "\n",
    "binary = [col for col in missing.index if col not in continuous]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# binary imputation -- median\n",
    "for column in binary:\n",
    "    dem[column] = dem.groupby([\"Country\"], sort = False)[column].apply(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examine distributions of continuous columns\n",
    "plt.figure(figsize=(20,20))\n",
    "for i, column in enumerate(continuous):\n",
    "    plt.subplot(5, 4, i+1)\n",
    "    sns.distplot(dem[column], hist = False, norm_hist = True, kde_kws={\"bw\":1})\n",
    "    plt.title(column.split('_')[1], size = 22)\n",
    "    plt.ylabel('KDE estimates', fontsize=16)\n",
    "    plt.xlabel('')\n",
    "    plt.tight_layout()\n",
    "    plt.tick_params(axis='both', which='major', labelsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# continuous imputation -- impute values based on the median for the participant's current country\n",
    "for i, column in enumerate(continuous):\n",
    "    dem[column] = dem.groupby([\"Country\"], sort = False)[column].apply(lambda x: x.fillna(x.median()))\n",
    "    \n",
    "dem.reset_index(inplace = True, drop = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 % of correlations > 0.90 in the demographic data.\n",
      "Highest (absolute value) correlation is 0.6966676876563982\n"
     ]
    }
   ],
   "source": [
    "# correlations\n",
    "cor_real = dem.corr()\n",
    "cor_abs = dem.corr().abs()\n",
    "triangle = cor_real.where(np.triu(np.ones(cor_real.shape), k = 1).astype(np.bool))\n",
    "\n",
    "cor = cor_abs.unstack()\n",
    "cor = cor.sort_values(kind=\"quicksort\", ascending = False)\n",
    "cor = pd.DataFrame(cor)\n",
    "cor.columns = ['cor']\n",
    "cor = cor.query('cor < 1')\n",
    "\n",
    "cor_high = cor.query('0.90 < cor < 1')\n",
    "print((len(cor_high)/len(cor))*100, \"% of correlations > 0.90 in the demographic data.\")\n",
    "print(\"Highest (absolute value) correlation is\", cor['cor'].max())\n",
    "\n",
    "# sns.heatmap(cor_real)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minmax Scaling by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting continuous/binary columns for scaling\n",
    "continuous = []\n",
    "for column in dem.columns:\n",
    "    if len(dem[column].unique()) > 3:\n",
    "        continuous.append(column)\n",
    "\n",
    "binary = [col for col in dem.columns if col not in continuous]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we only want to scale the continuous data (not binary or ID)\n",
    "col_names = [c for c in continuous if c not in ['ID']]\n",
    "to_scale = dem[col_names]\n",
    "\n",
    "# scale data by country\n",
    "for column in [c for c in col_names if c is not 'Country']:\n",
    "    to_scale[column] = to_scale.groupby([\"Country\"], sort = False)[column].transform(lambda x: minmax_scale(x.astype(float)))\n",
    "scaled = pd.DataFrame(to_scale, columns = col_names)\n",
    "# scaled.describe().round(3).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make dataframe with the binary columns\n",
    "binary_data = dem[binary]\n",
    "# binary_data.describe().round(3).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the fully processed dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_scaled = pd.concat([scaled, binary_data], axis=1)\n",
    "all_scaled = all_scaled.drop(columns=['Country'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Factor Analysis**\n",
    "\n",
    "***Only demographic, health, wellbeing/emotion, and personality characteristics were included in the factor analysis***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove music responses and country-level responses\n",
    "col_list = all_scaled.columns.tolist()\n",
    "col_list = [c for c in col_list if 'Music_' not in c]\n",
    "col_list.sort()\n",
    "\n",
    "all_psych = all_scaled[col_list]\n",
    "all_psych.columns = all_psych.columns.str.replace(r'^.*_', '', regex=True)\n",
    "all_psych.columns = all_psych.columns.str.replace(r'^.*-', '', regex=True)\n",
    "#all_psych.describe().round(1).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51 columns\n",
      "Bartlett’s test of sphericity: 67678.78956104333 0.0\n",
      "Kaiser-Meyer-Olkin test: 0.5483562530113852\n"
     ]
    }
   ],
   "source": [
    "# assessing how well the factor analysis explains the variables above\n",
    "chi_square_value, p_value = calculate_bartlett_sphericity(all_psych)\n",
    "kmo_all, kmo_model = calculate_kmo(all_psych)\n",
    "\n",
    "print(len(all_psych.columns), \"columns\")\n",
    "print(\"Bartlett’s test of sphericity:\", chi_square_value, p_value)\n",
    "print(\"Kaiser-Meyer-Olkin test:\", kmo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fb470849970>,\n",
       "  <matplotlib.axis.XTick at 0x7fb470849940>,\n",
       "  <matplotlib.axis.XTick at 0x7fb4708474c0>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e789d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e78ee0>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e8b430>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e8b940>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e8be50>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e913a0>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e918b0>,\n",
       "  <matplotlib.axis.XTick at 0x7fb454e91dc0>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scree plot\n",
    "fa = FactorAnalyzer()\n",
    "fa.fit(all_psych, len(all_psych.columns))\n",
    "eigenvalues, vectors = fa.get_eigenvalues()\n",
    "\n",
    "plt.plot(range(1, all_psych.shape[1]+1), eigenvalues)\n",
    "plt.scatter(range(1, all_psych.shape[1]+1), eigenvalues)\n",
    "plt.xlabel('Number of Factors')\n",
    "plt.ylabel('Eigenvalues')\n",
    "plt.title(\"Scree Plot\")\n",
    "plt.xticks(np.arange(0, 55, 5)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EFA Rotation -- 4 factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Variance</th>\n",
       "      <th>Variance Explained</th>\n",
       "      <th>Cumulative Variance</th>\n",
       "      <th>Top Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.0667738</td>\n",
       "      <td>0.0667738</td>\n",
       "      <td>[Depressed, Stressed, Anxious]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>0.0425464</td>\n",
       "      <td>0.10932</td>\n",
       "      <td>[Age, Parent, Student]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>0.0423694</td>\n",
       "      <td>0.15169</td>\n",
       "      <td>[Positive Valence, Arousal, Employed Full Time]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>0.0315459</td>\n",
       "      <td>0.183235</td>\n",
       "      <td>[Partner, Child, Alone]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variance Variance Explained Cumulative Variance  \\\n",
       "F1                0.0667738           0.0667738   \n",
       "F2                0.0425464             0.10932   \n",
       "F3                0.0423694             0.15169   \n",
       "F4                0.0315459            0.183235   \n",
       "\n",
       "Variance                                     Top Features  \n",
       "F1                         [Depressed, Stressed, Anxious]  \n",
       "F2                                 [Age, Parent, Student]  \n",
       "F3        [Positive Valence, Arousal, Employed Full Time]  \n",
       "F4                                [Partner, Child, Alone]  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_factors = 4\n",
    "factor_names = ['F1', 'F2', 'F3', 'F4']\n",
    "\n",
    "# quartimax rotation\n",
    "quartimax = FactorAnalyzer(num_factors, rotation = 'quartimax')\n",
    "all_quartimax = quartimax.fit_transform(all_psych)\n",
    "quartimax_loadings = quartimax.loadings_\n",
    "quartimax_communalities = quartimax.get_communalities()\n",
    "quartimax_variance = quartimax.get_factor_variance()\n",
    "\n",
    "# variable loading info\n",
    "quartimax_loading_DF = pd.DataFrame(columns = ['Demographic Feature'] + factor_names)\n",
    "quartimax_loading_DF['Demographic Feature'] = all_psych.columns\n",
    "for i, c in enumerate(all_psych.columns):\n",
    "    quartimax_loading_DF.iloc[i, 1:] = quartimax_loadings[i]\n",
    "\n",
    "# variance explained by factors\n",
    "quartimax_variance_DF = pd.DataFrame(columns = ['Variance'] + factor_names)\n",
    "quartimax_variance_DF['Variance'] = ['SS Loadings', 'Variance Explained', 'Cumulative Variance']\n",
    "for i, c in enumerate(quartimax_variance_DF.index):\n",
    "    quartimax_variance_DF.iloc[i, 1:] = quartimax_variance[i]\n",
    "    \n",
    "# find top features for each factor\n",
    "test = quartimax_loading_DF.set_index(['Demographic Feature'])\n",
    "test = test.apply(pd.to_numeric, errors='coerce')\n",
    "test = test.abs()\n",
    "variance_new = quartimax_variance_DF.loc[1:,:].set_index(['Variance']).T\n",
    "\n",
    "variance_new['Top Features'] = \"\"\n",
    "variance_new['Top Features'] = variance_new['Top Features'].astype('object')\n",
    "\n",
    "for c in variance_new.index.tolist():\n",
    "    max_vals = test[c].nlargest(3).index.tolist()\n",
    "    variance_new.at[c, 'Top Features'] = max_vals\n",
    "    \n",
    "variance_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EFA Rotation -- 5 factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Variance</th>\n",
       "      <th>Variance Explained</th>\n",
       "      <th>Cumulative Variance</th>\n",
       "      <th>Top Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.0667671</td>\n",
       "      <td>0.0667671</td>\n",
       "      <td>[Depressed, Stressed, Anxious]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>0.0425749</td>\n",
       "      <td>0.109342</td>\n",
       "      <td>[Age, Parent, Student]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>0.0412111</td>\n",
       "      <td>0.150553</td>\n",
       "      <td>[Positive Valence, Arousal, Employed Full Time]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>0.0314673</td>\n",
       "      <td>0.18202</td>\n",
       "      <td>[Partner, Child, Alone]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>0.0268332</td>\n",
       "      <td>0.208854</td>\n",
       "      <td>[Suburban, Urban, Rural]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variance Variance Explained Cumulative Variance  \\\n",
       "F1                0.0667671           0.0667671   \n",
       "F2                0.0425749            0.109342   \n",
       "F3                0.0412111            0.150553   \n",
       "F4                0.0314673             0.18202   \n",
       "F5                0.0268332            0.208854   \n",
       "\n",
       "Variance                                     Top Features  \n",
       "F1                         [Depressed, Stressed, Anxious]  \n",
       "F2                                 [Age, Parent, Student]  \n",
       "F3        [Positive Valence, Arousal, Employed Full Time]  \n",
       "F4                                [Partner, Child, Alone]  \n",
       "F5                               [Suburban, Urban, Rural]  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_factors = 5\n",
    "factor_names = ['F1', 'F2', 'F3', 'F4', 'F5']\n",
    "\n",
    "# quartimax rotation\n",
    "quartimax = FactorAnalyzer(num_factors, rotation = 'quartimax')\n",
    "all_quartimax = quartimax.fit_transform(all_psych)\n",
    "quartimax_loadings = quartimax.loadings_\n",
    "quartimax_communalities = quartimax.get_communalities()\n",
    "quartimax_variance = quartimax.get_factor_variance()\n",
    "\n",
    "# variable loading info\n",
    "quartimax_loading_DF = pd.DataFrame(columns = ['Demographic Feature'] + factor_names)\n",
    "quartimax_loading_DF['Demographic Feature'] = all_psych.columns\n",
    "for i, c in enumerate(all_psych.columns):\n",
    "    quartimax_loading_DF.iloc[i, 1:] = quartimax_loadings[i]\n",
    "\n",
    "# variance explained by factors\n",
    "quartimax_variance_DF = pd.DataFrame(columns = ['Variance'] + factor_names)\n",
    "quartimax_variance_DF['Variance'] = ['SS Loadings', 'Variance Explained', 'Cumulative Variance']\n",
    "for i, c in enumerate(quartimax_variance_DF.index):\n",
    "    quartimax_variance_DF.iloc[i, 1:] = quartimax_variance[i]\n",
    "    \n",
    "# find top features for each factor\n",
    "test = quartimax_loading_DF.set_index(['Demographic Feature'])\n",
    "test = test.apply(pd.to_numeric, errors='coerce')\n",
    "test = test.abs()\n",
    "variance_new = quartimax_variance_DF.loc[1:,:].set_index(['Variance']).T\n",
    "\n",
    "variance_new['Top Features'] = \"\"\n",
    "variance_new['Top Features'] = variance_new['Top Features'].astype('object')\n",
    "\n",
    "for c in variance_new.index.tolist():\n",
    "    max_vals = test[c].nlargest(3).index.tolist()\n",
    "    variance_new.at[c, 'Top Features'] = max_vals\n",
    "    \n",
    "variance_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EFA Rotation -- 6 factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Variance</th>\n",
       "      <th>Variance Explained</th>\n",
       "      <th>Cumulative Variance</th>\n",
       "      <th>Top Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.0662594</td>\n",
