aggregate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sentiment_analysis as sa\n",
    "import importlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "importlib.reload(sa)\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Trait analysis__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Introversion vs Extraversion__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "extroverts = sa.get_trait_types(\"e\")\n",
    "introverts = sa.get_trait_types(\"i\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Sentiment__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "extroverts_sentiment_max = sa.get_types_sentiment_max(extroverts)\n",
    "introverts_sentiment_max = sa.get_types_sentiment_max(introverts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extroverts_introverts_sentiment = pd.DataFrame(index=extroverts_sentiment_max.keys())\n",
    "extroverts_introverts_sentiment[\"Extroverts\"] = extroverts_sentiment_max.values()\n",
    "extroverts_introverts_sentiment[\"Introverts\"] = introverts_sentiment_max.values()\n",
    "\n",
    "extroverts_introverts_sentiment.plot.bar(color=[\"orange\", \"purple\"])\n",
    "plt.legend(loc='center')\n",
    "plt.ylabel(\"percentage value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Emotions__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "extroverts_emotion_avg = sa.get_types_avg_emotion(extroverts)\n",
    "introverts_emotion_avg = sa.get_types_avg_emotion(introverts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extroverts_introverts_emotion = pd.DataFrame(index=extroverts_emotion_avg.keys())\n",
    "extroverts_introverts_emotion[\"Extroverts\"] = extroverts_emotion_avg.values()\n",
    "extroverts_introverts_emotion[\"Introverts\"] = introverts_emotion_avg.values()\n",
    "\n",
    "extroverts_introverts_emotion.plot.bar(color=[\"orange\", \"purple\"])\n",
    "plt.ylabel(\"average value\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Sensing vs Intuition__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "intuitives = sa.get_trait_types(\"n\")\n",
    "sensors = sa.get_trait_types(\"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Sentiment__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "intuitives_sentiment_max = sa.get_types_sentiment_max(intuitives)\n",
    "sensors_sentiment_max = sa.get_types_sentiment_max(sensors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intuitives_sensors_sentiment = pd.DataFrame(index=intuitives_sentiment_max.keys())\n",
    "intuitives_sensors_sentiment[\"iNtuitives\"] = intuitives_sentiment_max.values()\n",
    "intuitives_sensors_sentiment[\"Sensors\"] = sensors_sentiment_max.values()\n",
    "\n",
    "intuitives_sensors_sentiment.plot.bar(color=[\"blue\", \"green\"])\n",
    "plt.legend(loc='center')\n",
    "plt.ylabel(\"percentage value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Emotion__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "intuitives_emotion_avg = sa.get_types_avg_emotion(intuitives)\n",
    "sensors_emotion_avg = sa.get_types_avg_emotion(sensors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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99+jEiRNatmyZevTocdVpqWvZuHGjUlNT1aVLF/n5+Wnjxo06deqUmjRpIkkaNWqUEhMT1aBBA4WHh+uTTz7R9u3b9emnn95w3OHDh6tr16665557dO7cOX3//feWMcsDAhAAAOVU1apVFRkZqb/+9a86cOCALl68qMDAQA0aNEhvvvmmTCaTli9frrfeekuxsbE6deqUAgIC9NBDD8nf3/+mfoa3t7fWrFmjGTNmKC8vT/Xr19e0adPUtWtXSZfX8uTm5urVV19Vdna2wsLC9NVXX6lhw4Y3HLekpEQvvfSSjh07Jm9vb8XExOivf/3rbR8TezGZr1znBou8vDz5+PgoNzdX3t7eji4H/8NUFjduHWv/QW/ljq8A7O/ChQs6dOiQgoOD5eHh4ehycJtu9Ptpy/c3a4AAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIbARc8Vg71+HwlAAIAK7cpjIAoLCx1cCezhyu/j/z7ew1bcCBEAUKG5urqqevXqlod/enl5yVQmNxRDWTKbzSosLFR2draqV68uV1fX2xqPAAQAqPACAgIkyRKC4LyqV69u+f28HQQgAECFZzKZVKdOHfn5+enixYuOLge3qHLlyrc983MFAQgAYBiurq52+wKFc2MRNAAAMBxmgADYlWmcfReX8lBZAGWBGSAAAGA4BCAAAGA4BCAAAGA4BCAAAGA45SIAzZo1S0FBQfLw8FBkZKQ2bdp03b5ffPGFIiIiVL16dVWpUkXh4eFauHChVR+z2ayEhATVqVNHnp6eioqK0r59+8p6NwAAgJNweABavHix4uLilJiYqK1bt6pFixaKjo6+7t06a9asqbfeekvp6enauXOnYmNjFRsbqxUrVlj6TJkyRTNnzlRycrI2btyoKlWqKDo6WhcuXLhTuwUAAMoxk9nBj8eNjIzUfffdp/fff1+SVFpaqsDAQL3yyit64403bmqMVq1a6bHHHtOECRNkNptVt25dvfrqqxo5cqQkKTc3V/7+/po3b5769Onzh+Pl5eXJx8dHubm58vb2vvWdQ5kok0f4jLX/oEa9fJvL4AE4ii3f3w6dASouLtaWLVsUFRVlaXNxcVFUVJTS09P/8PNms1mpqanau3evHnroIUnSoUOHlJmZaTWmj4+PIiMjrztmUVGR8vLyrDYAAFBxOTQAnT59WiUlJfL397dq9/f3V2Zm5nU/l5ubq6pVq8rNzU2PPfaY/va3v+mRRx6RJMvnbBkzKSlJPj4+li0wMPB2dgsAAJRzDl8DdCuqVaum7du368cff9Tbb7+tuLg4paWl3fJ48fHxys3NtWxHjx61X7EAAKDcceijMHx9feXq6qqsrCyr9qysrBs+6t7FxUWhoaGSpPDwcO3Zs0dJSUl6+OGHLZ/LyspSnTp1rMYMDw+/5nju7u5yd3e/zb0BAADOwqEzQG5ubmrdurVSU1MtbaWlpUpNTVXbtm1vepzS0lIVFRVJkoKDgxUQEGA1Zl5enjZu3GjTmAAAoOJy+MNQ4+LiNHDgQEVERKhNmzaaMWOGCgoKFBsbK0kaMGCA6tWrp6SkJEmX1+tERESoQYMGKioq0vLly7Vw4ULNnj1bkmQymTR8+HBNnDhRDRs2VHBwsMaMGaO6deuqe/fujtpNAABQjjg8APXu3VunTp1SQkKCMjMzFR4erpSUFMsi5oyMDLm4/D5RVVBQoBdffFHHjh2Tp6enGjdurL///e/q3bu3pc9rr72mgoICDR48WDk5OWrXrp1SUlLk4eFxx/cPAACUPw6/D1B5xH2AyjfuA1S+cR8gAI7iNPcBAgAAcAQCEAAAMBwCEAAAMBwCEGBgJpP9NwBwBgQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOOUiAM2aNUtBQUHy8PBQZGSkNm3adN2+c+bMUfv27VWjRg3VqFFDUVFRV/V/9tlnZTKZrLaYmJiy3g0AAOAkHB6AFi9erLi4OCUmJmrr1q1q0aKFoqOjlZ2dfc3+aWlp6tu3r77//nulp6crMDBQXbp00fHjx636xcTE6OTJk5bt888/vxO7AwAAnIDDA9D06dM1aNAgxcbGKiwsTMnJyfLy8tLcuXOv2f/TTz/Viy++qPDwcDVu3FgfffSRSktLlZqaatXP3d1dAQEBlq1GjRp3YncAAIATcGgAKi4u1pYtWxQVFWVpc3FxUVRUlNLT029qjMLCQl28eFE1a9a0ak9LS5Ofn58aNWqkIUOG6MyZM9cdo6ioSHl5eVYbAACouBwagE6fPq2SkhL5+/tbtfv7+yszM/Omxnj99ddVt25dqxAVExOjBQsWKDU1VZMnT9bq1avVtWtXlZSUXHOMpKQk+fj4WLbAwMBb3ykAAFDuVXJ0Abdj0qRJWrRokdLS0uTh4WFp79Onj+XXzZo1U/PmzdWgQQOlpaWpc+fOV40THx+vuLg4y+u8vDxCEAAAFZhDZ4B8fX3l6uqqrKwsq/asrCwFBATc8LNTp07VpEmTtHLlSjVv3vyGfUNCQuTr66v9+/df8313d3d5e3tbbQAAoOJyaAByc3NT69atrRYwX1nQ3LZt2+t+bsqUKZowYYJSUlIUERHxhz/n2LFjOnPmjOrUqWOXugEAgHNz+FVgcXFxmjNnjubPn689e/ZoyJAhKigoUGxsrCRpwIABio+Pt/SfPHmyxowZo7lz5yooKEiZmZnKzMxUfn6+JCk/P1+jRo3Shg0bdPjwYaWmpqpbt24KDQ1VdHS0Q/YRAACULw5fA9S7d2+dOnVKCQkJyszMVHh4uFJSUiwLozMyMuTi8ntOmz17toqLi/Xkk09ajZOYmKixY8fK1dVVO3fu1Pz585WTk6O6deuqS5cumjBhgtzd3e/ovgEAgPLJZDabzY4uorzJy8uTj4+PcnNzWQ9UDplMZTDoWPsPak4s/3+1nOFYOsNxBFA+2PL97fBTYAAAAHcaAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABjOLQWgH374Qf3791fbtm11/PhxSdLChQu1du1auxYHAABQFmwOQP/6178UHR0tT09Pbdu2TUVFRZKk3NxcvfPOO3YvEAAAwN5sDkATJ05UcnKy5syZo8qVK1vaH3zwQW3dutWuxQEAAJQFmwPQ3r179dBDD13V7uPjo5ycHHvUBAAAUKZsDkABAQHav3//Ve1r165VSEiIXYoCAAAoSzYHoEGDBmnYsGHauHGjTCaTTpw4oU8//VQjR47UkCFDyqJGAAAAu6pk6wfeeOMNlZaWqnPnziosLNRDDz0kd3d3jRw5Uq+88kpZ1AgAAGBXNgcgk8mkt956S6NGjdL+/fuVn5+vsLAwVa1atSzqAwAAsDubA9AVbm5uCgsLs2ctAAAAd4TNAahjx44ymUzXff+77767rYIAAADKms0BKDw83Or1xYsXtX37du3atUsDBw60V10AAABlxuYA9Ne//vWa7WPHjlV+fv5tFwQAAFDW7PYw1P79+2vu3Ln2Gg4AAKDM2C0Apaeny8PDw17DAQAAlBmbT4H17NnT6rXZbNbJkye1efNmjRkzxm6FAQAAlBWbA5CPj4/VaxcXFzVq1Ejjx49Xly5d7FYYAABAWbE5AH3yySdlUQcAAMAdY7c1QAAAAM7ipmaAatSoccObH/63s2fP3lZBAAAAZe2mAtCMGTPKuAwAAIA756YCEHd4BgAAFcktPwxVki5cuKDi4mKrNm9v79sqCAAAoKzZvAi6oKBAL7/8svz8/FSlShXVqFHDagMAACjvbA5Ar732mr777jvNnj1b7u7u+uijjzRu3DjVrVtXCxYsKIsaAQAA7MrmU2D/+c9/tGDBAj388MOKjY1V+/btFRoaqvr16+vTTz9Vv379yqJOAAAAu7F5Bujs2bMKCQmRdHm9z5XL3tu1a6c1a9bYtzoAAIAyYHMACgkJ0aFDhyRJjRs31j/+8Q9Jl2eGqlevfktFzJo1S0FBQfLw8FBkZKQ2bdp03b5z5sxR+/btLWuOoqKirupvNpuVkJCgOnXqyNPTU1FRUdq3b98t1QYAACoemwNQbGysduzYIUl64403NGvWLHl4eGjEiBEaNWqUzQUsXrxYcXFxSkxM1NatW9WiRQtFR0crOzv7mv3T0tLUt29fff/990pPT1dgYKC6dOmi48ePW/pMmTJFM2fOVHJysjZu3KgqVaooOjpaFy5csLk+AABQ8ZjMZrP5dgY4cuSItmzZotDQUDVv3tzmz0dGRuq+++7T+++/L0kqLS1VYGCgXnnlFb3xxht/+PmSkhLVqFFD77//vgYMGCCz2ay6devq1Vdf1ciRIyVJubm58vf317x589SnT58/HDMvL08+Pj7Kzc3lsv5y6CZvSm6bsfYf1Jx4W3+17ghnOJbOcBwBlA+2fH/bPAN09OhRq9f169dXz549byn8FBcXa8uWLYqKivq9IBcXRUVFKT09/abGKCws1MWLF1WzZk1J0qFDh5SZmWk1po+PjyIjI687ZlFRkfLy8qw2AABQcdkcgIKCgtShQwfNmTNH586du60ffvr0aZWUlMjf39+q3d/fX5mZmTc1xuuvv666detaAs+Vz9kyZlJSknx8fCxbYGCgrbsCAACciM0BaPPmzWrTpo3Gjx+vOnXqqHv37lqyZImKiorKor4bmjRpkhYtWqQvv/xSHh4etzxOfHy8cnNzLdv/znIBAICKxeYA1LJlS7377rvKyMjQN998o9q1a2vw4MHy9/fXc889Z9NYvr6+cnV1VVZWllV7VlaWAgICbvjZqVOnatKkSVq5cqXV6bcrn7NlTHd3d3l7e1ttAACg4rI5AF1hMpnUsWNHzZkzR99++62Cg4M1f/58m8Zwc3NT69atlZqaamkrLS1Vamqq2rZte93PTZkyRRMmTFBKSooiIiKs3gsODlZAQIDVmHl5edq4ceMNxwQAAMZxywHo2LFjmjJlisLDw9WmTRtVrVpVs2bNsnmcuLg4zZkzR/Pnz9eePXs0ZMgQFRQUKDY2VpI0YMAAxcfHW/pPnjxZY8aM0dy5cxUUFKTMzExlZmYqPz9f0uVgNnz4cE2cOFFfffWVfvrpJw0YMEB169ZV9+7db3V3AQBABWLzozA++OADffbZZ1q3bp0aN26sfv366d///rfq169/SwX07t1bp06dUkJCgjIzMxUeHq6UlBTLIuaMjAy5uPye02bPnq3i4mI9+eSTVuMkJiZq7Nixki4/r6ygoECDBw9WTk6O2rVrp5SUlNtaJwQAACoOm+8DFBgYqL59+6pfv35q0aJFWdXlUNwHqHxzhnvXSM5x/xpnOJbOcBwBlA+2fH/bPAOUkZEhU5n8XxMAAODOsHkNEOEHAAA4O5tngAAAV7P3vw1v7yFFAP7ILV8FBgAA4KwIQAAAwHBuKQBdunRJ3377rT744AOdP39eknTixAnLvXgAAADKM5vXAB05ckQxMTHKyMhQUVGRHnnkEVWrVk2TJ09WUVGRkpOTy6JOAAAAu7F5BmjYsGGKiIjQuXPn5OnpaWnv0aOH1eMnAAAAyiubZ4B++OEHrV+/Xm5ublbtQUFBOn78uN0KAwAAKCs2zwCVlpaqpKTkqvZjx46pWrVqdikKAACgLNkcgLp06aIZM2ZYXptMJuXn5ysxMVGPPvqoPWsDAAAoEzafAps2bZqio6MVFhamCxcu6JlnntG+ffvk6+urzz//vCxqBAAAsCubA9Bdd92lHTt2aNGiRdq5c6fy8/P1/PPPq1+/flaLogEAAMqrW3oURqVKldS/f3971wIAAHBH2ByAvvrqq2u2m0wmeXh4KDQ0VMHBwbddGAAAQFmxOQB1795dJpNJ5v95Ut+VNpPJpHbt2mnp0qWqUaOG3QoFAACwF5uvAlu1apXuu+8+rVq1Srm5ucrNzdWqVasUGRmpr7/+WmvWrNGZM2c0cuTIsqgXAADgttk8AzRs2DB9+OGHeuCBByxtnTt3loeHhwYPHqyff/5ZM2bM0HPPPWfXQgEAAOzF5hmgAwcOyNvb+6p2b29vHTx4UJLUsGFDnT59+varAwAAKAM2B6DWrVtr1KhROnXqlKXt1KlTeu2113TfffdJkvbt26fAwED7VQkAAGBHNp8C+/jjj9WtWzfdddddlpBz9OhRhYSE6N///rckKT8/X6NHj7ZvpQAAAHZicwBq1KiRdu/erZUrV+rXX3+1tD3yyCNycbk8odS9e3e7FgkAAGBPt3QjRBcXF8XExCgmJsbe9QAAAJS5WwpABQUFWr16tTIyMlRcXGz13tChQ+1SGAAAQFmxOQBt27ZNjz76qAoLC1VQUKCaNWvq9OnT8vLykp+fHwEIAACUezZfBTZixAg9/vjjOnfunDw9PbVhwwYdOXJErVu31tSpU8uiRgAAALuyOQBt375dr776qlxcXOTq6qqioiIFBgZqypQpevPNN8uiRgAAALuyOQBVrlzZcrWXn5+fMjIyJEk+Pj46evSofasDAAAoAzavAWrZsqV+/PFHNWzYUB06dFBCQoJOnz6thQsXqmnTpmVRIwAAgF3ZPAP0zjvvqE6dOpKkt99+WzVq1NCQIUN06tQpffjhh3YvEAAAwN5smgEym83y8/OzzPT4+fkpJSWlTAoDAAAoKzbNAJnNZoWGhrLWBwAAODWbApCLi4saNmyoM2fOlFU9AAAAZc7mNUCTJk3SqFGjtGvXrrKoBwAAoMzZfBXYgAEDVFhYqBYtWsjNzU2enp5W7589e9ZuxQEAAJQFmwPQjBkzyqAMAACAO8fmADRw4MCyqAMAAOCOsXkNkCQdOHBAo0ePVt++fZWdnS1J+uabb/Tzzz/btTgAAICyYPMM0OrVq9W1a1c9+OCDWrNmjd5++235+flpx44d+vjjj7VkyZKyqBMADMU0zmT3Mc2JZruPCTgrm2eA3njjDU2cOFGrVq2Sm5ubpb1Tp07asGGDzQXMmjVLQUFB8vDwUGRkpDZt2nTdvj///LN69eqloKAgmUyma65HGjt2rEwmk9XWuHFjm+sCAAAVl80B6KefflKPHj2uavfz89Pp06dtGmvx4sWKi4tTYmKitm7dqhYtWig6OtpyWu1/FRYWKiQkRJMmTVJAQMB1x7333nt18uRJy7Z27Vqb6gIAABWbzQGoevXqOnny5FXt27ZtU7169Wwaa/r06Ro0aJBiY2MVFham5ORkeXl5ae7cudfsf9999+ndd99Vnz595O7uft1xK1WqpICAAMvm6+trU10AAKBiszkA9enTR6+//royMzNlMplUWlqqdevWaeTIkRowYMBNj1NcXKwtW7YoKirq92JcXBQVFaX09HRby7Kyb98+1a1bVyEhIerXr58yMjJu2L+oqEh5eXlWGwAAqLhu6WnwjRs3VmBgoPLz8xUWFqaHHnpIDzzwgEaPHn3T45w+fVolJSXy9/e3avf391dmZqatZVlERkZq3rx5SklJ0ezZs3Xo0CG1b99e58+fv+5nkpKS5OPjY9kCAwNv+ecDAIDyz+arwNzc3DRnzhyNGTNGu3btUn5+vlq2bKmGDRuWRX0269q1q+XXzZs3V2RkpOrXr69//OMfev7556/5mfj4eMXFxVle5+XlEYIAAKjAbA5Aa9euVbt27XT33Xfr7rvvvuUf7OvrK1dXV2VlZVm1Z2Vl3XCBs62qV6+ue+65R/v3779uH3d39xuuKQIAABWLzafAOnXqpODgYL355pvavXv3Lf9gNzc3tW7dWqmpqZa20tJSpaamqm3btrc87v/Kz8/XgQMHVKdOHbuNCQAAnJvNAejEiRN69dVXtXr1ajVt2lTh4eF69913dezYMZt/eFxcnObMmaP58+drz549GjJkiAoKChQbGyvp8oNX4+PjLf2Li4u1fft2bd++XcXFxTp+/Li2b99uNbszcuRIrV69WocPH9b69evVo0cPubq6qm/fvjbXBwAAKiabA5Cvr69efvllrVu3TgcOHNBTTz2l+fPnKygoSJ06dbJprN69e2vq1KlKSEhQeHi4tm/frpSUFMvC6IyMDKtL7k+cOKGWLVuqZcuWOnnypKZOnaqWLVvqhRdesPQ5duyY+vbtq0aNGunpp59WrVq1tGHDBtWuXdvWXQUAABWUyWw239a90UtKSvTNN99ozJgx2rlzp0pKSuxVm8Pk5eXJx8dHubm58vb2dnQ5+B8m+z8hQBprzMcOOMOxdIbjKJXBsTTon0ngdtjy/X1LD0OVpHXr1unFF19UnTp19Mwzz6hp06ZatmzZrQ4HAABwx9h8FVh8fLwWLVqkEydO6JFHHtF7772nbt26ycvLqyzqAwAAsDubA9CaNWs0atQoPf300zxiAgAAOCWbA9C6devKog4AAIA7xuYAdMXu3buVkZGh4uJiq/YnnnjitosCAAAoSzYHoIMHD6pHjx766aefZDKZdOUiMtP/fwlERbgKDAAAVGw2XwU2bNgwBQcHKzs7W15eXvr555+1Zs0aRUREKC0trQxKBAAAsC+bZ4DS09P13XffydfXVy4uLnJxcVG7du2UlJSkoUOHatu2bWVRJwAAgN3YPANUUlKiatWqSbp8V+gTJ05IkurXr6+9e/fatzoAAIAyYPMMUNOmTbVjxw4FBwcrMjJSU6ZMkZubmz788EOFhISURY0AAAB2ZXMAGj16tAoKCiRJ48eP15/+9Ce1b99etWrV0uLFi+1eIAAAgL3ZHICio6Mtvw4NDdUvv/yis2fPqkaNGpYrwQAAAMqzW74P0H+rWbOmPYYBAAC4I275YagAAADOigAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMhwAEAAAMp5KjCwAAAPZnMtl/TLPZ/mM6CjNAAADAcJgBAgAAN8U0zr7TSuZEx00pMQMEAAAMx+EBaNasWQoKCpKHh4ciIyO1adOm6/b9+eef1atXLwUFBclkMmnGjBm3PSYAADAehwagxYsXKy4uTomJidq6datatGih6OhoZWdnX7N/YWGhQkJCNGnSJAUEBNhlTAAAYDwODUDTp0/XoEGDFBsbq7CwMCUnJ8vLy0tz5869Zv/77rtP7777rvr06SN3d3e7jAkAAIzHYQGouLhYW7ZsUVRU1O/FuLgoKipK6enpd3TMoqIi5eXlWW0AAKDiclgAOn36tEpKSuTv72/V7u/vr8zMzDs6ZlJSknx8fCxbYGDgLf18AADgHBy+CLo8iI+PV25urmU7evSoo0sCAABlyGH3AfL19ZWrq6uysrKs2rOysq67wLmsxnR3d7/umiIAAFDxOGwGyM3NTa1bt1ZqaqqlrbS0VKmpqWrbtm25GRMAAFQ8Dr0TdFxcnAYOHKiIiAi1adNGM2bMUEFBgWJjYyVJAwYMUL169ZSUlCTp8iLn3bt3W359/Phxbd++XVWrVlVoaOhNjQkAAODQANS7d2+dOnVKCQkJyszMVHh4uFJSUiyLmDMyMuTi8vsk1YkTJ9SyZUvL66lTp2rq1Knq0KGD0tLSbmpMAAAAk9lckZ7tah95eXny8fFRbm6uvL29HV0O/kdZPOFYY+0/qCOfcXOznOFYOsNxlMrgWBr0zyTsx4h/v235/uYqMAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDjlIgDNmjVLQUFB8vDwUGRkpDZt2nTD/v/85z/VuHFjeXh4qFmzZlq+fLnV+88++6xMJpPVFhMTU5a7AAAAnIjDA9DixYsVFxenxMREbd26VS1atFB0dLSys7Ov2X/9+vXq27evnn/+eW3btk3du3dX9+7dtWvXLqt+MTExOnnypGX7/PPP78TuAAAAJ+DwADR9+nQNGjRIsbGxCgsLU3Jysry8vDR37txr9n/vvfcUExOjUaNGqUmTJpowYYJatWql999/36qfu7u7AgICLFuNGjXuxO4AAAAn4NAAVFxcrC1btigqKsrS5uLioqioKKWnp1/zM+np6Vb9JSk6Ovqq/mlpafLz81OjRo00ZMgQnTlz5rp1FBUVKS8vz2oDAAAVl0MD0OnTp1VSUiJ/f3+rdn9/f2VmZl7zM5mZmX/YPyYmRgsWLFBqaqomT56s1atXq2vXriopKbnmmElJSfLx8bFsgYGBt7lnAACgPKvk6ALKQp8+fSy/btasmZo3b64GDRooLS1NnTt3vqp/fHy84uLiLK/z8vIIQQAAVGAOnQHy9fWVq6ursrKyrNqzsrIUEBBwzc8EBATY1F+SQkJC5Ovrq/3791/zfXd3d3l7e1ttAACg4nJoAHJzc1Pr1q2VmppqaSstLVVqaqratm17zc+0bdvWqr8krVq16rr9JenYsWM6c+aM6tSpY5/CAQCAU3P4VWBxcXGaM2eO5s+frz179mjIkCEqKChQbGysJGnAgAGKj4+39B82bJhSUlI0bdo0/fLLLxo7dqw2b96sl19+WZKUn5+vUaNGacOGDTp8+LBSU1PVrVs3hYaGKjo62iH7CAAAyheHrwHq3bu3Tp06pYSEBGVmZio8PFwpKSmWhc4ZGRlycfk9pz3wwAP67LPPNHr0aL355ptq2LChli5dqqZNm0qSXF1dtXPnTs2fP185OTmqW7euunTpogkTJsjd3d0h+wgAAMoXk9lsNju6iPImLy9PPj4+ys3NZT1QOWQylcGgY+0/qDmx/P/VcoZj6QzHUSqDY2nQP5OwHyP+/bbl+9vhM0C4daZx/A8SAIBb4fA1QAAAAHcaM0AAgAqN2XJcCzNAAADAcAhAAIByw2Sy/wZcCwEIAAAYDgEIAAAYDgHoDmJaFwCA8oEABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADKdcBKBZs2YpKChIHh4eioyM1KZNm27Y/5///KcaN24sDw8PNWvWTMuXL7d632w2KyEhQXXq1JGnp6eioqK0b9++stwFAADgRBwegBYvXqy4uDglJiZq69atatGihaKjo5WdnX3N/uvXr1ffvn31/PPPa9u2berevbu6d++uXbt2WfpMmTJFM2fOVHJysjZu3KgqVaooOjpaFy5cuFO7BQAAyjGHB6Dp06dr0KBBio2NVVhYmJKTk+Xl5aW5c+des/97772nmJgYjRo1Sk2aNNGECRPUqlUrvf/++5Iuz/7MmDFDo0ePVrdu3dS8eXMtWLBAJ06c0NKlS+/gngEAgPKqkiN/eHFxsbZs2aL4+HhLm4uLi6KiopSenn7Nz6SnpysuLs6qLTo62hJuDh06pMzMTEVFRVne9/HxUWRkpNLT09WnT5+rxiwqKlJRUZHldW5uriQpLy/vlvftjiiDCa1yv89lhWNpP3Y+lhxH++FY2g/H0j7sfRyvjGc2m/+wr0MD0OnTp1VSUiJ/f3+rdn9/f/3yyy/X/ExmZuY1+2dmZlrev9J2vT7/KykpSePGjbuqPTAw8OZ2xFEm2X9In0k+9h/UGXAs7cfOx5LjaD8cS/vhWNpHWR3H8+fPy8fnxmM7NACVF/Hx8VazSqWlpTp79qxq1aolk8nkwMquLy8vT4GBgTp69Ki8vb0dXY5T41jaD8fSPjiO9sOxtB9nOJZms1nnz59X3bp1/7CvQwOQr6+vXF1dlZWVZdWelZWlgICAa34mICDghv2v/DcrK0t16tSx6hMeHn7NMd3d3eXu7m7VVr16dVt2xWG8vb3L7R9EZ8OxtB+OpX1wHO2HY2k/5f1Y/tHMzxUOXQTt5uam1q1bKzU11dJWWlqq1NRUtW3b9pqfadu2rVV/SVq1apWlf3BwsAICAqz65OXlaePGjdcdEwAAGIvDT4HFxcVp4MCBioiIUJs2bTRjxgwVFBQoNjZWkjRgwADVq1dPSUlJkqRhw4apQ4cOmjZtmh577DEtWrRImzdv1ocffihJMplMGj58uCZOnKiGDRsqODhYY8aMUd26ddW9e3dH7SYAAChHHB6AevfurVOnTikhIUGZmZkKDw9XSkqKZRFzRkaGXFx+n6h64IEH9Nlnn2n06NF688031bBhQy1dulRNmza19HnttddUUFCgwYMHKycnR+3atVNKSoo8PDzu+P6VFXd3dyUmJl516g6241jaD8fSPjiO9sOxtJ+KdixN5pu5VgwAAKACcfiNEAEAAO40AhAAADAcAhAAADAcAhAAADAcAhAM5+DBg44uAQDgYAQgGE5oaKg6duyov//977pwoQyekmggly5d0vjx43Xs2DFHl+L0fvvtNxUWFlpeHzlyRDNmzNDKlSsdWJXz4+/4rbl48aIaNGigPXv2OLqUMkMAckKXLl3St99+qw8++EDnz5+XJJ04cUL5+fkOrsw5bN26Vc2bN1dcXJwCAgL0f//3f9q0aZOjy3JKlSpV0rvvvqtLly45uhSn161bNy1YsECSlJOTo8jISE2bNk3dunXT7NmzHVydcyktLdWECRNUr149Va1a1TLrO2bMGH388ccOrs45VK5cucKHRwKQkzly5IiaNWumbt266aWXXtKpU6ckSZMnT9bIkSMdXJ1zCA8P13vvvacTJ05o7ty5OnnypNq1a6emTZtq+vTplmOKm9OpUyetXr3a0WU4va1bt6p9+/aSpCVLlsjf319HjhzRggULNHPmTAdX51wmTpyoefPmacqUKXJzc7O0N23aVB999JEDK3MuL730kiZPnlxh/4HDjRCdTPfu3VWtWjV9/PHHqlWrlnbs2KGQkBClpaVp0KBB2rdvn6NLdDpFRUX6f//v/yk+Pl7FxcVyc3PT008/rcmTJ1s9UBfXlpycrHHjxqlfv35q3bq1qlSpYvX+E0884aDKnIuXl5d++eUX3X333Xr66ad17733KjExUUePHlWjRo2sTo/hxkJDQ/XBBx+oc+fOqlatmuX/k7/88ovatm2rc+fOObpEp9CjRw+lpqaqatWqatas2VV/t7/44gsHVWYfDn8UBmzzww8/aP369Vb/qpGkoKAgHT9+3EFVOafNmzdr7ty5WrRokapUqaKRI0fq+eef17FjxzRu3Dh169aNU2M34cUXX5QkTZ8+/ar3TCaTSkpK7nRJTik0NFRLly5Vjx49tGLFCo0YMUKSlJ2dXa6fvF0eHT9+XKGhoVe1l5aW6uLFiw6oyDlVr15dvXr1cnQZZYYA5GRKS0uv+YVy7NgxVatWzQEVOZ/p06frk08+0d69e/Xoo49qwYIFevTRRy3PnAsODta8efMUFBTk2EKdRGlpqaNLqBASEhL0zDPPaMSIEercubPatm0rSVq5cqVatmzp4OqcS1hYmH744QfVr1/fqn3JkiUcSxt88sknji6hTBGAnEyXLl00Y8YMffjhh5Iu/ws7Pz9fiYmJevTRRx1cnXOYPXu2nnvuOT377LPXPcXl5+fHYslbcOHChQr10OE76cknn1S7du108uRJtWjRwtLeuXNn9ejRw4GVOZ+EhAQNHDhQx48fV2lpqb744gvt3btXCxYs0Ndff+3o8lBOsAbIyRw7dkzR0dEym83at2+fIiIitG/fPvn6+mrNmjXy8/NzdIkwmJKSEr3zzjtKTk5WVlaWfv31V4WEhGjMmDEKCgrS888/7+gSnVJeXp6+++47NWrUSE2aNHF0OU7nhx9+0Pjx47Vjxw7l5+erVatWSkhIUJcuXRxdmlNZsmSJ/vGPfygjI0PFxcVW723dutVBVdkHV4E5mbvuuks7duzQm2++qREjRqhly5aaNGmStm3bRvixQU5OjqZNm6YXXnhBL7zwgv76178qNzfX0WU5pbfffpsrbuzg6aef1vvvvy/p8j2BIiIi9PTTT6t58+b617/+5eDqnE/79u21atUqZWdnq7CwUGvXriX82GjmzJmKjY2Vv7+/tm3bpjZt2qhWrVo6ePCgunbt6ujybhszQE6GUwy3b/PmzYqOjpanp6fatGkjSfrxxx/122+/aeXKlWrVqpWDK3QuXHFjHwEBAVqxYoVatGihzz77TImJidqxY4fmz5+vDz/8UNu2bXN0iU7j6NGjMplMuuuuuyRJmzZt0meffaawsDANHjzYwdU5j8aNGysxMVF9+/a1+rudkJCgs2fPWgK7s2IGyMn4+flp4MCBWrVqFYtPb9GIESP0xBNP6PDhw/riiy/0xRdf6NChQ/rTn/6k4cOHO7o8p8MVN/aRm5urmjVrSpJSUlLUq1cveXl56bHHHuP2FjZ65pln9P3330uSMjMzFRUVpU2bNumtt97S+PHjHVyd88jIyNADDzwgSfL09LTcePfPf/6zPv/8c0eWZhcEICczf/58FRYWqlu3bqpXr56GDx+uzZs3O7osp7J582a9/vrrqlTp92sAKlWqpNdee41jeQuuXHHzv7jixjaBgYFKT09XQUGBUlJSLKdrzp07x6yvjXbt2mWZ3f3HP/6hZs2aaf369fr00081b948xxbnRAICAnT27FlJ0t13360NGzZIkg4dOqSKcPKIq8CcTI8ePdSjRw+dP39eS5Ys0eeff677779fISEh6t+/vxISEhxdYrnn7e2tjIwMNW7c2Kr96NGj3ErgFnDFjX0MHz5c/fr1U9WqVXX33Xfr4YcfliStWbNGzZo1c2xxTubixYtyd3eXJH377beWm3E2btxYJ0+edGRpTqVTp0766quv1LJlS8XGxmrEiBFasmSJNm/erJ49ezq6vNvGGqAKYPfu3erXr5927tzJTeduwtChQ/Xll19q6tSplunddevWadSoUerVq5dmzJjh2AKdEFfc2MfmzZt19OhRPfLII6pataokadmyZapevboefPBBB1fnPCIjI9WxY0c99thj6tKlizZs2KAWLVpow4YNevLJJ3l4700qLS1VaWmpZbZ80aJFWr9+vRo2bKj/+7//u+qGvM6GAOSkLly4oK+++kqfffaZUlJS5O/vr759+2rSpEmOLq3cKy4u1qhRo5ScnKxLly7JbDbLzc1NQ4YM0aRJkyz/cgQcobi4WIcOHVKDBg2sTtPi5qWlpalHjx7Ky8vTwIEDNXfuXEnSm2++qV9++cXpH+EA+yAAOZkVK1bos88+09KlS1WpUiU9+eST6tevnx566CFHl+Z0CgsLdeDAAUlSgwYN5OXl5eCKYGSFhYV65ZVXNH/+fEmy3E/plVdeUb169fTGG284uELnUlJSory8PNWoUcPSdvjwYXl5eXHLEBv88MMP+uCDD3TgwAEtWbJE9erV08KFCxUcHKx27do5urzbwj8vnEyPHj30pz/9yfL4hsqVKzu6JKfQs2dPzZs3T97e3n947rpq1aq699579Ze//EU+Pj53qELnVaNGDZlMpqvaTSaTPDw8FBoaqmeffVaxsbEOqM55xMfHa8eOHUpLS1NMTIylPSoqSmPHjiUA2cjV1dUq/Eji8TY2+te//qU///nP6tevn7Zt26aioiJJl69YfOedd7R8+XIHV3h7CEBOJisri4W6t8DHx8fyJf1HoaaoqEjJyclat26dvvrqqztRnlNLSEjQ22+/ra5du1quvNm0aZNSUlL00ksv6dChQxoyZIguXbqkQYMGObja8mvp0qVavHix7r//fqtAee+991pmKnF9rVq1UmpqqmrUqKGWLVteM5Rf4ex3ML5TJk6cqOTkZA0YMECLFi2ytD/44IOaOHGiAyuzDwKQk6lWrZpKSkq0dOlS7dmzR9Lly5C7desmV1dXB1dXfv33Q/1u5gF/u3fv1n333VeWJVUYa9eu1cSJE/WXv/zFqv2DDz7QypUr9a9//UvNmzfXzJkzCUA3cOrUqWuemikoKLjhlzku69atm2X9Xvfu3R1bTAWxd+/eay6v8PHxUU5Ozp0vyM4IQE5m//79evTRR3X8+HE1atRIkpSUlKTAwEAtW7ZMDRo0cHCFFUOjRo20fv16R5fhFFasWKHJkydf1d65c2e9+uqrkqRHH32UUzh/ICIiQsuWLdMrr7wiSZbQ89FHH1meDI/rS0xMlHR57U/Hjh3VvHlzVa9e3bFFObmAgADt37//qlOHa9euVUhIiGOKsiNuhOhkhg4dqgYNGujo0aPaunWrtm7dqoyMDAUHB2vo0KGOLq/CcHV1tXoiN66vZs2a+s9//nNV+3/+8x/LnY0LCgo4dfsH3nnnHb355puW04XvvfeeunTpok8++URvv/22o8tzGq6ururSpQuPYLGDQYMGadiwYdq4caNMJpNOnDihTz/9VCNHjtSQIUMcXd5tYwbIyaxevVobNmywfLFIUq1atTRp0iTuEwKHGDNmjIYMGaLvv//e6tlqy5cvV3JysiRp1apV6tChgyPLLPfatWun7du3a9KkSWrWrJnluXTp6encCNFGTZs21cGDBxUcHOzoUpzOzp071bRpU7m4uCg+Pl6lpaXq3LmzCgsL9dBDD8nd3V0jR460zFQ6My6DdzI1a9bU119/bbmB3xXr1q3T448/brltOXAnrVu3Tu+//7727t0r6fIpxFdeeeWqP6fAnZCSkqL4+HhNmDBBrVu3VpUqVaze9/b2dlBl5Z+rq6tOnjwpPz8/hYSE6Mcff1S1atW0f/9+5efnKywszHKTTmdHAHIyAwYM0NatW/Xxxx9b/rW9ceNGDRo0SK1bt+Y5N4ATKy0t1f79+5WdnX3Vw46519fNc3H5fXXHfy8gN5vNMplM3DH/BmrVqqXly5crMjJSLi4uysrKUu3atR1dVpngFJiTmTlzpgYOHKi2bdta7gF08eJFdevWjUc4wGH44r59GzZs0DPPPKMjR45c9aBJvrRtc+VJ8LBdr1691KFDB9WpU0cmk0kRERHXvcL44MGDd7g6+2IGyEnt37/fchl8kyZNFBoa6uCKYFR8cdtHeHi47rnnHo0bN87y5fPfuCkn7pSUlBTt379fQ4cO1fjx4697AcOwYcPucGX2RQByAnFxcTfdd/r06WVYCXA1vrjto0qVKtqxYwf/mLGTc+fO6eOPP7a6X1psbKzVBSS4sdjYWM2cObPCXsFJAHICHTt2vKl+JpNJ3333XRlXA1jji9s+OnXqpNdee83qMRi4NWvWrNHjjz8uHx8fRURESJK2bNminJwc/ec//+G0LCQRgADcJr647ePLL7/U6NGjNWrUKDVr1uyq5/w1b97cQZU5n2bNmqlt27aaPXu2Zf1KSUmJXnzxRa1fv14//fSTgytEeUAAAnBb+OK2j/++cukKk8nElUu3wNPTU9u3b7fcLf+KvXv3Kjw8XL/99puDKkN5wlVgAG5Lr169JEnPPffcVe/xxX3zDh065OgSKoxWrVppz549VwWgPXv2cId3WBCAANwWvrjto379+o4uocIYOnSohg0bpv379+v++++XdPlqxVmzZmnSpEnauXOnpS8zlMbFKTAAdrF7925lZGSouLjY0mYymfT44487sKry7auvvrrpvk888UQZVlKxXOt04n/j1CIkAhCA23Tw4EH16NFDP/30k+WLRfr9Drx8wVzf/35R//fxu/L6Co7jzTty5MhN92Xmzbh4GjyA2zJs2DAFBwcrOztbXl5e2rVrl9asWaOIiAilpaU5urxyrbS01LKtXLlS4eHh+uabb5STk6OcnBwtX75crVq1UkpKiqNLdRoXL17UuHHjVFpaqvr16//hBuNiBgjAbfH19dV3332n5s2by8fHR5s2bVKjRo303Xff6dVXX9W2bdscXaJTaNq0qZKTk9WuXTur9h9++EGDBw+23NAPf8zHx0fbt2/nafC4IWaAANyWkpISy51ifX19deLECUmXTy1ceTo8/tiBAwdUvXr1q9p9fHx0+PDhO16PM+vevbuWLl3q6DJQznEVGIDb0rRpU+3YsUPBwcGKjIzUlClT5Obmpg8//FAhISGOLs9p3HfffYqLi9PChQvl7+8vScrKytKoUaPUpk0bB1fnXBo2bKjx48dr3bp1at26tapUqWL1/tChQx1UGcoTToEBuC0rVqxQQUGBevbsqf379+tPf/qTfv31V9WqVUuLFy9Wp06dHF2iU9i/f7969OihX3/9VYGBgZKko0ePqmHDhlq6dCmPGrHBjU59mUwmp3+KOeyDAATA7s6ePasaNWpc9WBU3JjZbNaqVav0yy+/SJKaNGmiqKgojiNQBghAAADAcFgDBADlREFBgVavXn3VDSUl1q3Y4lqPZflvc+fOvUOVoDwjAAFAObBt2zY9+uijKiwsVEFBgWrWrKnTp0/Ly8tLfn5+BCAbnDt3zur1xYsXtWvXLuXk5LAmDRYEIAAoB0aMGKHHH39cycnJ8vHx0YYNG1S5cmX1799fw4YNc3R5TuXLL7+8qq20tFRDhgxRgwYNHFARyiPWAAFAOVC9enVt3LhRjRo1UvXq1ZWenq4mTZpo48aNGjhwoGVhNG7d3r179fDDD+vkyZOOLgXlADdCBIByoHLlypZng/n5+SkjI0PS5RshHj161JGlVRgHDhzQpUuXHF0GyglOgQFAOdCyZUv9+OOPatiwoTp06KCEhASdPn1aCxcuVNOmTR1dnlOJi4uzem02m3Xy5EktW7ZMAwcOdFBVKG84BQYA5cDmzZt1/vx5dezYUdnZ2RowYIDWr1+ve+65Rx999JHCw8MdXaLT6Nixo9VrFxcX1a5dW506ddJzzz2nSpX4tz8IQABQLvz2228ym83y8vKSJB0+fFhffvmlwsLCFB0d7eDqnEthYaHMZrPlERiHDx/W0qVL1aRJE44lLFgDBADlQLdu3bRgwQJJUk5Oju6//35Nnz5d3bt31+zZsx1cnXPp3r27Fi5cKOn3Yzlt2jSOJawQgACgHNi6davat28vSVqyZIn8/f115MgRLViwQDNnznRwdc6FY4mbQQACgHKgsLBQ1apVkyStXLlSPXv2lIuLi+6//34dOXLEwdU5F44lbgYBCADKgdDQUC1dulRHjx7VihUr1KVLF0lSdna2vL29HVydc+FY4mYQgACgHEhISNDIkSMVFBSkyMhItW3bVtLlGYyWLVs6uDrnwrHEzeAqMAAoJzIzM3Xy5Em1aNHCclPETZs2ydvbW40bN3Zwdc6FY4k/QgACAACGwykwAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOAQgAABgOP8fjnjZip/n6r4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intuitives_sensors = pd.DataFrame(index=intuitives_emotion_avg.keys())\n",
    "intuitives_sensors[\"iNtuitives\"] = intuitives_emotion_avg.values()\n",
    "intuitives_sensors[\"Sensors\"] = sensors_emotion_avg.values()\n",
    "\n",
    "intuitives_sensors.plot.bar(color=[\"blue\", \"green\"])\n",
    "plt.ylabel(\"average value\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Feeling vs Thinking__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "feelers = sa.get_trait_types(\"f\")\n",
    "thinkers = sa.get_trait_types(\"t\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Sentiment__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feelers_sentiment_max = sa.get_types_sentiment_max(feelers)\n",
    "thinkers_sentiment_max = sa.get_types_sentiment_max(thinkers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feelers_thinkers_sentiment = pd.DataFrame(index=feelers_sentiment_max.keys())\n",
    "feelers_thinkers_sentiment[\"Feelers\"] = feelers_sentiment_max.values()\n",
    "feelers_thinkers_sentiment[\"Thinkers\"] = thinkers_sentiment_max.values()\n",
    "\n",
    "feelers_thinkers_sentiment.plot.bar(color=[\"red\", \"black\"])\n",
    "plt.ylabel(\"percentage value\")\n",
    "plt.legend(loc='center')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Emotion__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feelers_emotion_avg = sa.get_types_avg_emotion(feelers)\n",
    "thinkers_emotion_avg = sa.get_types_avg_emotion(thinkers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feelers_thinkers_emotions = pd.DataFrame(index=feelers_emotion_avg.keys())\n",
    "feelers_thinkers_emotions[\"Feelers\"] = feelers_emotion_avg.values()\n",
    "feelers_thinkers_emotions[\"Thinkers\"] = thinkers_emotion_avg.values()\n",
    "\n",