       "      <td>0.0662594</td>\n",
       "      <td>[Depressed, Stressed, Anxious]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>0.0429912</td>\n",
       "      <td>0.109251</td>\n",
       "      <td>[Age, Parent, Retired]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>0.0363433</td>\n",
       "      <td>0.145594</td>\n",
       "      <td>[Positive Valence, Arousal, General Health]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>0.0310213</td>\n",
       "      <td>0.176615</td>\n",
       "      <td>[Partner, Child, Alone]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>0.0306774</td>\n",
       "      <td>0.207293</td>\n",
       "      <td>[Employed Full Time, Retired, Now Work From Home]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F6</th>\n",
       "      <td>0.0268776</td>\n",
       "      <td>0.23417</td>\n",
       "      <td>[Suburban, Urban, Rural]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variance Variance Explained Cumulative Variance  \\\n",
       "F1                0.0662594           0.0662594   \n",
       "F2                0.0429912            0.109251   \n",
       "F3                0.0363433            0.145594   \n",
       "F4                0.0310213            0.176615   \n",
       "F5                0.0306774            0.207293   \n",
       "F6                0.0268776             0.23417   \n",
       "\n",
       "Variance                                       Top Features  \n",
       "F1                           [Depressed, Stressed, Anxious]  \n",
       "F2                                   [Age, Parent, Retired]  \n",
       "F3              [Positive Valence, Arousal, General Health]  \n",
       "F4                                  [Partner, Child, Alone]  \n",
       "F5        [Employed Full Time, Retired, Now Work From Home]  \n",
       "F6                                 [Suburban, Urban, Rural]  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_factors = 6\n",
    "factor_names = ['F1', 'F2', 'F3', 'F4', 'F5', 'F6']\n",
    "\n",
    "# quartimax rotation\n",
    "quartimax = FactorAnalyzer(num_factors, rotation = 'quartimax')\n",
    "all_quartimax = quartimax.fit_transform(all_psych)\n",
    "quartimax_loadings = quartimax.loadings_\n",
    "quartimax_communalities = quartimax.get_communalities()\n",
    "quartimax_variance = quartimax.get_factor_variance()\n",
    "\n",
    "# variable loading info\n",
    "quartimax_loading_DF = pd.DataFrame(columns = ['Demographic Feature'] + factor_names)\n",
    "quartimax_loading_DF['Demographic Feature'] = all_psych.columns\n",
    "for i, c in enumerate(all_psych.columns):\n",
    "    quartimax_loading_DF.iloc[i, 1:] = quartimax_loadings[i]\n",
    "\n",
    "# variance explained by factors\n",
    "quartimax_variance_DF = pd.DataFrame(columns = ['Variance'] + factor_names)\n",
    "quartimax_variance_DF['Variance'] = ['SS Loadings', 'Variance Explained', 'Cumulative Variance']\n",
    "for i, c in enumerate(quartimax_variance_DF.index):\n",
    "    quartimax_variance_DF.iloc[i, 1:] = quartimax_variance[i]\n",
    "    \n",
    "# find top features for each factor\n",
    "test = quartimax_loading_DF.set_index(['Demographic Feature'])\n",
    "test = test.apply(pd.to_numeric, errors='coerce')\n",
    "test = test.abs()\n",
    "variance_new = quartimax_variance_DF.loc[1:,:].set_index(['Variance']).T\n",
    "\n",
    "variance_new['Top Features'] = \"\"\n",
    "variance_new['Top Features'] = variance_new['Top Features'].astype('object')\n",
    "\n",
    "for c in variance_new.index.tolist():\n",
    "    max_vals = test[c].nlargest(3).index.tolist()\n",
    "    variance_new.at[c, 'Top Features'] = max_vals\n",
    "    \n",
    "variance_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EFA Rotation -- 7 factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Variance</th>\n",
       "      <th>Variance Explained</th>\n",
       "      <th>Cumulative Variance</th>\n",
       "      <th>Top Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.0661582</td>\n",
       "      <td>0.0661582</td>\n",
       "      <td>[Depressed, Stressed, Anxious]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>0.0424616</td>\n",
       "      <td>0.10862</td>\n",
       "      <td>[Age, Parent, Retired]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>0.0354102</td>\n",
       "      <td>0.14403</td>\n",
       "      <td>[Positive Valence, Arousal, General Health]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>0.0326698</td>\n",
       "      <td>0.1767</td>\n",
       "      <td>[Employed Full Time, Retired, Now Work From Home]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>0.0305911</td>\n",
       "      <td>0.207291</td>\n",
       "      <td>[Partner, Child, Alone]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F6</th>\n",
       "      <td>0.0289208</td>\n",
       "      <td>0.236212</td>\n",
       "      <td>[Suburban, Urban, Rural]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F7</th>\n",
       "      <td>0.0278536</td>\n",
       "      <td>0.264065</td>\n",
       "      <td>[Urban, Rural, None]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variance Variance Explained Cumulative Variance  \\\n",
       "F1                0.0661582           0.0661582   \n",
       "F2                0.0424616             0.10862   \n",
       "F3                0.0354102             0.14403   \n",
       "F4                0.0326698              0.1767   \n",
       "F5                0.0305911            0.207291   \n",
       "F6                0.0289208            0.236212   \n",
       "F7                0.0278536            0.264065   \n",
       "\n",
       "Variance                                       Top Features  \n",
       "F1                           [Depressed, Stressed, Anxious]  \n",
       "F2                                   [Age, Parent, Retired]  \n",
       "F3              [Positive Valence, Arousal, General Health]  \n",
       "F4        [Employed Full Time, Retired, Now Work From Home]  \n",
       "F5                                  [Partner, Child, Alone]  \n",
       "F6                                 [Suburban, Urban, Rural]  \n",
       "F7                                     [Urban, Rural, None]  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_factors = 7\n",
    "factor_names = ['F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7']\n",
    "\n",
    "# quartimax rotation\n",
    "quartimax = FactorAnalyzer(num_factors, rotation = 'quartimax')\n",
    "all_quartimax = quartimax.fit_transform(all_psych)\n",
    "quartimax_loadings = quartimax.loadings_\n",
    "quartimax_communalities = quartimax.get_communalities()\n",
    "quartimax_variance = quartimax.get_factor_variance()\n",
    "\n",
    "# variable loading info\n",
    "quartimax_loading_DF = pd.DataFrame(columns = ['Demographic Feature'] + factor_names)\n",
    "quartimax_loading_DF['Demographic Feature'] = all_psych.columns\n",
    "for i, c in enumerate(all_psych.columns):\n",
    "    quartimax_loading_DF.iloc[i, 1:] = quartimax_loadings[i]\n",
    "\n",
    "# variance explained by factors\n",
    "quartimax_variance_DF = pd.DataFrame(columns = ['Variance'] + factor_names)\n",
    "quartimax_variance_DF['Variance'] = ['SS Loadings', 'Variance Explained', 'Cumulative Variance']\n",
    "for i, c in enumerate(quartimax_variance_DF.index):\n",
    "    quartimax_variance_DF.iloc[i, 1:] = quartimax_variance[i]\n",
    "    \n",
    "# find top features for each factor\n",
    "test = quartimax_loading_DF.set_index(['Demographic Feature'])\n",
    "test = test.apply(pd.to_numeric, errors='coerce')\n",
    "test = test.abs()\n",
    "variance_new = quartimax_variance_DF.loc[1:,:].set_index(['Variance']).T\n",
    "\n",
    "variance_new['Top Features'] = \"\"\n",
    "variance_new['Top Features'] = variance_new['Top Features'].astype('object')\n",
    "\n",
    "for c in variance_new.index.tolist():\n",
    "    max_vals = test[c].nlargest(3).index.tolist()\n",
    "    variance_new.at[c, 'Top Features'] = max_vals\n",
    "    \n",
    "variance_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Digging Deeper into the 6-factor solution\n",
    "\n",
    "This is the factor solution used in the manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Variance</th>\n",
       "      <th>Variance Explained</th>\n",
       "      <th>Cumulative Variance</th>\n",
       "      <th>Top Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.0662594</td>\n",
       "      <td>0.0662594</td>\n",
       "      <td>[Depressed, Stressed, Anxious]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2</th>\n",
       "      <td>0.0429912</td>\n",
       "      <td>0.109251</td>\n",
       "      <td>[Age, Parent, Retired]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>0.0363433</td>\n",
       "      <td>0.145594</td>\n",
       "      <td>[Positive Valence, Arousal, General Health]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>0.0310213</td>\n",
       "      <td>0.176615</td>\n",
       "      <td>[Partner, Child, Alone]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>0.0306774</td>\n",
       "      <td>0.207293</td>\n",
       "      <td>[Employed Full Time, Retired, Now Work From Home]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F6</th>\n",
       "      <td>0.0268776</td>\n",
       "      <td>0.23417</td>\n",
       "      <td>[Suburban, Urban, Rural]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variance Variance Explained Cumulative Variance  \\\n",
       "F1                0.0662594           0.0662594   \n",
       "F2                0.0429912            0.109251   \n",
       "F3                0.0363433            0.145594   \n",
       "F4                0.0310213            0.176615   \n",
       "F5                0.0306774            0.207293   \n",
       "F6                0.0268776             0.23417   \n",
       "\n",
       "Variance                                       Top Features  \n",
       "F1                           [Depressed, Stressed, Anxious]  \n",
       "F2                                   [Age, Parent, Retired]  \n",
       "F3              [Positive Valence, Arousal, General Health]  \n",
       "F4                                  [Partner, Child, Alone]  \n",
       "F5        [Employed Full Time, Retired, Now Work From Home]  \n",
       "F6                                 [Suburban, Urban, Rural]  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_factors = 6\n",
    "factor_names = ['F1', 'F2', 'F3', 'F4', 'F5', 'F6']\n",
    "\n",
    "# quartimax rotation\n",
    "quartimax = FactorAnalyzer(num_factors, rotation = 'quartimax')\n",
    "all_quartimax = quartimax.fit_transform(all_psych)\n",
    "quartimax_loadings = quartimax.loadings_\n",
    "quartimax_communalities = quartimax.get_communalities()\n",
    "quartimax_variance = quartimax.get_factor_variance()\n",
    "\n",
    "# variable loading info\n",
    "quartimax_loading_DF = pd.DataFrame(columns = ['Demographic Feature'] + factor_names)\n",
    "quartimax_loading_DF['Demographic Feature'] = all_psych.columns\n",
    "for i, c in enumerate(all_psych.columns):\n",
    "    quartimax_loading_DF.iloc[i, 1:] = quartimax_loadings[i]\n",
    "\n",
    "# variance explained by factors\n",
    "quartimax_variance_DF = pd.DataFrame(columns = ['Variance'] + factor_names)\n",
    "quartimax_variance_DF['Variance'] = ['SS Loadings', 'Variance Explained', 'Cumulative Variance']\n",
    "for i, c in enumerate(quartimax_variance_DF.index):\n",
    "    quartimax_variance_DF.iloc[i, 1:] = quartimax_variance[i]\n",
    "    \n",
    "# find top features for each factor\n",
    "test = quartimax_loading_DF.set_index(['Demographic Feature'])\n",
    "test = test.apply(pd.to_numeric, errors='coerce')\n",
    "test = test.abs()\n",
    "variance_new = quartimax_variance_DF.loc[1:,:].set_index(['Variance']).T\n",
    "\n",
    "variance_new['Top Features'] = \"\"\n",
    "variance_new['Top Features'] = variance_new['Top Features'].astype('object')\n",
    "\n",
    "for c in variance_new.index.tolist():\n",
    "    max_vals = test[c].nlargest(3).index.tolist()\n",
    "    variance_new.at[c, 'Top Features'] = max_vals\n",
    "    \n",
    "variance_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Variance</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SS Loadings</td>\n",
       "      <td>3.37923</td>\n",
       "      <td>2.19255</td>\n",
       "      <td>1.85351</td>\n",
       "      <td>1.58209</td>\n",
       "      <td>1.56455</td>\n",
       "      <td>1.37076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Variance Explained</td>\n",
       "      <td>0.0662594</td>\n",
       "      <td>0.0429912</td>\n",
       "      <td>0.0363433</td>\n",
       "      <td>0.0310213</td>\n",
       "      <td>0.0306774</td>\n",
       "      <td>0.0268776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cumulative Variance</td>\n",
       "      <td>0.0662594</td>\n",
       "      <td>0.109251</td>\n",
       "      <td>0.145594</td>\n",
       "      <td>0.176615</td>\n",
       "      <td>0.207293</td>\n",
       "      <td>0.23417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variance         F1         F2         F3         F4         F5  \\\n",
       "0          SS Loadings    3.37923    2.19255    1.85351    1.58209    1.56455   \n",
       "1   Variance Explained  0.0662594  0.0429912  0.0363433  0.0310213  0.0306774   \n",
       "2  Cumulative Variance  0.0662594   0.109251   0.145594   0.176615   0.207293   \n",
       "\n",
       "          F6  \n",
       "0    1.37076  \n",
       "1  0.0268776  \n",
       "2    0.23417  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartimax_variance_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Demographic Feature</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Current Place Home</td>\n",
       "      <td>-0.00431701</td>\n",
       "      <td>-0.140202</td>\n",
       "      <td>0.0439915</td>\n",
       "      <td>0.115686</td>\n",
       "      <td>0.0257655</td>\n",
       "      <td>-0.0062972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alone</td>\n",
       "      <td>0.00791044</td>\n",
       "      <td>-0.186843</td>\n",
       "      <td>-0.0401782</td>\n",
       "      <td>-0.488386</td>\n",
       "      <td>0.122442</td>\n",
       "      <td>0.00589809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Child</td>\n",
       "      <td>0.0748446</td>\n",
       "      <td>0.0223418</td>\n",
       "      <td>0.12049</td>\n",
       "      <td>0.550035</td>\n",
       "      <td>0.164168</td>\n",
       "      <td>-0.0235628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elderly</td>\n",
       "      <td>-0.0138292</td>\n",
       "      <td>0.0956409</td>\n",
       "      <td>0.0325344</td>\n",
       "      <td>-0.0267818</td>\n",
       "      <td>-0.030966</td>\n",
       "      <td>-0.0170509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friend</td>\n",
       "      <td>0.06479</td>\n",
       "      <td>0.112194</td>\n",
       "      <td>0.029229</td>\n",
       "      <td>-0.0660482</td>\n",
       "      <td>0.0234066</td>\n",
       "      <td>-0.012096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other</td>\n",
       "      <td>-0.00397399</td>\n",
       "      <td>-0.0576582</td>\n",
       "      <td>-0.0284526</td>\n",
       "      <td>-0.0280042</td>\n",
       "      <td>-0.0913733</td>\n",
       "      <td>-0.0215368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Parent</td>\n",
       "      <td>0.0269043</td>\n",
       "      <td>0.541933</td>\n",
       "      <td>0.0832828</td>\n",
       "      <td>-0.227729</td>\n",
       "      <td>-0.137661</td>\n",
       "      <td>0.0101938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Partner</td>\n",
       "      <td>-0.0235574</td>\n",
       "      <td>-0.302143</td>\n",
       "      <td>0.053727</td>\n",
       "      <td>0.696275</td>\n",
       "      <td>0.0622544</td>\n",
       "      <td>-0.0232338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pet</td>\n",
       "      <td>-0.00145937</td>\n",
       "      <td>0.00735372</td>\n",
       "      <td>-0.0561349</td>\n",
       "      <td>0.201511</td>\n",
       "      <td>0.00864353</td>\n",
       "      <td>0.00371133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shared</td>\n",
       "      <td>0.0534547</td>\n",
       "      <td>0.106545</td>\n",
       "      <td>-0.0373373</td>\n",
       "      <td>-0.0674095</td>\n",
       "      <td>0.00693788</td>\n",
       "      <td>-0.02136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SibCous</td>\n",
       "      <td>0.0283424</td>\n",
       "      <td>0.310519</td>\n",
       "      <td>0.0173983</td>\n",
       "      <td>-0.136953</td>\n",
       "      <td>-0.115557</td>\n",
       "      <td>0.0372286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Number Times Leave</td>\n",
       "      <td>-0.0140966</td>\n",
       "      <td>-0.222044</td>\n",
       "      <td>0.0816163</td>\n",
       "      <td>-0.0748188</td>\n",
       "      <td>0.188978</td>\n",
       "      <td>0.0222915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Homeschool Children</td>\n",
       "      <td>0.00280707</td>\n",
       "      <td>0.0208786</td>\n",
       "      <td>-0.0623389</td>\n",
       "      <td>0.281379</td>\n",
       "      <td>-0.189249</td>\n",
       "      <td>0.00823274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Job At Risk</td>\n",
       "      <td>0.140211</td>\n",
       "      <td>0.10994</td>\n",
       "      <td>0.0880609</td>\n",
       "      <td>0.060844</td>\n",
       "      <td>0.0559679</td>\n",
       "      <td>-0.0608346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lost Job</td>\n",
       "      <td>0.120797</td>\n",
       "      <td>0.136455</td>\n",
       "      <td>0.0931421</td>\n",
       "      <td>0.0609677</td>\n",
       "      <td>-0.0563238</td>\n",
       "      <td>-0.047117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lower Income</td>\n",
       "      <td>0.137255</td>\n",
       "      <td>0.115992</td>\n",
       "      <td>0.0562663</td>\n",
       "      <td>0.109021</td>\n",
       "      <td>0.0300757</td>\n",
       "      <td>-0.0551819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Now Work From Home</td>\n",
       "      <td>0.0271741</td>\n",
       "      <td>0.301032</td>\n",
       "      <td>0.0493586</td>\n",
       "      <td>0.081652</td>\n",
       "      <td>0.282178</td>\n",
       "      <td>-0.0200919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Reduced Hours</td>\n",
       "      <td>0.0844065</td>\n",
       "      <td>0.118006</td>\n",
       "      <td>0.127066</td>\n",
       "      <td>0.10033</td>\n",
       "      <td>0.207384</td>\n",
       "      <td>-0.0411267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Still Go To Work</td>\n",
       "      <td>-0.0621142</td>\n",
       "      <td>-0.352119</td>\n",
       "      <td>0.00656567</td>\n",
       "      <td>-0.184417</td>\n",
       "      <td>0.148024</td>\n",
       "      <td>0.0236504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Still Work From Home</td>\n",
       "      <td>-0.0147179</td>\n",
       "      <td>-0.0489588</td>\n",
       "      <td>0.0964623</td>\n",
       "      <td>-0.0739292</td>\n",
       "      <td>-0.242333</td>\n",
       "      <td>-0.0036499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.095602</td>\n",
       "      <td>-0.750324</td>\n",
       "      <td>-0.0210584</td>\n",
       "      <td>0.0841966</td>\n",
       "      <td>-0.114088</td>\n",
       "      <td>0.0105488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rural</td>\n",
       "      <td>-0.0473155</td>\n",
       "      <td>-0.151287</td>\n",
       "      <td>-0.0963051</td>\n",
       "      <td>0.0619049</td>\n",
       "      <td>-0.181324</td>\n",
       "      <td>-0.184133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Suburban</td>\n",
       "      <td>0.0137673</td>\n",
       "      <td>0.0149557</td>\n",
       "      <td>-0.021891</td>\n",
       "      <td>-0.0157862</td>\n",
       "      <td>-0.00757588</td>\n",
       "      <td>1.03181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Urban</td>\n",
       "      <td>0.0321714</td>\n",
       "      <td>0.137395</td>\n",
       "      <td>0.134246</td>\n",
       "      <td>-0.0429775</td>\n",
       "      <td>0.20216</td>\n",
       "      <td>-0.470764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Education Years</td>\n",
       "      <td>-0.012405</td>\n",
       "      <td>0.211573</td>\n",
       "      <td>-0.203574</td>\n",
       "      <td>0.0993717</td>\n",
       "      <td>0.0693306</td>\n",
       "      <td>0.0581154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Employed Full Time</td>\n",
       "      <td>0.0364027</td>\n",
       "      <td>0.0379678</td>\n",
       "      <td>0.11784</td>\n",
       "      <td>0.0432496</td>\n",
       "      <td>0.845723</td>\n",
       "      <td>-0.0382679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Employed Part Time</td>\n",
       "      <td>0.0532429</td>\n",
       "      <td>0.0350525</td>\n",
       "      <td>-0.0473218</td>\n",
       "      <td>0.0906832</td>\n",
       "      <td>-0.117318</td>\n",
       "      <td>0.038592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Home Maker Caregiver</td>\n",
       "      <td>0.00513138</td>\n",
       "      <td>-0.0387784</td>\n",
       "      <td>-0.0297963</td>\n",
       "      <td>0.197265</td>\n",
       "      <td>-0.209681</td>\n",
       "      <td>0.0151007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Retired</td>\n",
       "      <td>-0.113055</td>\n",
       "      <td>-0.47298</td>\n",
       "      <td>-0.0497995</td>\n",
       "      <td>-0.113722</td>\n",
       "      <td>-0.330786</td>\n",
       "      <td>0.0143501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Self Employed</td>\n",
       "      <td>-0.00395838</td>\n",
       "      <td>0.0556861</td>\n",
       "      <td>0.0356902</td>\n",
       "      <td>0.0572485</td>\n",
       "      <td>-0.10539</td>\n",
       "      <td>-0.0583307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Student</td>\n",
       "      <td>0.0125083</td>\n",
       "      <td>0.460618</td>\n",
       "      <td>-0.0573275</td>\n",
       "      <td>-0.230276</td>\n",
       "      <td>-0.210182</td>\n",
       "      <td>0.0758802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>binary</td>\n",
       "      <td>-0.154081</td>\n",
       "      <td>-0.046078</td>\n",
       "      <td>0.0880345</td>\n",
       "      <td>-0.127833</td>\n",
       "      <td>0.173616</td>\n",
       "      <td>-0.0156655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Anxious</td>\n",
       "      <td>0.786135</td>\n",
       "      <td>0.0370611</td>\n",
       "      <td>-0.038175</td>\n",
       "      <td>0.0781136</td>\n",
       "      <td>-0.00591308</td>\n",
       "      <td>0.0106413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Arousal</td>\n",
       "      <td>-0.126678</td>\n",
       "      <td>0.0019301</td>\n",
       "      <td>0.682302</td>\n",
       "      <td>0.0347204</td>\n",
       "      <td>0.0847371</td>\n",
       "      <td>-0.0368373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Depressed</td>\n",
       "      <td>0.806465</td>\n",
       "      <td>0.04468</td>\n",
       "      <td>-0.0134649</td>\n",
       "      <td>-0.0326265</td>\n",
       "      <td>0.0357809</td>\n",
       "      <td>-0.00135338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>General Health</td>\n",
       "      <td>-0.180237</td>\n",
       "      <td>0.082537</td>\n",
       "      <td>0.511071</td>\n",
       "      <td>0.0538183</td>\n",
       "      <td>0.0655006</td>\n",
       "      <td>-0.0318403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>I Am Infected</td>\n",
       "      <td>0.17185</td>\n",
       "      <td>-0.00468427</td>\n",
       "      <td>0.26813</td>\n",
       "      <td>-0.0291131</td>\n",
       "      <td>0.0931099</td>\n",
       "      <td>-0.0660335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>I Am Mourning A Loss</td>\n",
       "      <td>0.115691</td>\n",
       "      <td>0.0577583</td>\n",
       "      <td>0.142938</td>\n",
       "      <td>0.0358986</td>\n",
       "      <td>0.0419289</td>\n",
       "      <td>-0.0190353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>I Have Professional Contact</td>\n",
       "      <td>0.16364</td>\n",
       "      <td>0.0293327</td>\n",
       "      <td>0.214666</td>\n",
       "      <td>0.00663734</td>\n",
       "      <td>0.173839</td>\n",
       "      <td>-0.0371681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>I Live With Someone</td>\n",
       "      <td>0.174496</td>\n",
       "      <td>0.0406759</td>\n",
       "      <td>0.306231</td>\n",
       "      <td>0.00301895</td>\n",
       "      <td>0.0686235</td>\n",
       "      <td>-0.0600815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>None</td>\n",
       "      <td>-0.161689</td>\n",
       "      <td>-0.375111</td>\n",
       "      <td>-0.23134</td>\n",
       "      <td>-0.0858862</td>\n",
       "      <td>-0.1448</td>\n",
       "      <td>-0.0139271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Someone I Am Close To</td>\n",
       "      <td>0.11115</td>\n",
       "      <td>0.0817654</td>\n",
       "      <td>0.0418047</td>\n",
       "      <td>0.0693124</td>\n",
       "      <td>0.0586888</td>\n",
       "      <td>-0.00782324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Lonely</td>\n",
       "      <td>0.679376</td>\n",
       "      <td>0.0110622</td>\n",
       "      <td>-0.00126805</td>\n",
       "      <td>-0.161251</td>\n",
       "      <td>0.0198128</td>\n",
       "      <td>0.00937898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Negative Valence</td>\n",
       "      <td>0.714915</td>\n",
       "      <td>-0.0151807</td>\n",
       "      <td>-0.182109</td>\n",
       "      <td>0.00472309</td>\n",
       "      <td>0.00261814</td>\n",
       "      <td>0.00908042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Positive Valence</td>\n",
       "      <td>-0.172663</td>\n",
       "      <td>0.040722</td>\n",
       "      <td>0.730979</td>\n",
       "      <td>0.0554018</td>\n",
       "      <td>0.0938936</td>\n",
       "      <td>-0.0424614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Stressed</td>\n",
       "      <td>0.792577</td>\n",
       "      <td>0.0553557</td>\n",
       "      <td>-0.0508624</td>\n",
       "      <td>0.0603375</td>\n",
       "      <td>0.015638</td>\n",
       "      <td>0.00358839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>-0.178488</td>\n",
       "      <td>0.0114367</td>\n",
       "      <td>-0.130039</td>\n",
       "      <td>0.0961673</td>\n",
       "      <td>0.0462312</td>\n",
       "      <td>0.0531763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>-0.147919</td>\n",
       "      <td>-0.114947</td>\n",
       "      <td>-0.159552</td>\n",
       "      <td>0.160803</td>\n",
       "      <td>0.0800399</td>\n",
       "      <td>0.0231766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>-0.0979234</td>\n",
       "      <td>-0.0410796</td>\n",
       "      <td>0.0199242</td>\n",
       "      <td>0.0941599</td>\n",
       "      <td>0.0981985</td>\n",
       "      <td>-0.00369044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Neuroticism</td>\n",
       "      <td>0.309302</td>\n",
       "      <td>0.0623828</td>\n",
       "      <td>-0.126133</td>\n",
       "      <td>-0.0298249</td>\n",
       "      <td>-0.155303</td>\n",
       "      <td>0.0165737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Openness</td>\n",
       "      <td>0.0108431</td>\n",
       "      <td>0.110988</td>\n",
       "      <td>-0.118041</td>\n",
       "      <td>-0.0147023</td>\n",
       "      <td>-0.00246284</td>\n",
       "      <td>-0.000465984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Demographic Feature          F1          F2          F3  \\\n",
       "0            Current Place Home -0.00431701   -0.140202   0.0439915   \n",
       "1                         Alone  0.00791044   -0.186843  -0.0401782   \n",
       "2                         Child   0.0748446   0.0223418     0.12049   \n",
       "3                       Elderly  -0.0138292   0.0956409   0.0325344   \n",
       "4                        Friend     0.06479    0.112194    0.029229   \n",
       "5                         Other -0.00397399  -0.0576582  -0.0284526   \n",
       "6                        Parent   0.0269043    0.541933   0.0832828   \n",
       "7                       Partner  -0.0235574   -0.302143    0.053727   \n",
       "8                           Pet -0.00145937  0.00735372  -0.0561349   \n",
       "9                        Shared   0.0534547    0.106545  -0.0373373   \n",
       "10                      SibCous   0.0283424    0.310519   0.0173983   \n",
       "11           Number Times Leave  -0.0140966   -0.222044   0.0816163   \n",
       "12          Homeschool Children  0.00280707   0.0208786  -0.0623389   \n",
       "13                  Job At Risk    0.140211     0.10994   0.0880609   \n",
       "14                     Lost Job    0.120797    0.136455   0.0931421   \n",
       "15                 Lower Income    0.137255    0.115992   0.0562663   \n",
       "16           Now Work From Home   0.0271741    0.301032   0.0493586   \n",
       "17                Reduced Hours   0.0844065    0.118006    0.127066   \n",
       "18             Still Go To Work  -0.0621142   -0.352119  0.00656567   \n",
       "19         Still Work From Home  -0.0147179  -0.0489588   0.0964623   \n",
       "20                          Age   -0.095602   -0.750324  -0.0210584   \n",
       "21                        Rural  -0.0473155   -0.151287  -0.0963051   \n",
       "22                     Suburban   0.0137673   0.0149557   -0.021891   \n",
       "23                        Urban   0.0321714    0.137395    0.134246   \n",
       "24              Education Years   -0.012405    0.211573   -0.203574   \n",
       "25           Employed Full Time   0.0364027   0.0379678     0.11784   \n",
       "26           Employed Part Time   0.0532429   0.0350525  -0.0473218   \n",
       "27         Home Maker Caregiver  0.00513138  -0.0387784  -0.0297963   \n",
       "28                      Retired   -0.113055    -0.47298  -0.0497995   \n",
       "29                Self Employed -0.00395838   0.0556861   0.0356902   \n",
       "30                      Student   0.0125083    0.460618  -0.0573275   \n",
       "31                       binary   -0.154081   -0.046078   0.0880345   \n",
       "32                      Anxious    0.786135   0.0370611   -0.038175   \n",
       "33                      Arousal   -0.126678   0.0019301    0.682302   \n",
       "34                    Depressed    0.806465     0.04468  -0.0134649   \n",
       "35               General Health   -0.180237    0.082537    0.511071   \n",
       "36                I Am Infected     0.17185 -0.00468427     0.26813   \n",
       "37         I Am Mourning A Loss    0.115691   0.0577583    0.142938   \n",
       "38  I Have Professional Contact     0.16364   0.0293327    0.214666   \n",
       "39          I Live With Someone    0.174496   0.0406759    0.306231   \n",
       "40                         None   -0.161689   -0.375111    -0.23134   \n",
       "41        Someone I Am Close To     0.11115   0.0817654   0.0418047   \n",
       "42                       Lonely    0.679376   0.0110622 -0.00126805   \n",
       "43             Negative Valence    0.714915  -0.0151807   -0.182109   \n",
       "44             Positive Valence   -0.172663    0.040722    0.730979   \n",
       "45                     Stressed    0.792577   0.0553557  -0.0508624   \n",
       "46                Agreeableness   -0.178488   0.0114367   -0.130039   \n",
       "47            Conscientiousness   -0.147919   -0.114947   -0.159552   \n",
       "48                 Extraversion  -0.0979234  -0.0410796   0.0199242   \n",
       "49                  Neuroticism    0.309302   0.0623828   -0.126133   \n",
       "50                     Openness   0.0108431    0.110988   -0.118041   \n",
       "\n",
       "            F4          F5           F6  \n",
       "0     0.115686   0.0257655   -0.0062972  \n",
       "1    -0.488386    0.122442   0.00589809  \n",
       "2     0.550035    0.164168   -0.0235628  \n",
       "3   -0.0267818   -0.030966   -0.0170509  \n",
       "4   -0.0660482   0.0234066    -0.012096  \n",
       "5   -0.0280042  -0.0913733   -0.0215368  \n",
       "6    -0.227729   -0.137661    0.0101938  \n",
       "7     0.696275   0.0622544   -0.0232338  \n",
       "8     0.201511  0.00864353   0.00371133  \n",
       "9   -0.0674095  0.00693788     -0.02136  \n",
       "10   -0.136953   -0.115557    0.0372286  \n",
       "11  -0.0748188    0.188978    0.0222915  \n",
       "12    0.281379   -0.189249   0.00823274  \n",
       "13    0.060844   0.0559679   -0.0608346  \n",
       "14   0.0609677  -0.0563238    -0.047117  \n",
       "15    0.109021   0.0300757   -0.0551819  \n",
       "16    0.081652    0.282178   -0.0200919  \n",
       "17     0.10033    0.207384   -0.0411267  \n",
       "18   -0.184417    0.148024    0.0236504  \n",
       "19  -0.0739292   -0.242333   -0.0036499  \n",
       "20   0.0841966   -0.114088    0.0105488  \n",
       "21   0.0619049   -0.181324    -0.184133  \n",
       "22  -0.0157862 -0.00757588      1.03181  \n",
       "23  -0.0429775     0.20216    -0.470764  \n",
       "24   0.0993717   0.0693306    0.0581154  \n",
       "25   0.0432496    0.845723   -0.0382679  \n",
       "26   0.0906832   -0.117318     0.038592  \n",
       "27    0.197265   -0.209681    0.0151007  \n",
       "28   -0.113722   -0.330786    0.0143501  \n",
       "29   0.0572485    -0.10539   -0.0583307  \n",
       "30   -0.230276   -0.210182    0.0758802  \n",
       "31   -0.127833    0.173616   -0.0156655  \n",
       "32   0.0781136 -0.00591308    0.0106413  \n",
       "33   0.0347204   0.0847371   -0.0368373  \n",
       "34  -0.0326265   0.0357809  -0.00135338  \n",
       "35   0.0538183   0.0655006   -0.0318403  \n",
       "36  -0.0291131   0.0931099   -0.0660335  \n",
       "37   0.0358986   0.0419289   -0.0190353  \n",
       "38  0.00663734    0.173839   -0.0371681  \n",
       "39  0.00301895   0.0686235   -0.0600815  \n",
       "40  -0.0858862     -0.1448   -0.0139271  \n",
       "41   0.0693124   0.0586888  -0.00782324  \n",
       "42   -0.161251   0.0198128   0.00937898  \n",
       "43  0.00472309  0.00261814   0.00908042  \n",
       "44   0.0554018   0.0938936   -0.0424614  \n",
       "45   0.0603375    0.015638   0.00358839  \n",
       "46   0.0961673   0.0462312    0.0531763  \n",
       "47    0.160803   0.0800399    0.0231766  \n",
       "48   0.0941599   0.0981985  -0.00369044  \n",
       "49  -0.0298249   -0.155303    0.0165737  \n",
       "50  -0.0147023 -0.00246284 -0.000465984  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartimax_loading_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor interpretations:\n",
    "- *Factor 1*: Negative Emotions [Depressed, Stressed, Anxious]\n",
    "- *Factor 2*: Age [Age, Parent, Retired]\n",
    "- *Factor 3*: Positive Emotions [Positive Valence, Arousal, General Health]\n",
    "- *Factor 4*: Living Situation [Partner, Child, Alone]\n",
    "- *Factor 5*: Employment Situation [Employed Full Time, Retired, Now Work From Home]\n",
    "- *Factor 6*: City Type [Suburban, Urban, Rural]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot variable importance for Factors 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJklrPkEySJEmSJEmtZ0gmSZIkSZKk1jMkkyRJkiRJUusZkkmSJEmSJKn1DMkkSZIkSZLUeoZkkiRJkiRJar3/B4YTGqdEYCOrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_xlabel('Factor 1', fontsize = 15)\n",
    "ax.set_ylabel('Factor 2', fontsize = 15)\n",
    "\n",
    "ax.scatter(quartimax_loading_DF.loc[:, 'F1'], quartimax_loading_DF.loc[:, 'F2'], s = 100)\n",
    "\n",
    "def label_point(x, y, val, ax):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
    "    for i, point in a.iterrows():\n",
    "        ax.text(point['x']+.02, point['y'], str(point['val']), fontsize=14)\n",
    "\n",
    "label_point(quartimax_loading_DF.F1, quartimax_loading_DF.F2, \n",
    "            quartimax_loading_DF['Demographic Feature'], plt.gca())  \n",
    "\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize how each participant scores on the 6 Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.383295</td>\n",
       "      <td>-0.213301</td>\n",
       "      <td>-0.063168</td>\n",
       "      <td>0.213664</td>\n",
       "      <td>0.956620</td>\n",
       "      <td>1.512462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.529635</td>\n",
       "      <td>-0.712101</td>\n",
       "      <td>-0.019120</td>\n",
       "      <td>0.430004</td>\n",
       "      <td>-0.704978</td>\n",
       "      <td>-0.250727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.120029</td>\n",
       "      <td>-0.885506</td>\n",
       "      <td>0.398221</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>-1.177736</td>\n",
       "      <td>1.540852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F1        F2        F3        F4        F5        F6\n",
       "0 -0.383295 -0.213301 -0.063168  0.213664  0.956620  1.512462\n",
       "1 -0.529635 -0.712101 -0.019120  0.430004 -0.704978 -0.250727\n",
       "2  0.120029 -0.885506  0.398221  0.433721 -1.177736  1.540852"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe of the factor scores for each person\n",
    "factor_list = ['F1','F2','F3','F4','F5','F6']\n",
    "fa_components_DF = pd.DataFrame(data = all_quartimax, columns = factor_list)\n",
    "fa_components_DF.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.120029</td>\n",
       "      <td>-0.885506</td>\n",
       "      <td>0.398221</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>-1.177736</td>\n",
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      "text/plain": [
       "   ID        F1        F2        F3        F4        F5        F6\n",
       "0   2 -0.383295 -0.213301 -0.063168  0.213664  0.956620  1.512462\n",
       "1   3 -0.529635 -0.712101 -0.019120  0.430004 -0.704978 -0.250727\n",
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     "execution_count": 28,
     "metadata": {},
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    }
   ],
   "source": [
    "# merge with ID\n",
    "all_FA = pd.concat([fa_components_DF, dem[['ID']]], axis = 1)\n",
    "all_FA = all_FA.iloc[:, np.r_[6, 0:6]]\n",
    "all_FA.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5113, 240)\n"
     ]
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       "      <th>Music Engagement_Engaging With Music Differently YN</th>\n",
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       "      <th>Music Engagement_Music Types-Other Musicians Composers</th>\n",
       "      <th>Music Engagement_Music Types-Same Musicians Composers</th>\n",
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       "      <th>Music Listening_Formats_Always Available Programs</th>\n",
       "      <th>Music Listening_Formats_Free Livestreams</th>\n",
       "      <th>Music Listening_Formats_Free Streaming Others Playlists</th>\n",
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       "      <th>Music Listening_Functions_Moves Me Emotionally</th>\n",