    "feelers_thinkers_emotions.plot.bar(color=[\"red\", \"black\"])\n",
    "plt.ylabel(\"average value\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Judging vs Perceiving__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Sentiment__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "perceivers = sa.get_trait_types(\"p\")\n",
    "judgers = sa.get_trait_types(\"j\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "perceivers_sentiment_max = sa.get_types_sentiment_max(perceivers)\n",
    "judgers_sentiment_max = sa.get_types_sentiment_max(judgers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perceivers_judgers_sentiment = pd.DataFrame(index=perceivers_sentiment_max.keys())\n",
    "perceivers_judgers_sentiment[\"Judgers\"] = perceivers_sentiment_max.values()\n",
    "perceivers_judgers_sentiment[\"Perceivers\"] = judgers_sentiment_max.values()\n",
    "\n",
    "perceivers_judgers_sentiment.plot.bar(color=[\"yellow\", \"brown\"])\n",
    "plt.ylabel(\"percentage value\")\n",
    "plt.legend(loc='center')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Emotion__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "perceivers_emotion_avg = sa.get_types_avg_emotion(perceivers)\n",
    "judgers_emotion_avg = sa.get_types_avg_emotion(judgers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perceivers_judgers_emotions = pd.DataFrame(index=perceivers_emotion_avg.keys())\n",
    "perceivers_judgers_emotions[\"Perceivers\"] = perceivers_emotion_avg.values()\n",
    "perceivers_judgers_emotions[\"Judgers\"] = judgers_emotion_avg.values()\n",
    "\n",
    "perceivers_judgers_emotions.plot.bar(color=[\"yellow\", \"brown\"])\n",
    "plt.ylabel(\"average value\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Cognitive functions analysis__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ni_doms = [\"infj\", \"intj\"]\n",
    "ne_doms = [\"entp\", \"enfp\"]\n",
    "fi_doms = [\"infp\", \"isfp\"]\n",
    "ti_doms = [\"intp\", \"istp\"]\n",
    "fe_doms = [\"enfj\", \"esfj\"]\n",
    "te_doms = [\"entj\", \"estj\"]\n",
    "si_doms = [\"istj\", \"isfj\"]\n",
    "se_doms = [\"estp\", \"esfp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Sentiment__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ni_doms_sentiment_max = sa.get_types_sentiment_max(ni_doms)\n",
    "ne_doms_sentiment_max = sa.get_types_sentiment_max(ne_doms)\n",
    "fi_doms_sentiment_max = sa.get_types_sentiment_max(fi_doms)\n",
    "ti_doms_sentiment_max = sa.get_types_sentiment_max(ti_doms)\n",
    "fe_doms_sentiment_max = sa.get_types_sentiment_max(fe_doms)\n",
    "te_doms_sentiment_max = sa.get_types_sentiment_max(te_doms)\n",
    "si_doms_sentiment_max = sa.get_types_sentiment_max(si_doms)\n",
    "se_doms_sentiment_max= sa.get_types_sentiment_max(se_doms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__POSITIVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_sentiment_comparison(sentiment):\n",
    "    doms = [\"Ni\", \"Ne\", \"Fi\", \"Ti\", \"Fe\", \"Te\", \"Si\", \"Se\"]\n",
    "    values = [ni_doms_sentiment_max[sentiment], ne_doms_sentiment_max[sentiment], fi_doms_sentiment_max[sentiment], ti_doms_sentiment_max[sentiment], fe_doms_sentiment_max[sentiment], te_doms_sentiment_max[sentiment],\n",
    "              si_doms_sentiment_max[sentiment], se_doms_sentiment_max[sentiment]]\n",
    "\n",
    "    plt.barh(doms, values, color=[\"blue\", \"orange\", \"green\", \"red\",\n",
    "             \"purple\", \"gray\", \"cyan\", \"yellow\"])\n",
    "    plt.title(sentiment)\n",
    "    plt.ylabel('Dominant function')\n",
    "    plt.xlabel(f'{sentiment} percentage')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "get_sentiment_comparison(\"POSITIVE\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__NEGATIVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_sentiment_comparison(\"NEGATIVE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Emotions__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ni_doms_emotions_avg = sa.get_types_avg_emotion(ni_doms)\n",
    "ne_doms_emotions_avg = sa.get_types_avg_emotion(ne_doms)\n",
    "fi_doms_emotions_avg = sa.get_types_avg_emotion(fi_doms)\n",
    "ti_doms_emotions_avg = sa.get_types_avg_emotion(ti_doms)\n",
    "fe_doms_emotions_avg = sa.get_types_avg_emotion(fe_doms)\n",
    "te_doms_emotions_avg = sa.get_types_avg_emotion(te_doms)\n",
    "si_doms_emotions_avg = sa.get_types_avg_emotion(si_doms)\n",
    "se_doms_emotions_avg = sa.get_types_avg_emotion(se_doms)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__LOVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_emotion_comparison(emotion):\n",
    "    doms = [\"Ni\", \"Ne\", \"Fi\", \"Ti\", \"Fe\", \"Te\", \"Si\", \"Se\"]\n",
    "    values = [ni_doms_emotions_avg[emotion], ne_doms_emotions_avg[emotion], fi_doms_emotions_avg[emotion], ti_doms_emotions_avg[emotion], fe_doms_emotions_avg[emotion], te_doms_emotions_avg[emotion],\n",
    "              si_doms_emotions_avg[emotion], se_doms_emotions_avg[emotion]]\n",
    "\n",
    "    plt.barh(doms, values, color=[\"blue\", \"orange\", \"green\", \"red\",\n",
    "             \"purple\", \"gray\", \"cyan\", \"yellow\"])\n",
    "    plt.title(emotion)\n",
    "    plt.ylabel('Dominant function')\n",
    "    plt.xlabel(f'average {emotion}')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "get_emotion_comparison(\"love\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SADNESS__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_emotion_comparison(\"sadness\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__JOY__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_emotion_comparison(\"joy\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SURPRISE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AqE5V6qEpLy/Xjh07dODAAZWXl7ss69Gjh1cKO6GkpESS9Oqrr6pz584uy4KDgytdx2q1ymq1erUOAAAQuDwONN98841uvfVW7dq1S4ZhuCyzWCwqKyvzWnGS1LhxY8XGxurnn3/W4MGDvbptAABQM3gcaO666y516tRJixcvVpMmTWSxWHxRl4v09HSNGTNGNptNffr0kcPh0Nq1a3X48GGNGzfO5/sHAACBzeNAs337ds2bN0+tWrXyRT2VGjFihGrXrq2pU6dq/PjxioiIUEJCgsaOHVttNQAAgMDlcaDp3LmzduzY4dNAk5KSopSUFJd5t956q2699Vaf7RMAAJiXx4Hmnnvu0f333699+/YpISFBoaGhLsvbtWvnteIAAADc4XGgueGGGyRJt912m3OexWKRYRg+GRQMAABwJh4Hmvz8fF/UAQAAUGUeB5pmzZr5og4AAIAqq9KD9fLy8vT8888rNzdXktS2bVvde++9atmypVeLAwAAcIfHrz749NNP1bZtW3333Xdq166d2rVrp2+//VYXX3yxsrOzfVEjAADAaXncQzNx4kTdd999evrppyvMnzBhgnr37u214gAAANzhcQ9Nbm6ubr/99grzb7vtNm3dutUrRQEAAHjC40DTsGFDbdiwocL8DRs2qFGjRt6oCQAAwCMeX3IaOXKk7rjjDv3888/q2rWrJGnVqlWaMmUK71UCAAB+4XGgefTRR1WnTh1NmzZNqampkqTY2FhNnjxZY8aM8XqBAAAAZ2IxDMOo6spHjhyRJNWpU8drBfmC3W6XzWZTcXGxoqKi/F0OAABwgyff31V6Ds0JgR5kAADAucGtQJOYmKicnBzVrVtXHTt2lMViOWXb77//3mvFAQAAuMOtQNO/f39ZrVbnz6cLNAAAANXtrMbQmAVjaAAAMB9Pvr89fg5NixYtVFhYWGF+UVGRWrRo4enmAAAAzprHgWbnzp0qKyurMN/hcOiXX37xSlEAAACecPsup4ULFzp//vTTT2Wz2Zyfy8rKlJOTo+bNm3u3OgAAADe4PYYmKOivzhyLxaK/rxIaGqr4+HhNmzZN1113nferPEsnrsFN1ESFK9zf5QAA4JRmpPm7hIDlk+fQlJeXS5KaN2+uNWvWqEGDBmdXJQAAgJd4/GC9/Px8X9QBAABQZR4PCh4zZoxmzJhRYf6LL76osWPHeqMmAAAAj3gcaD744AN169atwvyuXbtq3rx5XikKAADAEx4HmsLCQpc7nE6IiorSb7/95pWiAAAAPOFxoGnVqpU++eSTCvOXLl3Kg/UAAIBfeDwoeNy4cRo9erQOHjyoq666SpKUk5OjadOm6fnnn/d2fQAAAGfkcaC57bbb5HA49OSTT+rxxx+XJMXHx2vWrFkaOnSo1wsEAAA4E48DjSSNGjVKo0aN0sGDB1WrVi1FRkZ6uy4AAAC3VSnQnNCwYUNv1QEAAFBlHg8K3r9/v4YMGaLY2FiFhIQoODjYZQIAAKhuHvfQpKSkaPfu3Xr00UfVpEkTWSwWX9QFAADgNo8DzcqVK/XVV1+pQ4cOXi0kJSVFc+bMqTB/+/btatWqlVf3BQAAahaPA01cXFyFt217S58+ffTmm2+6zGOcDgAAOBOPx9A8//zzmjhxonbu3On1YqxWq2JiYlym4OBgffTRR0pMTFR4eLhatGih9PR0/fnnn17fPwAAMCePe2huvvlm/f7772rZsqVq166t0NBQl+WHDh3yWnGS9NVXX2no0KGaMWOGLr/8cuXl5emOO+6QJKWlpVW6jsPhkMPhcH622+1erQkAAAQWjwONL58GvGjRIpdn2vTt21eHDx/WxIkTNWzYMElSixYt9Pjjj+vBBx88ZaDJyMhQenq6z+oEAACBxWL4akCMh1JSUvTrr79q1qxZznkRERFq166dSkpKXG4JLysr07Fjx3T06FHVrl27wrYq66GJi4vTRE1UuMJ9eyAAAHggzaj8H+f46/vbZrOpuLhYUVFRp23rcQ/N7t27T7u8adOmnm7SKSIiosIdTSUlJUpPT9f1119foX14eOXhxGq1ymq1VrkOAABgLh4Hmvj4+NM+e6asrOysCvq7xMREbdu2jVu3AQDAKXkcaNavX+/y+fjx41q/fr2ee+45Pfnkk14r7IRJkybpuuuuU9OmTXXjjTcqKChIGzdu1ObNm/XEE094fX8AAMB8PA407du3rzCvU6dOio2N1dSpUyu9NHQ2kpOTtWjRIj322GOaMmWKQkNDdeGFF