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       "      <th>Music Listening_Situations_Sexual Activity</th>\n",
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       "      <th>date</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>641288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>listening only</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.529635</td>\n",
       "      <td>-0.712101</td>\n",
       "      <td>-0.019120</td>\n",
       "      <td>0.430004</td>\n",
       "      <td>-0.704978</td>\n",
       "      <td>-0.250727</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</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>5.0</td>\n",
       "      <td>159119.0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>Germany</td>\n",
       "      <td>87.5</td>\n",
       "      <td>75.64</td>\n",
       "      <td>73.15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>503353</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>both</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.120029</td>\n",
       "      <td>-0.885506</td>\n",
       "      <td>0.398221</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>-1.177736</td>\n",
       "      <td>1.540852</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>159119.0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>Germany</td>\n",
       "      <td>87.5</td>\n",
       "      <td>75.64</td>\n",
       "      <td>73.15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>764934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</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>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>listening only</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID        F1        F2        F3        F4        F5        F6  \\\n",
       "0   2 -0.383295 -0.213301 -0.063168  0.213664  0.956620  1.512462   \n",
       "1   3 -0.529635 -0.712101 -0.019120  0.430004 -0.704978 -0.250727   \n",
       "2   4  0.120029 -0.885506  0.398221  0.433721 -1.177736  1.540852   \n",
       "\n",
       "   Activities_Call People  Activities_Cleaning  Activities_Cooking  \\\n",
       "0                     4.0                  4.0                 4.0   \n",
       "1                     4.0                  4.0                 4.0   \n",
       "2                     6.0                  4.0                 4.0   \n",
       "\n",
       "   Activities_Crafting  Activities_Create Online Content  \\\n",
       "0                  4.0                               4.0   \n",
       "1                  4.0                               5.0   \n",
       "2                  4.0                               4.0   \n",
       "\n",
       "   Activities_Drugs or Alcohol  Activities_Exercise Indoors  \\\n",
       "0                          4.0                          4.0   \n",
       "1                          4.0                          4.0   \n",
       "2                          4.0                          4.0   \n",
       "\n",
       "   Activities_Exercise Outdoors  Activities_Fashion  Activities_Gardening  \\\n",
       "0                           4.0                 1.0                   4.0   \n",
       "1                           4.0                 4.0                   4.0   \n",
       "2                           4.0                 4.0                   4.0   \n",
       "\n",
       "   Activities_Learning New Skills  Activities_Making Music  \\\n",
       "0                             4.0                      4.0   \n",
       "1                             5.0                      4.0   \n",
       "2                             4.0                      4.0   \n",
       "\n",
       "   Activities_Meditating  Activities_Music Listening  \\\n",
       "0                    1.0                         4.0   \n",
       "1                    4.0                         5.0   \n",
       "2                    4.0                         4.0   \n",
       "\n",
       "   Activities_Online Dating  Activities_Photography  Activities_Playing Games  \\\n",
       "0                       4.0                     4.0                       4.0   \n",
       "1                       4.0                     3.0                       4.0   \n",
       "2                       4.0                     4.0                       4.0   \n",
       "\n",
       "   Activities_Reading Books  Activities_Searching for Music Info  \\\n",
       "0                       4.0                                  4.0   \n",
       "1                       5.0                                  4.0   \n",
       "2                       4.0                                  4.0   \n",
       "\n",
       "   Activities_Sexual Activity  Activities_Shopping Online  \\\n",
       "0                         4.0                         4.0   \n",
       "1                         4.0                         4.0   \n",
       "2                         4.0                         4.0   \n",
       "\n",
       "   Activities_Social Media  Activities_Thinking About Music  \\\n",
       "0                      4.0                              4.0   \n",
       "1                      4.0                              4.0   \n",
       "2                      4.0                              4.0   \n",
       "\n",
       "   Activities_Virtual Exhibitions  Activities_Visit People in Person  \\\n",
       "0                             4.0                                1.0   \n",
       "1                             5.0                                4.0   \n",
       "2                             4.0                                4.0   \n",
       "\n",
       "   Activities_Volunteering  Activities_Watching Movies  \\\n",
       "0                      2.0                         4.0   \n",
       "1                      4.0                         4.0   \n",
       "2                      4.0                         4.0   \n",
       "\n",
       "   Activities_Watching News  Activities_Writing  Country_Confirmed Cases  \\\n",
       "0                       4.0                 4.0                 159119.0   \n",
       "1                       5.0                 5.0                 159119.0   \n",
       "2                       6.0                 4.0                 159119.0   \n",
       "\n",
       "   Country_Confirmed Deaths  Country_Containment Health Index  Country  \\\n",
       "0                    6288.0                             73.48  Germany   \n",
       "1                    6288.0                             73.48  Germany   \n",
       "2                    6288.0                             73.48  Germany   \n",
       "\n",
       "   Country_Economic Support Index  Country_Government Response Index  \\\n",
       "0                            87.5                              75.64   \n",
       "1                            87.5                              75.64   \n",
       "2                            87.5                              75.64   \n",
       "\n",
       "   Country_Stringency Index  Demographics_COVID_Current Place Home  \\\n",
       "0                     73.15                                    1.0   \n",
       "1                     73.15                                    1.0   \n",
       "2                     73.15                                    1.0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Alone  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Child  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Elderly  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Friend  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-No Response  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Other  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Parent  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Partner  \\\n",
       "0                                            1   \n",
       "1                                            1   \n",
       "2                                            1   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Pet  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-Shared  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "\n",
       "   Demographics_COVID_Living Situation-SibCous  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "\n",
       "   Demographics_COVID_Number Times Leave  \\\n",
       "0                                    3.0   \n",
       "1                                    2.0   \n",
       "2                                    3.0   \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Homeschool Children  \\\n",
       "0                                                  0           \n",
       "1                                                  0           \n",
       "2                                                  0           \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Job At Risk  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Lost Job  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Lower Income  \\\n",
       "0                                                  0    \n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-No Response  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Now Work From Home  \\\n",
       "0                                                  1          \n",
       "1                                                  0          \n",
       "2                                                  0          \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Reduced Hours  \\\n",
       "0                                                  0     \n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Still Go To Work  \\\n",
       "0                                                  0        \n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "\n",
       "   Demographics_COVID_Ways Work Affected-Still Work From Home  \\\n",
       "0                                                  0            \n",
       "1                                                  1            \n",
       "2                                                  1            \n",
       "\n",
       "   Demographics_COVID_Work Changed Binary  Demographics_General_Age  \\\n",
       "0                                       1                      43.0   \n",
       "1                                       0                      51.0   \n",
       "2                                       0                      53.0   \n",
       "\n",
       "   Demographics_General_City Type-No Response  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "2                                           0   \n",
       "\n",
       "   Demographics_General_City Type-Rural  \\\n",
       "0                                     0   \n",
       "1                                     1   \n",
       "2                                     0   \n",
       "\n",
       "   Demographics_General_City Type-Suburban  \\\n",
       "0                                        1   \n",
       "1                                        0   \n",
       "2                                        1   \n",
       "\n",
       "   Demographics_General_City Type-Urban  Demographics_General_Education Years  \\\n",
       "0                                     0                                  45.0   \n",
       "1                                     0                                  17.0   \n",
       "2                                     0                                  14.0   \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Employed Full Time  \\\n",
       "0                                                  1                  \n",
       "1                                                  0                  \n",
       "2                                                  0                  \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Employed Part Time  \\\n",
       "0                                                  0                  \n",
       "1                                                  0                  \n",
       "2                                                  0                  \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Home Maker Caregiver  \\\n",
       "0                                                  0                    \n",
       "1                                                  0                    \n",
       "2                                                  1                    \n",
       "\n",
       "   Demographics_General_Employement Before COVID-No Response  \\\n",
       "0                                                  0           \n",
       "1                                                  0           \n",
       "2                                                  0           \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Prefer Not To Say  \\\n",
       "0                                                  0                 \n",
       "1                                                  0                 \n",
       "2                                                  0                 \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Retired  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Self Employed  \\\n",
       "0                                                  0             \n",
       "1                                                  1             \n",
       "2                                                  0             \n",
       "\n",
       "   Demographics_General_Employement Before COVID-Student  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "\n",
       "   Demographics_General_Gender  Demographics_Health_Anxious  \\\n",
       "0                          2.0                          4.0   \n",
       "1                          1.0                          4.0   \n",
       "2                          1.0                          4.0   \n",
       "\n",
       "   Demographics_Health_Arousal  Demographics_Health_Bored  \\\n",
       "0                          4.0                        4.0   \n",
       "1                          4.0                        4.0   \n",
       "2                          4.0                        4.0   \n",
       "\n",
       "   Demographics_Health_COVID Contact Binary  Demographics_Health_Depressed  \\\n",
       "0                                         0                            4.0   \n",
       "1                                         0                            4.0   \n",
       "2                                         0                            4.0   \n",
       "\n",
       "   Demographics_Health_General Health  \\\n",
       "0                                 4.0   \n",
       "1                                 4.0   \n",
       "2                                 4.0   \n",
       "\n",
       "   Demographics_Health_Infected with COVID-I Am Infected  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "\n",
       "   Demographics_Health_Infected with COVID-I Am Mourning A Loss  \\\n",
       "0                                                  0              \n",
       "1                                                  0              \n",
       "2                                                  0              \n",
       "\n",
       "   Demographics_Health_Infected with COVID-I Have Professional Contact  \\\n",
       "0                                                  0                     \n",
       "1                                                  0                     \n",
       "2                                                  0                     \n",
       "\n",
       "   Demographics_Health_Infected with COVID-I Live With Someone  \\\n",
       "0                                                  0             \n",
       "1                                                  0             \n",
       "2                                                  0             \n",
       "\n",
       "   Demographics_Health_Infected with COVID-No Response  \\\n",
       "0                                                  0     \n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "\n",
       "   Demographics_Health_Infected with COVID-None  \\\n",
       "0                                             1   \n",
       "1                                             1   \n",
       "2                                             1   \n",
       "\n",
       "   Demographics_Health_Infected with COVID-Prefer Not To Say  \\\n",
       "0                                                  0           \n",
       "1                                                  0           \n",
       "2                                                  0           \n",
       "\n",
       "   Demographics_Health_Infected with COVID-Someone I Am Close To  \\\n",
       "0                                                  0               \n",
       "1                                                  0               \n",
       "2                                                  0               \n",
       "\n",
       "   Demographics_Health_Lonely  Demographics_Health_Negative Valence  \\\n",
       "0                         4.0                                   4.0   \n",
       "1                         4.0                                   4.0   \n",
       "2                         4.0                                   5.0   \n",
       "\n",
       "   Demographics_Health_Positive Valence  Demographics_Health_Stressed  \\\n",
       "0                                   4.0                           4.0   \n",
       "1                                   4.0                           4.0   \n",
       "2                                   4.0                           5.0   \n",
       "\n",
       "   Demographics_Music_Making Music-Alone  \\\n",
       "0                                      0   \n",
       "1                                      1   \n",
       "2                                      0   \n",
       "\n",
       "   Demographics_Music_Making Music-Both Alone And Together  \\\n",
       "0                                                  0         \n",
       "1                                                  0         \n",
       "2                                                  0         \n",
       "\n",
       "   Demographics_Music_Making Music-No Response  \\\n",
       "0                                            1   \n",
       "1                                            0   \n",
       "2                                            1   \n",
       "\n",
       "   Demographics_Music_Making Music-Together  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "\n",
       "   Demographics_Music_Music Importance  Demographics_Music_Number of Concerts  \\\n",
       "0                                  4.0                                    3.0   \n",
       "1                                  7.0                                    2.0   \n",
       "2                                  7.0                                    1.0   \n",
       "\n",
       "   Demographics_Music_Ollen  Demographics_Personality_Agreeableness  \\\n",
       "0                       2.0                                     4.0   \n",
       "1                       3.0                                     6.5   \n",
       "2                       2.0                                     3.5   \n",
       "\n",
       "   Demographics_Personality_Conscientiousness  \\\n",
       "0                                         4.0   \n",
       "1                                         6.5   \n",
       "2                                         4.0   \n",
       "\n",
       "   Demographics_Personality_Extraversion  \\\n",
       "0                                    4.0   \n",
       "1                                    3.0   \n",
       "2                                    3.0   \n",
       "\n",
       "   Demographics_Personality_Neuroticism  Demographics_Personality_Openness  \\\n",
       "0                                   4.0                                4.0   \n",
       "1                                   1.0                                5.0   \n",
       "2                                   5.0                                4.0   \n",
       "\n",
       "   ESM_ID  Making Music_Functions_Creates a Personal Space  \\\n",
       "0  641288                                              NaN   \n",
       "1  503353                                              4.0   \n",
       "2  764934                                              NaN   \n",
       "\n",
       "   Making Music_Functions_Enables Me to Vent Negative Emotions  \\\n",
       "0                                                NaN             \n",
       "1                                                4.0             \n",
       "2                                                NaN             \n",
       "\n",
       "   Making Music_Functions_Energizes Me  \\\n",
       "0                                  NaN   \n",
       "1                                  7.0   \n",
       "2                                  NaN   \n",
       "\n",
       "   Making Music_Functions_Feel Connected to My Culture  \\\n",
       "0                                                NaN     \n",
       "1                                                4.0     \n",
       "2                                                NaN     \n",
       "\n",
       "   Making Music_Functions_Feel Less Lonely  \\\n",
       "0                                      NaN   \n",
       "1                                      4.0   \n",
       "2                                      NaN   \n",
       "\n",
       "   Making Music_Functions_Feel Like I Am Sharing My Experience  \\\n",
       "0                                                NaN             \n",
       "1                                                4.0             \n",
       "2                                                NaN             \n",
       "\n",
       "   Making Music_Functions_Feel Like I Have Company  \\\n",
       "0                                              NaN   \n",
       "1                                              4.0   \n",
       "2                                              NaN   \n",
       "\n",
       "   Making Music_Functions_Feel Like I Understand Others  \\\n",
       "0                                                NaN      \n",
       "1                                                4.0      \n",
       "2                                                NaN      \n",
       "\n",
       "   Making Music_Functions_Feel Like Part of a Bigger Group  \\\n",
       "0                                                NaN         \n",
       "1                                                4.0         \n",
       "2                                                NaN         \n",
       "\n",
       "   Making Music_Functions_Gives Me New Perspectives  \\\n",
       "0                                               NaN   \n",
       "1                                               5.0   \n",
       "2                                               NaN   \n",
       "\n",
       "   Making Music_Functions_Gives Me Sense of Control  \\\n",
       "0                                               NaN   \n",
       "1                                               4.0   \n",
       "2                                               NaN   \n",
       "\n",
       "   Making Music_Functions_Gives Meaning to My Life  \\\n",
       "0                                              NaN   \n",
       "1                                              4.0   \n",
       "2                                              NaN   \n",
       "\n",
       "   Making Music_Functions_Helps Distract Me  \\\n",
       "0                                       NaN   \n",
       "1                                       4.0   \n",
       "2                                       NaN   \n",
       "\n",
       "   Making Music_Functions_Helps Keep a Routine  \\\n",
       "0                                          NaN   \n",
       "1                                          4.0   \n",
       "2                                          NaN   \n",
       "\n",
       "   Making Music_Functions_Helps Me Accept My Situation  \\\n",
       "0                                                NaN     \n",
       "1                                                4.0     \n",
       "2                                                NaN     \n",
       "\n",
       "   Making Music_Functions_Helps Me Concentrate  \\\n",
       "0                                          NaN   \n",
       "1                                          4.0   \n",
       "2                                          NaN   \n",
       "\n",
       "   Making Music_Functions_Helps Me Dwell on My Worries  \\\n",
       "0                                                NaN     \n",
       "1                                                4.0     \n",
       "2                                                NaN     \n",
       "\n",
       "   Making Music_Functions_Helps Me Express My Identity  \\\n",
       "0                                                NaN     \n",
       "1                                                4.0     \n",
       "2                                                NaN     \n",
       "\n",
       "   Making Music_Functions_Helps Me Fall Asleep  \\\n",
       "0                                          NaN   \n",
       "1                                          4.0   \n",
       "2                                          NaN   \n",
       "\n",
       "   Making Music_Functions_Helps Me Identify with the Artist  \\\n",
       "0                                                NaN          \n",
       "1                                                4.0          \n",
       "2                                                NaN          \n",
       "\n",
       "   Making Music_Functions_Helps Me Relax  \\\n",
       "0                                    NaN   \n",
       "1                                    7.0   \n",
       "2                                    NaN   \n",
       "\n",
       "   Making Music_Functions_Helps Me Understand My Feelings  \\\n",
       "0                                                NaN        \n",
       "1                                                4.0        \n",
       "2                                                NaN        \n",
       "\n",
       "   Making Music_Functions_Helps Time Pass Quickly  \\\n",
       "0                                             NaN   \n",
       "1                                             4.0   \n",