2rEiBFe3Q8AADCvs3o55cnatGmjNWvWVHn9zMzMUy5LTk5WcnJylbcNAABqNo8Dzd+f6WIYhvbu3avJkyerdevWXisMAADAXR4Hmujo6AqDgg3DUFxcnLKysrxWGAAAgLs8DjTLly93+RwUFKSGDRuqVatWCgnx2hUsAAAAt7mVQBITE5WTk6O6devqiy++0AMPPFDpA+0AAAD8wa2XU+bm5uro0aOSpPT0dOfPAAAAgcCtHpoOHTpo+PDh6t69uwzD0NSpU13euXSySZMmebVAAACAM3Er0GRmZiotLU2LFi2SxWLR0qVLKx0vY7FYCDQAAKDauRVo2rRp47yDKSgoSDk5OWrUqJFPCwMAAHCXx7cllZeX+6IOAACAKnNrUDAAAEAgI9AAAADTO6eehJdanKqoqCh/lwEAALyMHhoAAGB6HgeaFi1aqLCwsML8oqIitWjRwitFAQAAeMLjQLNz506VlZVVmO9wOPTrr796pSgAAABPuD2GZuHChc6fP/30U9lsNufnsrIy5eTkKD4+3qvFAQAAuMPtQDNgwABJfz0NeNiwYS7LQkNDFR8fr2nTpnm1OAAAAHe4HWhOPFCvefPmWrNmjRo0aOCzogAAADzh8W3b+fn5vqgDAACgyqr0HJqcnBzl5OTowIEDFV6F8MYbb3ilMJ84adwPAAA+Zxj+ruCc4XGgSU9P12OPPaZOnTqpSZMmslgsvqgLAADAbR4Hmv/85z/KzMzUkCFDfFEPAACAxzx+Dk1paam6du3qi1oAAACqxONAM2LECL399tu+qAUAAKBKPL7kdOzYMc2ePVvLli1Tu3btFBoa6rL8ueee81pxAAAA7vA40GzatEkdOnSQJG3evNllGQOEAQCAP3gcaJYvX+6LOgAAAKrM4zE0AAAAgaZKD9Zbu3at3nvvPe3evVulpaUuy+bPn++VwgAAANzlcQ9NVlaWunbtqtzcXH344Yc6fvy4tmzZos8//9zlDdwAAADVxeNA89RTT2n69On6+OOPFRYWphdeeEE//vijBg0apKZNm/qiRgAAgNPyONDk5eXp2muvlSSFhYXp6NGjslgsuu+++zR79myvF1iZlJQUDRgwoFr2BQAAAp/HY2jq1q2rI0eOSJLOO+88bd68WQkJCSoqKtLvv/9+1gWd6dbvtLQ0vfDCCzJ44RcAAPj/eRxoevTooezsbCUkJOimm27Svffeq88//1zZ2dnq1avXWRe0d+9e58/vvvuuJk2apG3btjnnRUZGKjIy8qz3AwAAag6PA82LL76oY8eOSZIefvhhhYaG6uuvv9YNN9ygRx555KwLiomJcf5ss9lksVhc5kl/XXIqKirSggULznp/AADA/DwONPXq1XP+HBQUpIkTJ3q1IG9wOBxyOBzOz3a73Y/VAAAAX6vSc2jKy8u1Y8cOHThwQOXl5S7LevTo4ZXCzkZGRobS09P9XQYAAKgmHgeab775Rrfeeqt27dpVYWCuxWJRWVmZ14qrqtTUVI0bN8752W63Ky4uzo8VAQAAX/I40Nx1113q1KmTFi9erCZNmgTkCymtVqusVqu/ywAAANXE40Czfft2zZs3T61atfJFPQAAAB7z+MF6nTt31o4dO3xRCwAAQJV43ENzzz336P7779e+ffuUkJCg0NBQl+Xt2rXzWnEAAADusBgePnI3KKhip47FYpFhGAEzKPjv7Ha7bDabiiVF+bsYAMC5g6fanxXn93dxsaKiTv8N7nEPTX5+fpULAwAA8AWPA02zZs18UQcAAECVuRVoFi5cqL59+yo0NFQLFy48bdt+/fp5pTAAAAB3uTWGJigoSPv27VOjRo0qHUPj3BhjaAAA+D+MoTkrXh9Dc/LrDf7+qgMAAAB/8/g5NAAAAIGmSi+nXLNmjZYvX17pyymfe+45rxQGAADgLo8DzVNPPaVHHnlEbdq0UePGjV3e5RSI73UCAAA1n8eB5oUXXtAbb7yhlJQUH5QDAADgOY/H0AQFBalbt26+qAUAAKBKPA409913n1566SVf1AIAAFAlHr/Lqby8XNdee61++ukntW3btsLLKefPn+/VAr3Bk/vYAQBAYPDpu5zGjBmj5cuX68orr1T9+vUZCAwAAPzO40AzZ84cffDBB7r22mt9UQ8AAIDHPB5DU69ePbVs2dIXtQAAAFSJx4Fm8uTJSktL0++//+6LegAAADzm8SWnGTNmKC8vT40bN1Z8fHyFQcHff/+914oDAABwh8eBZsCAAT4oAwAAoOo8vm3bjLhtGwAA8/HpbdsnrFu3Trm5uZKkiy++WB07dqzqpqqNLcMmhfu7CgCAp4y0Gv9vb5wljwPNgQMH9K9//UsrVqxQdHS0JKmoqEhXXnmlsrKy1LBhQ2/XCAAAcFoe3+V0zz336MiRI9qyZYsOHTqkQ4cOafPmzbLb7RozZowvagQAADgtj3toPvnkEy1btkwXXXSRc17btm310ksv6eqrr/ZqcQAAAO7wuIemvLy8wq3akhQaGqry8nKvFAUAAOAJjwPNVVddpXvvvVd79uxxzvv111913333qVevXl4tDgAAwB0eB5oXX3xRdrtd8fHxatmypVq2bKnmzZvLbrdr5syZvqgRAADgtDweQxMXF6fvv/9ey5Yt048//ihJuuiii5SUlOT14gAAANxRpefQWCwW9e7dW7179/Z2PQAAAB7zKNCUl5crMzNT8+fP186dO2WxWNS8eXPdeOONGjJkiCwWi6/qBAAAOCW3x9AYhqF+/fppxIgR+vXXX5WQkKCLL75Yu3btUkpKigYOHOjLOgEAAE7J7R6azMxMffnll8rJydGVV17psuzzzz/XgAED9NZbb2no0KFeLxIAAOB03O6heeedd/TQQw9VCDPSX7dyT5w4UXPnzvVqcafSs2dPjR07tlr2BQAAAp/bgWbTpk3q06fPKZf37dtXGzdu9EpRJ6SkpMhisVSYnnnmGT3++ONe3RcAADAvty85HTp0SI0bNz7l8saNG+vw4cNeKepkffr00Ztvvukyr2HDhgoODvb6vgAAgDm5HWjKysoUEnLq5sHBwfrzzz+9UtTJrFarYmJiXOb17NlTHTp00PPPP+/1/QEAAPNxO9AYhqGUlBRZrdZKlzscDq8VBQAA4Am3A82wYcPO2MYXdzgtWrRIkZGRzs99+/Y94zoOh8MlYNntdq/XBQAAAofbgebv41iqy5VXXqlZs2Y5P0dEROiWW2457ToZGRlKT0/3dWkAACBAVOnVB9UpIiJCrVq18mid1NRUjRs3zvnZbrcrLi7O26UBAIAAEfCBpiqsVuspx/oAAICax+3n0AAAAAQqAg0AADC9gL7klJmZWen8FStWVGsdAAAgsNFDAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATC+g3+XkbcWpxYqKivJ3GQAAwMvooQEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZ3Tj2HRu/ZpNr+LgIAUOPdavi7gnMOPTQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0/BpoUlJSZLFY9PTTT7vMX7BggSwWi5+qAgAAZuP3Hprw8HBNmTJFhw8f9ncpAADApPweaJKSkhQTE6OMjIxTtlm5cqUuv/xy1apVS3FxcRozZoyOHj1ajVUCAIBA5vdAExwcrKeeekozZ87UL7/8UmF5Xl6e+vTpoxtuuEGbNm3Su+++q5UrV2r06NF+qBYAAAQivwcaSRo4cKA6dOigtLS0CssyMjI0ePBgjR07Vq1bt1bXrl01Y8YMvfXWWzp27Fil23M4HLLb7S4TAACouQIi0EjSlClTNGfOHOXm5rrM37hxozIzMxUZGemckpOTVV5ervz8/Eq3lZGRIZvN5pzi4uKq4xAAAICfBEyg6dGjh5KTk5Wamuoyv6SkRHfeeac2bNjgnDZu3Kjt27erZcuWlW4rNTVVxcXFzqmgoKA6DgEAAPhJiL8LONnTTz+tDh06qE2bNs55iYmJ2rp1q1q1auX2dqxWq6xWqy9KBAAAAShgemgkKSEhQYMHD9aMGTOc8yZMmKCvv/5ao0eP1oYNG7R9+3Z99NFHDAoGAABOARVoJOmxxx5TeXm583O7du30xRdf6KefftLll1+ujh07atKkSYqNjfVjlQAAIJBYDMMw/F2Er9ntdtlsNhW/KkXV9nc1AIAa79Ya/9VaLZzf38XFioqKOm3bgOuhAQAA8BSBBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmF6IvwuoVoOKpTO8CwIAAJgPPTQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0zqnn0Nhs/q4AAICaxzD8XQE9NAAAoAYg0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMzZaDJzMxUdHS0v8sAAAABIuACTUpKiiwWi55++mmX+QsWLJDFYpEk3Xzzzfrpp5/8UR4AAAhAARdoJCk8PFxTpkzR4cOHK11eq1YtNWrUqJqrAgAAgSogA01SUpJiYmKUkZFR6XIuOQEAgJMFZKAJDg7WU089pZkzZ+qXX37xdzkAACDABWSgkaSBAweqQ4cOSktL83hdh8Mhu93uMgEAgJorYAONJE2ZMkVz5sxRbm6uR+tlZGTIZrM5p7i4OB9VCAAAAkFAB5oePXooOTlZqampHq2Xmpqq4uJi51RQUOCjCgEAQCAI8XcBZ/L000+rQ4cOatOmjdvrWK1WWa1WH1YFAAACSUD30EhSQkKCBg8erBkzZvi7FAAAEKACPtBI0mOPPaby8nJ/lwEAAAKUxTAMw99F+JrdbpfNZpNULCnK3+UAAFCj+CpJnPj+Li4uVlTU6b+/TdFDAwAAcDoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoh/i6gOhUXS2d4FQQAADAhemgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDphfi7gOpgGIYkyW63+7kSAADgrhPf2ye+x0/nnAg0hYWFkqS4uDg/VwIAADx15MgR2Wy207Y5JwJNvXr1JEm7d+8+4y8E/mG32xUXF6eCggJFRUX5uxxUgnNkDpynwMc5cp9hGDpy5IhiY2PP2PacCDRBQX8NFbLZbPzPE+CioqI4RwGOc2QOnKfAxzlyj7sdEQwKBgAApkegAQAApndOBBqr1aq0tDRZrVZ/l4JT4BwFPs6ROXCeAh/nyDcshjv3QgEAAASwc6KHBgAA1GwEGgAAYHoEGgAAYHoEGgAAYHqmDTQvvfSS4uPjFR4ers6dO+u77747bfv3339fF154ocLDw5WQkKAlS5a4LDcMQ5MmTVKTJk1Uq1YtJSUlafv27b48hBrP2+do/vz5uvrqq1W/fn1ZLBZt2LDBh9WfG7x5jo4fP64JEyYoISFBERERio2N1dChQ7Vnzx5fH0aN5u0/R5MnT9aFF16oiIgI1a1bV0lJSfr22299eQg1nrfP0cnuuusuWSwWPf/8816uugYyTCgrK8sICwsz3njjDWPLli3GyJEjjejoaGP//v2Vtl+1apURHBxsPPPMM8bWrVuNRx55xAgNDTV++OEHZ5unn37asNlsxoIFC4yNGzca/fr1M5o3b2788ccf1XVYNYovztFbb71lpKenG6+++qohyVi/fn01HU3N5O1zVFRUZCQlJRnvvvuu8eOPPxqrV682LrvsMuPSSy+tzsOqUXzx52ju3LlGdna2kZeXZ2zevNm4/fbbjaioKOPAgQPVdVg1ii/O0Qnz58832rdvb8TGxhrTp0/38ZGYnykDzWWXXWb8+9//dn4uKyszYmNjjYyMjErbDxo0yLj22mtd5nXu3Nm48847DcMwjPLyciMmJsaYOnWqc3lRUZFhtVqNd955xwdHUPN5+xydLD8/n0DjBb48Ryd89913hiRj165d3in6HFMd56i4uNiQZCxbtsw7RZ9jfHWOfvnlF+O8884zNm/ebDRr1oxA4wbTXXIqLS3VunXrlJSU5JwXFBSkpKQkrV69utJ1Vq9e7dJekpKTk53t8/PztW/fPpc2NptNnTt3PuU2cWq+OEfwruo6R8XFxbJYLIqOjvZK3eeS6jhHpaWlmj17tmw2m9q3b++94s8RvjpH5eXlGjJkiMaPH6+LL77YN8XXQKYLNL/99pvKysrUuHFjl/mNGzfWvn37Kl1n3759p21/4r+ebBOn5otzBO+qjnN07NgxTZgwQbfccgsv4KsCX56jRYsWKTIyUuHh4Zo+fbqys7PVoEED7x7AOcBX52jKlCkKCQnRmDFjvF90DWa6QAMg8B0/flyDBg2SYRiaNWuWv8vB31x55ZXasGGDvv76a/Xp00eDBg3SgQMH/F0WJK1bt04vvPCCMjMzZbFY/F2OqZgu0DRo0EDBwcHav3+/y/z9+/crJiam0nViYmJO2/7Efz3ZJk7NF+cI3uXLc3QizOzatUvZ2dn0zlSRL89RRESEWrVqpf/3//6fXn/9dYWEhOj111/37gGcA3xxjr766isdOHBATZs2VUhIiEJCQrRr1y7df//9io+P98lx1BSmCzRhYWG69NJLlZOT45xXXl6unJwcdenSpdJ1unTp4tJekrKzs53tmzdvrpiYGJc2drtd33777Sm3iVPzxTmCd/nqHJ0IM9u3b9eyZctUv3593xzAOaA6/xyVl5fL4XCcfdHnGF+coyFDhmjTpk3asGGDc4qNjdX48eP16aef+u5gagJ/j0quiqysLMNqtRqZmZnG1q1bjTvuuMOIjo429u3bZxiGYQwZMsSYOHGis/2qVauMkJAQ49lnnzVyc3ONtLS0Sm/bjo6ONj766CNj06ZNRv/+/blt+yz44hwVFhYa69evNxYvXmxIMrKysoz169cbe/furfbjqwm8fY5KS0uNfv36Geeff76xYcMGY+/evc7J4XD45RjNztvnqKSkxEhNTTVWr15t7Ny501i7dq0xfPhww2q1Gps3b/bLMZqdL/6u+zvucnKPKQONYRjGzJkzjaZNmxphYWHGZZddZnzzzTfOZVdccYUxbNgwl/bvvfeeccEFFxhhYWHGxRdfbCxevNhleXl5ufHoo48ajRs3NqxWq9GrVy9j27Zt1XEoNZa3z9Gbb75pSKowpaWlVcPR1EzePEcnbqevbFq+fHk1HVHN481z9McffxgDBw40YmNjjbCwMKNJkyZGv379jO+++666DqdG8vbfdX9HoHGPxTAMwz99QwAAAN5hujE0AAAAf0egAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAYBqYLFYtGDBAn+XAdRYIf4uAADOBXv37lXdunX9XQZQY/GkYABeV1ZWJovFoqCgmtEJfDbHU1paqrCwMB9UBeBkNeNvGwCn9Mknn6h79+6Kjo5W/fr1dd111ykvL8+5vGvXrpowYYLLOgcPHlRoaKi+/PJLSZLD4dADDzyg8847TxEREercubNWrFjhbJ+Zmano6GgtXLhQbdu2ldVq1e7du7VmzRr17t1bDRo0kM1m0xVXXKHvv//eZV8//vijunfvrvDwcLVt21bLli2rcHmmoKBAgwYNUnR0tOrVq6f+/ftr586dpzzmw4cPa/DgwWrYsKFq1aql1q1b680335QkrVixQhaLRUVFRc72GzZskMVicW7zVMeTkpKiAQMGKD09XQ0bNlRUVJTuuusulZaWOrfVs2dPjR49WmPHjlWDBg2UnJwsyfWSU2lpqUaPHq0mTZooPDxczZo1U0ZGhnMbRUVFGjFihHMfV111lTZu3HjK4wVAoAFqvKNHj2rcuHFau3atcnJyFBQUpIEDB6q8vFySNHjwYGVlZenkztp3331XsbGxuvzyyyVJo0eP1urVq5WVlaVNmzbppptuUp8+fbR9+3bnOr///rumTJmi1157