       "2                                             NaN   \n",
       "\n",
       "   Making Music_Functions_Is Enjoyable  \\\n",
       "0                                  NaN   \n",
       "1                                  7.0   \n",
       "2                                  NaN   \n",
       "\n",
       "   Making Music_Functions_Lets Me Daydream  \\\n",
       "0                                      NaN   \n",
       "1                                      4.0   \n",
       "2                                      NaN   \n",
       "\n",
       "   Making Music_Functions_Makes Me Feel Good About Myself  \\\n",
       "0                                                NaN        \n",
       "1                                                4.0        \n",
       "2                                                NaN        \n",
       "\n",
       "   Making Music_Functions_Makes Me Want to Move  \\\n",
       "0                                           NaN   \n",
       "1                                           4.0   \n",
       "2                                           NaN   \n",
       "\n",
       "   Making Music_Functions_Moves Me Emotionally  \\\n",
       "0                                          NaN   \n",
       "1                                          4.0   \n",
       "2                                          NaN   \n",
       "\n",
       "   Making Music_Functions_Provides Comfort  \\\n",
       "0                                      NaN   \n",
       "1                                      4.0   \n",
       "2                                      NaN   \n",
       "\n",
       "   Making Music_Functions_Provides a Good Background  \\\n",
       "0                                                NaN   \n",
       "1                                                7.0   \n",
       "2                                                NaN   \n",
       "\n",
       "   Making Music_Functions_Provides a Spiritual Experience  \\\n",
       "0                                                NaN        \n",
       "1                                                4.0        \n",
       "2                                                NaN        \n",
       "\n",
       "   Making Music_Functions_Provides an Aesthetic Experience  \\\n",
       "0                                                NaN         \n",
       "1                                                4.0         \n",
       "2                                                NaN         \n",
       "\n",
       "   Making Music_Functions_Puts Me in a Good Mood  \\\n",
       "0                                            NaN   \n",
       "1                                            5.0   \n",
       "2                                            NaN   \n",
       "\n",
       "   Making Music_Functions_Reduces My Stress  \\\n",
       "0                                       NaN   \n",
       "1                                       4.0   \n",
       "2                                       NaN   \n",
       "\n",
       "   Making Music_Functions_Reminiscent of Positive Times  \\\n",
       "0                                                NaN      \n",
       "1                                                4.0      \n",
       "2                                                NaN      \n",
       "\n",
       "   Making Music_Functions_Stimulates Me Intellectually  \\\n",
       "0                                                NaN     \n",
       "1                                                5.0     \n",
       "2                                                NaN     \n",
       "\n",
       "   Making Music_Functions_Supports Me in a Bad Mood  \\\n",
       "0                                               NaN   \n",
       "1                                               4.0   \n",
       "2                                               NaN   \n",
       "\n",
       "   Making Music_Hypotheses_Feel Connected to Others  \\\n",
       "0                                               NaN   \n",
       "1                                               7.0   \n",
       "2                                               NaN   \n",
       "\n",
       "   Making Music_Hypotheses_Helps Me Cope  \\\n",
       "0                                    NaN   \n",
       "1                                    7.0   \n",
       "2                                    NaN   \n",
       "\n",
       "   Making Music_Hypotheses_Serve as Social Interaction  \\\n",
       "0                                                NaN     \n",
       "1                                                4.0     \n",
       "2                                                NaN     \n",
       "\n",
       "   Making Music_Situations_Balconies  Making Music_Situations_Dance Alone  \\\n",
       "0                                NaN                                  NaN   \n",
       "1                                4.0                                  4.0   \n",
       "2                                NaN                                  NaN   \n",
       "\n",
       "   Making Music_Situations_Dance Internet  \\\n",
       "0                                     NaN   \n",
       "1                                     4.0   \n",
       "2                                     NaN   \n",
       "\n",
       "   Making Music_Situations_Dance Together  \\\n",
       "0                                     NaN   \n",
       "1                                     4.0   \n",
       "2                                     NaN   \n",
       "\n",
       "   Making Music_Situations_Make Own Music  \\\n",
       "0                                     NaN   \n",
       "1                                     5.0   \n",
       "2                                     NaN   \n",
       "\n",
       "   Making Music_Situations_Perform For Others  \\\n",
       "0                                         NaN   \n",
       "1                                         4.0   \n",
       "2                                         NaN   \n",
       "\n",
       "   Making Music_Situations_Play Alone  Making Music_Situations_Play Internet  \\\n",
       "0                                 NaN                                    NaN   \n",
       "1                                 5.0                                    1.0   \n",
       "2                                 NaN                                    NaN   \n",
       "\n",
       "   Making Music_Situations_Play Together  \\\n",
       "0                                    NaN   \n",
       "1                                    4.0   \n",
       "2                                    NaN   \n",
       "\n",
       "   Making Music_Situations_Post Recordings  \\\n",
       "0                                      NaN   \n",
       "1                                      4.0   \n",
       "2                                      NaN   \n",
       "\n",
       "   Making Music_Situations_Sing Alone  Making Music_Situations_Sing Internet  \\\n",
       "0                                 NaN                                    NaN   \n",
       "1                                 4.0                                    4.0   \n",
       "2                                 NaN                                    NaN   \n",
       "\n",
       "   Making Music_Situations_Sing Together  Music Engagement_Diversity of Music  \\\n",
       "0                                    NaN                                  4.0   \n",
       "1                                    4.0                                  4.0   \n",
       "2                                    NaN                                  4.0   \n",
       "\n",
       "   Music Engagement_Engaging With Music Differently YN  \\\n",
       "0                                                0.0     \n",
       "1                                                0.0     \n",
       "2                                                0.0     \n",
       "\n",
       "   Music Engagement_Interest in Others Music  \\\n",
       "0                                        4.0   \n",
       "1                                        6.0   \n",
       "2                                        1.0   \n",
       "\n",
       "   Music Engagement_Music Type-No Response  \\\n",
       "0                                        1   \n",
       "1                                        1   \n",
       "2                                        1   \n",
       "\n",
       "   Music Engagement_Music Types-Other Genres  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "\n",
       "   Music Engagement_Music Types-Other Musicians Composers  \\\n",
       "0                                                  0        \n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "\n",
       "   Music Engagement_Music Types-Same Musicians Composers  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "\n",
       "   Music Engagement_Nostalgic Music  \\\n",
       "0                               3.0   \n",
       "1                               6.0   \n",
       "2                               3.0   \n",
       "\n",
       "   Music Listening_Formats_Always Available Programs  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "\n",
       "   Music Listening_Formats_Free Livestreams  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "\n",
       "   Music Listening_Formats_Free Streaming Others Playlists  \\\n",
       "0                                                NaN         \n",
       "1                                                NaN         \n",
       "2                                                NaN         \n",
       "\n",
       "   Music Listening_Formats_Free Streaming Own Playlists  \\\n",
       "0                                                NaN      \n",
       "1                                                NaN      \n",
       "2                                                NaN      \n",
       "\n",
       "   Music Listening_Formats_Limited Time Programs  \\\n",
       "0                                            NaN   \n",
       "1                                            NaN   \n",
       "2                                            NaN   \n",
       "\n",
       "   Music Listening_Formats_Owned Digital Music  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "\n",
       "   Music Listening_Formats_Owned Physical Music  \\\n",
       "0                                           NaN   \n",
       "1                                           NaN   \n",
       "2                                           NaN   \n",
       "\n",
       "   Music Listening_Formats_Paid Livestreams  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "\n",
       "   Music Listening_Formats_Paid Streaming Others Playlists  \\\n",
       "0                                                NaN         \n",
       "1                                                NaN         \n",
       "2                                                NaN         \n",
       "\n",
       "   Music Listening_Formats_Paid Streaming Own Playlists  \\\n",
       "0                                                NaN      \n",
       "1                                                NaN      \n",
       "2                                                NaN      \n",
       "\n",
       "   Music Listening_Formats_Radio  Music Listening_Formats_TV Broadcasts  \\\n",
       "0                            NaN                                    NaN   \n",
       "1                            NaN                                    NaN   \n",
       "2                            NaN                                    NaN   \n",
       "\n",
       "   Music Listening_Functions_Creates a Personal Space  \\\n",
       "0                                                4.0    \n",
       "1                                                4.0    \n",
       "2                                                4.0    \n",
       "\n",
       "   Music Listening_Functions_Enables Me to Vent Negative Emotions  \\\n",
       "0                                                4.0                \n",
       "1                                                4.0                \n",
       "2                                                4.0                \n",
       "\n",
       "   Music Listening_Functions_Energizes Me  \\\n",
       "0                                     4.0   \n",
       "1                                     7.0   \n",
       "2                                     4.0   \n",
       "\n",
       "   Music Listening_Functions_Feel Connected to My Culture  \\\n",
       "0                                                4.0        \n",
       "1                                                4.0        \n",
       "2                                                4.0        \n",
       "\n",
       "   Music Listening_Functions_Feel Less Lonely  \\\n",
       "0                                         4.0   \n",
       "1                                         4.0   \n",
       "2                                         4.0   \n",
       "\n",
       "   Music Listening_Functions_Feel Like I Am Sharing My Experience  \\\n",
       "0                                                4.0                \n",