TVu2bFGjRo105MgRDRs2TCtXrtQ333yj1q1b65prrtGRI0ck/dXzMWDAANWuXVvffvutZs+erYcfftil/uPHjys5OVl16tTRV199pVWrVikyMlJ9+vRxCRIne/TRR7V161YtXbpUubm5mjVrlho0aODR762y45GknJwc5ebmasWKFXrnnXc0f/58paenu6w7Z84chYWFadWqVfrPf/5TYdszZszQwoUL9d5772nbtm2aO3eu4uPjnctvuukmHThwQEuXLtW6deuUmJioXr166dChQx4dA3BO8eurMQFUu4MHDxqSjB9++MEwDMM4cOCAERISYnz55ZfONl26dDEmTJhgGIZh7Nq1ywgODjZ+/fVXl+306tXLSE1NNQzj/96EvmHDhtPuu6yszKhTp47x8ccfG4ZhGEuXLjVCQkKMvXv3OttkZ2cbkowPP/zQMAzD+O9//2u0adPGKC8vd7ZxOBxGrVq1jE8//bTS/fzzn/80hg8fXumy5cuXG5KMw4cPO+etX7/ekGTk5+ef9niGDRtm1KtXzzh69Khz3qxZs4zIyEijrKzMMIy/3q7csWPHCvs9+Zjuuece46qrrnI5phO++uorIyoqyjh27JjL/JYtWxqvvPJKpccEwDDooQFquO3bt+uWW25RixYtFBUV5ewJ2L17tySpYcOGuvrqqzV37lxJUn5+vlavXq3BgwdLkn744QeVlZXpggsuUGRkpHP64osvXC5dhYWFqV27di773r9/v0aOHKnWrVvLZrMpKipKJSUlzn1v27ZNcXFxiomJca5z2WWXuWxj48aN2rFjh+rUqePcd7169XTs2DGX/Z9s1KhRysrKUocOHfTggw/q66+/9vj3VtnxSFL79u1Vu3Zt5+cuXbqopKREBQUFznmXXnrpabedkpKiDRs2qE2bNhozZow+++wz57KNGzeqpKRE9evXd/l95+fnn/J4AXCXE1Dj/fOf/1SzZs306quvKjY2VuXl5brkkktcLtcMHjxYY8aM0cyZM/X2228rISFBCQkJkqSSkhIFBwdr3bp1Cg4Odtl2ZGSk8+datWrJYrG4LB82bJgKCwv1wgsvqFmzZrJarerSpcspLxVVpqSkRJdeeqkzcJ2sYcOGla7Tt29f7dq1S0uWLFF2drZ69eqlf//733r22WedA3uNky6xHT9+vMI2Kjsed0VERJx2eWJiovLz87V06VItW7ZMgwYNUlJSkubNm6eSkhI1adLEZYzSCdHR0VWqBzgXEGiAGqywsFDbtm3Tq6++6hwPs3Llygrt+vfvrzvuuEOffPKJ3n77bQ0dOtS5rGPHjiorK9OBAwec23DXqlWr9PLLL+uaa66R9Nfg3t9++825vE2bNiooKND+/fvVuHFjSdKaNWtctpGYmKh3331XjRo1UlRUlNv7btiwoYYNG6Zhw4bp8ssv1/jx4/Xss886Q9DJt1Fv2LDB7e1u3LhRf/zxh2rVqiVJ+uabbxQZGam4uDi3tyFJUVFRuvnmm3XzzTfrxhtvVJ8+fXTo0CElJiZq3759CgkJcRlXA+D0uOQE1GB169ZV/fr1NXv2bO3YsUOff/65xo0bV6FdRESEBgwYoEcffVS5ubm65ZZbnMsuuOACDR48WEOHDtX8+fOVn5+v7777ThkZGVq8ePFp99+6dWv997//VW5urr799lsNHjzYGQQkqXfv3mrZsqWGDRumTZs2adWqVXrkkUckydk7MnjwYDVo0ED9+/fXV199pfz8fK1YsUJjxozRL7/8Uul+J02apI8++kg7duzQli1btGjRIl100UWSpFatWikuLk6TJ0/W9u3btXjxYk2bNs3t32lpaaluv/12bd26VUuWLFFaWppGjx7t0S3dzz33nN555x39+OOP+umnn/T+++8rJiZG0dHRSkpKUpcuXTRgwAB99tln2rlzp77++ms9/PDDWrt2rdv7AM41BBqgBgsKClJWVpbWrVunSy65RPfdd5+mTp1aadvBgwdr48aNuvzyy9W0aVOXZW+++aaGDh2q+++/X23atNGAAQO0Zs2aCu3+7vXXX9fhw4eVmJioIUOGaMyYMc67hSQpODhYCxYsUElJif7xj39oxIgRzrucwsPDJUm1a9fWl19+qaZNm+r666/XRRddpNtvv13Hjh07ZY9NWFiYUlNT1a5dO/Xo0UPBwcHKysqSJIWGhjrDRLt27TRlyhQ98cQT7v1CJfXq1UutW7dWjx49dPPNN6tfv36aPHmy2+tLUp06dfTMM8+oU6dO+sc//qGdO3dqyZIlCgoKksVi0ZIlS9SjRw8NHz5cF1xwgf71r39p165dzl4sABXxYD0AAWXVqlXq3r27duzYoZYtW/q7HBcpKSkqKiriFQZAAGIMDQC/+vDDDxUZGanWrVtrx44duvfee9WtW7eACzMAAhuBBoBfHTlyRBMmTNDu3bvVoEEDJSUleTSmBQAkLjkBAIAagEHBAADA9Ag0AADA9Ag0AADA9Ag0AADA9Ag0AADA9Ag0AADA9Ag0AADA9Ag0AADA9Ag0AADA9P4/LSDgo2YuHUQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_emotion_comparison(\"surprise\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Clustering__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import clustering \n",
    "from sklearn.cluster import KMeans\n",
    "importlib.reload(clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "TYPES = ['infj',\n",
    "         'intj',\n",
    "         'entj',\n",
    "         'infp',\n",
    "         'intp',\n",
    "         'enfp',\n",
    "         'entp',\n",
    "         'enfj',\n",
    "         'isfj',\n",
    "         'istj',\n",
    "         'istp',\n",
    "         'isfp',\n",
    "         'esfj',\n",
    "         'estj',\n",
    "         'estp',\n",
    "         'esfp']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:04<00:00,  3.20it/s]\n"
     ]
    }
   ],
   "source": [
    "types_rep = dict()\n",
    "\n",
    "for type in tqdm(TYPES):\n",
    "    type_rep = list(sa.get_emotions_avg(type).values())\n",
    "    sentiments, total = sa.get_sentiment_max(type)\n",
    "    type_sentiment = [n/total for n in sentiments.values()]\n",
    "    type_rep.extend(type_sentiment)\n",
    "    types_rep[type] = type_rep"
   ]
  },
  {
   "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>love</th>\n",
       "      <th>joy</th>\n",
       "      <th>anger</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surpise</th>\n",
       "      <th>fear</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>infj</th>\n",
       "      <td>0.042288</td>\n",
       "      <td>0.335714</td>\n",
       "      <td>0.220951</td>\n",
       "      <td>0.244089</td>\n",
       "      <td>0.046252</td>\n",
       "      <td>0.110706</td>\n",
       "      <td>0.376979</td>\n",
       "      <td>0.623021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intj</th>\n",
       "      <td>0.032165</td>\n",
       "      <td>0.343957</td>\n",
       "      <td>0.295120</td>\n",
       "      <td>0.189258</td>\n",
       "      <td>0.043589</td>\n",
       "      <td>0.095911</td>\n",
       "      <td>0.341722</td>\n",
       "      <td>0.658278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entj</th>\n",
       "      <td>0.030792</td>\n",
       "      <td>0.342150</td>\n",
       "      <td>0.336193</td>\n",
       "      <td>0.168791</td>\n",
       "      <td>0.034291</td>\n",
       "      <td>0.087785</td>\n",
       "      <td>0.411657</td>\n",
       "      <td>0.588343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infp</th>\n",
       "      <td>0.035468</td>\n",
       "      <td>0.302647</td>\n",
       "      <td>0.305387</td>\n",
       "      <td>0.196471</td>\n",
       "      <td>0.030803</td>\n",
       "      <td>0.129222</td>\n",
       "      <td>0.610499</td>\n",
       "      <td>0.389501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intp</th>\n",
       "      <td>0.025972</td>\n",
       "      <td>0.333150</td>\n",
       "      <td>0.303901</td>\n",
       "      <td>0.180137</td>\n",
       "      <td>0.037689</td>\n",
       "      <td>0.119151</td>\n",
       "      <td>0.488883</td>\n",
       "      <td>0.511117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          love       joy     anger   sadness   surpise      fear  positive  \\\n",
       "infj  0.042288  0.335714  0.220951  0.244089  0.046252  0.110706  0.376979   \n",
       "intj  0.032165  0.343957  0.295120  0.189258  0.043589  0.095911  0.341722   \n",
       "entj  0.030792  0.342150  0.336193  0.168791  0.034291  0.087785  0.411657   \n",
       "infp  0.035468  0.302647  0.305387  0.196471  0.030803  0.129222  0.610499   \n",
       "intp  0.025972  0.333150  0.303901  0.180137  0.037689  0.119151  0.488883   \n",
       "\n",
       "      negative  \n",
       "infj  0.623021  \n",
       "intj  0.658278  \n",
       "entj  0.588343  \n",
       "infp  0.389501  \n",
       "intp  0.511117  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types_rep_df = pd.DataFrame.from_dict(types_rep, orient=\"index\").rename(columns={\n",
    "    0: \"love\", 1: \"joy\", 2: \"anger\", 3: \"sadness\", 4: \"surpise\", 5: \"fear\", 6: \"positive\", 7: \"negative\"})\n",
    "types_rep_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>love</th>\n",
       "      <th>joy</th>\n",
       "      <th>anger</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surpise</th>\n",
       "      <th>fear</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>infj</th>\n",
       "      <td>0.318426</td>\n",
       "      <td>-0.377074</td>\n",
       "      <td>-2.450774</td>\n",
       "      <td>2.788641</td>\n",
       "      <td>1.617522</td>\n",
       "      <td>0.413345</td>\n",
       "      <td>-1.499599</td>\n",
       "      <td>1.499599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intj</th>\n",
       "      <td>-0.785176</td>\n",
       "      <td>0.110812</td>\n",
       "      <td>-0.125270</td>\n",
       "      <td>0.639193</td>\n",
       "      <td>0.970200</td>\n",
       "      <td>-1.186034</td>\n",
       "      <td>-2.050117</td>\n",
       "      <td>2.050117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entj</th>\n",
       "      <td>-0.934949</td>\n",
       "      <td>0.003844</td>\n",
       "      <td>1.162537</td>\n",
       "      <td>-0.163157</td>\n",
       "      <td>-1.290038</td>\n",
       "      <td>-2.064491</td>\n",
       "      <td>-0.958129</td>\n",