       "1                                                4.0                \n",
       "2                                                4.0                \n",
       "\n",
       "   Music Listening_Functions_Feel Like I Have Company  \\\n",
       "0                                                4.0    \n",
       "1                                                4.0    \n",
       "2                                                4.0    \n",
       "\n",
       "   Music Listening_Functions_Feel Like I Understand Others  \\\n",
       "0                                                4.0         \n",
       "1                                                4.0         \n",
       "2                                                4.0         \n",
       "\n",
       "   Music Listening_Functions_Feel Like Part of a Bigger Group  \\\n",
       "0                                                4.0            \n",
       "1                                                4.0            \n",
       "2                                                4.0            \n",
       "\n",
       "   Music Listening_Functions_Gives Me New Perspectives  \\\n",
       "0                                                4.0     \n",
       "1                                                4.0     \n",
       "2                                                4.0     \n",
       "\n",
       "   Music Listening_Functions_Gives Me Sense of Control  \\\n",
       "0                                                4.0     \n",
       "1                                                4.0     \n",
       "2                                                4.0     \n",
       "\n",
       "   Music Listening_Functions_Gives Meaning to My Life  \\\n",
       "0                                                4.0    \n",
       "1                                                4.0    \n",
       "2                                                4.0    \n",
       "\n",
       "   Music Listening_Functions_Helps Distract Me  \\\n",
       "0                                          4.0   \n",
       "1                                          4.0   \n",
       "2                                          4.0   \n",
       "\n",
       "   Music Listening_Functions_Helps Keep a Routine  \\\n",
       "0                                             4.0   \n",
       "1                                             4.0   \n",
       "2                                             4.0   \n",
       "\n",
       "   Music Listening_Functions_Helps Me Accept My Situation  \\\n",
       "0                                                4.0        \n",
       "1                                                4.0        \n",
       "2                                                4.0        \n",
       "\n",
       "   Music Listening_Functions_Helps Me Concentrate  \\\n",
       "0                                             4.0   \n",
       "1                                             4.0   \n",
       "2                                             4.0   \n",
       "\n",
       "   Music Listening_Functions_Helps Me Dwell on My Worries  \\\n",
       "0                                                4.0        \n",
       "1                                                4.0        \n",
       "2                                                4.0        \n",
       "\n",
       "   Music Listening_Functions_Helps Me Express My Identity  \\\n",
       "0                                                4.0        \n",
       "1                                                4.0        \n",
       "2                                                4.0        \n",
       "\n",
       "   Music Listening_Functions_Helps Me Fall Asleep  \\\n",
       "0                                             4.0   \n",
       "1                                             4.0   \n",
       "2                                             4.0   \n",
       "\n",
       "   Music Listening_Functions_Helps Me Identify with the Artist  \\\n",
       "0                                                4.0             \n",
       "1                                                4.0             \n",
       "2                                                4.0             \n",
       "\n",
       "   Music Listening_Functions_Helps Me Relax  \\\n",
       "0                                       4.0   \n",
       "1                                       4.0   \n",
       "2                                       4.0   \n",
       "\n",
       "   Music Listening_Functions_Helps Me Understand My Feelings  \\\n",
       "0                                                4.0           \n",
       "1                                                4.0           \n",
       "2                                                4.0           \n",
       "\n",
       "   Music Listening_Functions_Helps Time Pass Quickly  \\\n",
       "0                                                4.0   \n",
       "1                                                4.0   \n",
       "2                                                4.0   \n",
       "\n",
       "   Music Listening_Functions_Is Enjoyable  \\\n",
       "0                                     4.0   \n",
       "1                                     7.0   \n",
       "2                                     4.0   \n",
       "\n",
       "   Music Listening_Functions_Lets Me Daydream  \\\n",
       "0                                         4.0   \n",
       "1                                         4.0   \n",
       "2                                         4.0   \n",
       "\n",
       "   Music Listening_Functions_Makes Me Feel Good About Myself  \\\n",
       "0                                                4.0           \n",
       "1                                                4.0           \n",
       "2                                                4.0           \n",
       "\n",
       "   Music Listening_Functions_Makes Me Want to Move  \\\n",
       "0                                              4.0   \n",
       "1                                              4.0   \n",
       "2                                              4.0   \n",
       "\n",
       "   Music Listening_Functions_Moves Me Emotionally  \\\n",
       "0                                             4.0   \n",
       "1                                             4.0   \n",
       "2                                             4.0   \n",
       "\n",
       "   Music Listening_Functions_Provides Comfort  \\\n",
       "0                                         4.0   \n",
       "1                                         4.0   \n",
       "2                                         4.0   \n",
       "\n",
       "   Music Listening_Functions_Provides a Good Background  \\\n",
       "0                                                4.0      \n",
       "1                                                4.0      \n",
       "2                                                4.0      \n",
       "\n",
       "   Music Listening_Functions_Provides a Spiritual Experience  \\\n",
       "0                                                4.0           \n",
       "1                                                4.0           \n",
       "2                                                4.0           \n",
       "\n",
       "   Music Listening_Functions_Provides an Aesthetic Experience  \\\n",
       "0                                                4.0            \n",
       "1                                                4.0            \n",
       "2                                                4.0            \n",
       "\n",
       "   Music Listening_Functions_Puts Me in a Good Mood  \\\n",
       "0                                               4.0   \n",
       "1                                               5.0   \n",
       "2                                               4.0   \n",
       "\n",
       "   Music Listening_Functions_Reduces My Stress  \\\n",
       "0                                          4.0   \n",
       "1                                          4.0   \n",
       "2                                          4.0   \n",
       "\n",
       "   Music Listening_Functions_Reminiscent of Positive Times  \\\n",
       "0                                                4.0         \n",
       "1                                                4.0         \n",
       "2                                                4.0         \n",
       "\n",
       "   Music Listening_Functions_Stimulates Me Intellectually  \\\n",
       "0                                                4.0        \n",
       "1                                                7.0        \n",
       "2                                                4.0        \n",
       "\n",
       "   Music Listening_Functions_Supports Me in a Bad Mood  \\\n",
       "0                                                4.0     \n",
       "1                                                4.0     \n",
       "2                                                4.0     \n",
       "\n",
       "   Music Listening_Hypotheses_Feel Connected to Others  \\\n",
       "0                                                4.0     \n",
       "1                                                5.0     \n",
       "2                                                5.0     \n",
       "\n",
       "   Music Listening_Hypotheses_Helps Me Cope  \\\n",
       "0                                       4.0   \n",
       "1                                       4.0   \n",
       "2                                       4.0   \n",
       "\n",
       "   Music Listening_Hypotheses_Serve as Social Interaction  \\\n",
       "0                                                4.0        \n",
       "1                                                1.0        \n",
       "2                                                2.0        \n",
       "\n",
       "   Music Listening_Situations_Alone  Music Listening_Situations_Dining  \\\n",
       "0                               4.0                                4.0   \n",
       "1                               4.0                                4.0   \n",
       "2                               4.0                                4.0   \n",
       "\n",
       "   Music Listening_Situations_Doing Nothing Else  \\\n",
       "0                                            4.0   \n",
       "1                                            4.0   \n",
       "2                                            4.0   \n",
       "\n",
       "   Music Listening_Situations_Evening  Music Listening_Situations_Exercising  \\\n",
       "0                                 4.0                                    4.0   \n",
       "1                                 4.0                                    4.0   \n",
       "2                                 4.0                                    4.0   \n",
       "\n",
       "   Music Listening_Situations_Housework  Music Listening_Situations_Morning  \\\n",
       "0                                   4.0                                 4.0   \n",
       "1                                   5.0                                 4.0   \n",
       "2                                   4.0                                 4.0   \n",
       "\n",
       "   Music Listening_Situations_Other Activities  \\\n",
       "0                                          4.0   \n",
       "1                                          5.0   \n",
       "2                                          4.0   \n",
       "\n",
       "   Music Listening_Situations_Sexual Activity  \\\n",
       "0                                         4.0   \n",
       "1                                         4.0   \n",
       "2                                         4.0   \n",
       "\n",
       "   Music Listening_Situations_Traveling  \\\n",
       "0                                   4.0   \n",
       "1                                   4.0   \n",
       "2                                   4.0   \n",
       "\n",
       "   Music Listening_Situations_With Others  Music Listening_Situations_Working  \\\n",
       "0                                     4.0                                 4.0   \n",
       "1                                     4.0                                 4.0   \n",
       "2                                     4.0                                 4.0   \n",
       "\n",
       "         date Survey Info_Group  Demographics_General_Gender  \n",
       "0  2020-04-30    listening only                          1.0  \n",
       "1  2020-04-30              both                          0.0  \n",
       "2  2020-04-30    listening only                          0.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge ID/Factor scores with all other variables from the original dataset\n",
    "factors_all_data = all_FA.merge(original_df, on='ID', how='outer', suffixes=('', ''))\n",
    "print(factors_all_data.shape)\n",
    "factors_all_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write out dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "factors_all_data.to_csv(r'factors_all_data.csv', index = None, header = True)"
   ]
  }
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