       "      <td>0.958129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infp</th>\n",
       "      <td>-0.425069</td>\n",
       "      <td>-2.334270</td>\n",
       "      <td>0.196651</td>\n",
       "      <td>0.921967</td>\n",
       "      <td>-2.137698</td>\n",
       "      <td>2.415011</td>\n",
       "      <td>2.146629</td>\n",
       "      <td>-2.146629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intp</th>\n",
       "      <td>-1.460409</td>\n",
       "      <td>-0.528845</td>\n",
       "      <td>0.150066</td>\n",
       "      <td>0.281632</td>\n",
       "      <td>-0.463883</td>\n",
       "      <td>1.326253</td>\n",
       "      <td>0.247681</td>\n",
       "      <td>-0.247681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          love       joy     anger   sadness   surpise      fear  positive  \\\n",
       "infj  0.318426 -0.377074 -2.450774  2.788641  1.617522  0.413345 -1.499599   \n",
       "intj -0.785176  0.110812 -0.125270  0.639193  0.970200 -1.186034 -2.050117   \n",
       "entj -0.934949  0.003844  1.162537 -0.163157 -1.290038 -2.064491 -0.958129   \n",
       "infp -0.425069 -2.334270  0.196651  0.921967 -2.137698  2.415011  2.146629   \n",
       "intp -1.460409 -0.528845  0.150066  0.281632 -0.463883  1.326253  0.247681   \n",
       "\n",
       "      negative  \n",
       "infj  1.499599  \n",
       "intj  2.050117  \n",
       "entj  0.958129  \n",
       "infp -2.146629  \n",
       "intp -0.247681  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types_rep_df[types_rep_df.columns] = StandardScaler().fit_transform(types_rep_df)\n",
    "types_rep_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variation per principal component: [0.37860024 0.26971225]\n",
      "Cumulative variance explained by 2 principal components: 64.83%\n",
      "\n",
      "\n",
      "           love       joy     anger   sadness   surpise      fear  positive  \\\n",
      "PC_1  0.041741  0.145686  0.193594  0.206460  0.443459  0.323772  0.545639   \n",
      "PC_2  0.334721  0.018116  0.620070  0.464491  0.182331  0.477081  0.115205   \n",
      "\n",
      "      negative  \n",
      "PC_1  0.545639  \n",
      "PC_2  0.115205  \n",
      "\n",
      "*************** Most important features *************************\n",
      "As per PC 1:\n",
      " surpise     0.443459\n",
      "fear        0.323772\n",
      "positive    0.545639\n",
      "negative    0.545639\n",
      "Name: PC_1, dtype: float64\n",
      "\n",
      "\n",
      "As per PC 2:\n",
      " love       0.334721\n",
      "anger      0.620070\n",
      "sadness    0.464491\n",
      "fear       0.477081\n",
      "Name: PC_2, dtype: float64\n",
      "\n",
      "******************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Reducing via PC\n",
    "pca_result, pca_2 = clustering.pca_embeddings(types_rep_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model selection and clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter: {'n_clusters': 2} Score 0.20050511298867701\n",
      "Parameter: {'n_clusters': 3} Score 0.21081897695453838\n",
      "Parameter: {'n_clusters': 4} Score 0.22183697065739735\n",
      "Parameter: {'n_clusters': 5} Score 0.16082658010982287\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimum num of clusters = 4\n",
      "Visualizing the data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alessia/Documenti/Progetti/mbti_sentiment/venv/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  HyperTuning the Parameter for KMeans\n",
    "optimum_num_clusters = clustering.kmean_hyper_param_tuning(types_rep_df)\n",
    "print(\"optimum num of clusters =\", optimum_num_clusters)\n",
    "\n",
    "# fitting KMeans\n",
    "kmeans = KMeans(n_clusters=optimum_num_clusters)\n",
    "kmeans.fit(types_rep_df)\n",
    "centroids = kmeans.cluster_centers_\n",
    "centroids_pca = pca_2.transform(centroids)\n",
    "\n",
    "print(\"Visualizing the data\")\n",
    "clustering.visualizing_results(pca_result, kmeans.labels_, centroids_pca)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Trait/Sentiment dependence__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used two statistics:\n",
    "\n",
    "-  [Chi-Squared](https://en.wikipedia.org/wiki/Chi-squared_test): a p_value less then 0.05 excludes the null hypothesis of independence between the variables, indicating a significant relationship between the variables.\n",
    "-  [Odds Ratio](https://it.wikipedia.org/wiki/Odds_ratio): you can find an intuitive explanation [here](https://www.statology.org/interpret-odds-ratios/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_trait_sentiment_contingency_table(trait, non_trait, trait_name):\n",
    "    trait_types = sa.get_trait_types(trait)\n",
    "    non_trait_types = sa.get_trait_types(non_trait)\n",
    "\n",
    "    trait_sentiment = []\n",
    "\n",
    "    for type in trait_types:\n",
    "        type_sentiment = sa.get_posts_sentiment(type)\n",
    "        for sentiment in type_sentiment.values():\n",
    "            trait_sentiment.append((trait, max(sentiment, key=lambda x:x[\"score\"])[\"label\"]))\n",
    "\n",
    "    for type in non_trait_types:\n",
    "        type_sentiment = sa.get_posts_sentiment(type)\n",
    "\n",
    "        for sentiment in type_sentiment.values():\n",
    "            trait_sentiment.append((non_trait, max(sentiment, key=lambda x:x[\"score\"])[\"label\"]))\n",
    "\n",
    "    trait_sentiment = pd.DataFrame(trait_sentiment).rename(columns = {0: trait_name, 1: \"sentiment\"})\n",
    "\n",
    "    sentiment_contingency  = pd.crosstab(trait_sentiment[trait_name], trait_sentiment.sentiment, margins = True)\n",
    "\n",
    "    return sentiment_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_chi2_significance(contingency_table, p_value):\n",
    "    if p_value < 0.05:\n",
    "        print(f\"significant relationship between {contingency_table.index.name} and {contingency_table.columns.name}\")\n",
    "    else:\n",
    "        print(f\"non significant relationship between {contingency_table.index.name} and {contingency_table.columns.name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_odds_ratio_meaning(contingency_table, oddsratio, pvalue):\n",
    "\n",
    "    if pvalue < 0.05:\n",
    "        i, d = divmod(oddsratio, 1)\n",
    "\n",
    "        if i == 1:\n",
    "            print(f\"{round(d*100,2)}% more probable to get a {contingency_table.columns[0]} score if {contingency_table.index[0]}\" )\n",
    "        else:\n",
    "            print(f\"{round((1 - d)*100,2)}% less probable to get a {contingency_table.columns[0]} score if {contingency_table.index[0]}\" )\n",
    "    else:\n",
    "        print(f\"non significant relationship between {contingency_table.index.name} and {contingency_table.columns.name}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Extraversion (e) and Introversion (i)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>sentiment</th>\n",
       "      <th>NEGATIVE</th>\n",
       "      <th>POSITIVE</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>76491</td>\n",
       "      <td>65992</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>208730</td>\n",
       "      <td>197868</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>285221</td>\n",
       "      <td>263860</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentiment     NEGATIVE  POSITIVE     All\n",
       "introversion                            \n",
       "e                76491     65992  142483\n",
       "i               208730    197868  406598\n",
       "All             285221    263860  549081"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = get_trait_sentiment_contingency_table(\"i\", \"e\", \"introversion\")\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 233.1422826473785 , p_value:  2.7803361605723146e-49\n",
      "significant relationship between introversion and sentiment\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(contingency_table, correction= True)\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "\n",
    "get_chi2_significance(contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.0987774528600733 , p value:  1.1506259794981315e-52\n",
      "9.88% more probable to get a NEGATIVE score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Intuition (n) and Sensing (s)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>sentiment</th>\n",
       "      <th>NEGATIVE</th>\n",
       "      <th>POSITIVE</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>256204</td>\n",
       "      <td>237877</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>29017</td>\n",
       "      <td>25983</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>285221</td>\n",
       "      <td>263860</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentiment  NEGATIVE  POSITIVE     All\n",
       "intuition                            \n",
       "n            256204    237877  494081\n",
       "s             29017     25983   55000\n",
       "All          285221    263860  549081"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = get_trait_sentiment_contingency_table(\"n\", \"s\", \"intuition\")\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 16.18541736957416 , p_value:  0.002780190479388022\n",
      "significant relationship between intuition and sentiment\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(contingency_table, correction= True)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.9644289462700179 , p value:  5.777651826245248e-05\n",
      "3.56% less probable to get a NEGATIVE score if n\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feeling (f) and Thinking (t)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>sentiment</th>\n",
       "      <th>NEGATIVE</th>\n",
       "      <th>POSITIVE</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>129998</td>\n",
       "      <td>146134</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>155223</td>\n",
       "      <td>117726</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>285221</td>\n",
       "      <td>263860</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentiment  NEGATIVE  POSITIVE     All\n",
       "feeling                              \n",
       "f            129998    146134  276132\n",
       "t            155223    117726  272949\n",
       "All          285221    263860  549081"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = get_trait_sentiment_contingency_table(\"f\", \"t\", \"feeling\")\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 5271.124156275215 , p_value:  0.0\n",
      "significant relationship between feeling and sentiment\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(contingency_table, correction= True)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.6746860243361212 , p value:  0.0\n",
      "32.53% less probable to get a NEGATIVE score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Judgement (j) and Perspective (p)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>sentiment</th>\n",
       "      <th>NEGATIVE</th>\n",
       "      <th>POSITIVE</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>107883</td>\n",
       "      <td>67908</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>177338</td>\n",
       "      <td>195952</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>285221</td>\n",
       "      <td>263860</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentiment  NEGATIVE  POSITIVE     All\n",
       "judgement                            \n",
       "j            107883     67908  175791\n",
       "p            177338    195952  373290\n",
       "All          285221    263860  549081"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = get_trait_sentiment_contingency_table(\"p\", \"j\", \"judgement\")\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 9201.44066772911 , p_value:  0.0\n",
      "significant relationship between judgement and sentiment\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(contingency_table, correction= True)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.755415662800323 , p value:  0.0\n",
      "75.54% more probable to get a NEGATIVE score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Trait/Emotion correlation__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I consider the __dependence__ between the __trait__ and the __single emotions__. \n",
    "\n",
    "Given the non-binarity of the emotion attribute, I compare the number of posts classified as conveying that emotion and the ones classified as containing the others. A post is assigned a certain emotion if its score is the maximum between all emotion scores: this dependency doesn't always match the visualized one, since in that case I was considering the average score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_trait_emotion_contingency_table(trait, non_trait, trait_name):\n",
    "    trait_types = sa.get_trait_types(trait)\n",
    "    non_trait_types = sa.get_trait_types(non_trait)\n",
    "\n",
    "    trait_emotion = []\n",
    "\n",
    "    for type in trait_types:\n",
    "        type_emotions = sa.get_posts_emotions(type)\n",
    "\n",
    "        for emotion in type_emotions.values():\n",
    "            trait_emotion.append((trait, max(emotion, key=lambda x:x[\"score\"])[\"label\"]))\n",
    "\n",
    "    for type in non_trait_types:\n",
    "        type_emotions = sa.get_posts_emotions(type)\n",
    "\n",
    "        for emotion in type_emotions.values():\n",
    "            trait_emotion.append((non_trait, max(emotion, key=lambda x:x[\"score\"])[\"label\"]))\n",
    "\n",
    "    trait_emotion = pd.DataFrame(trait_emotion).rename(columns = {0: trait_name, 1: \"emotion\"})\n",
    "    \n",
    "    return pd.crosstab(trait_emotion[trait_name],  trait_emotion[\"emotion\"], margins = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_emotion_contingency_table(all_emotions_contingency_table, emotion):\n",
    "    all_emotions = [\"anger\", \"fear\", \"joy\", \"love\", \"sadness\", \"surprise\"]\n",
    "    all_emotions.remove(emotion)\n",
    "\n",
    "    emotion_contingency_table = all_emotions_contingency_table.copy()\n",
    "    emotion_contingency_table[\"others\"] = emotion_contingency_table.loc[:, all_emotions].sum(axis=1)\n",
    "    emotion_contingency_table = emotion_contingency_table.drop(columns= all_emotions)\n",
    "    return emotion_contingency_table[[emotion, \"others\", \"All\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Introversion (i) and Extraversion (e)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>63309</td>\n",
       "      <td>7582</td>\n",
       "      <td>42587</td>\n",
       "      <td>4156</td>\n",
       "      <td>20130</td>\n",
       "      <td>4719</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>183835</td>\n",
       "      <td>23450</td>\n",
       "      <td>109841</td>\n",
       "      <td>10741</td>\n",
       "      <td>66038</td>\n",
       "      <td>12693</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>31032</td>\n",
       "      <td>152428</td>\n",
       "      <td>14897</td>\n",
       "      <td>86168</td>\n",
       "      <td>17412</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion        anger   fear     joy   love  sadness  surprise     All\n",
       "introversion                                                         \n",
       "e              63309   7582   42587   4156    20130      4719  142483\n",
       "i             183835  23450  109841  10741    66038     12693  406598\n",
       "All           247144  31032  152428  14897    86168     17412  549081"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_contingency_table = get_trait_emotion_contingency_table(\"i\", \"e\", \"introversion\")\n",
    "emotion_contingency_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ANGER__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>63309</td>\n",
       "      <td>79174</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>183835</td>\n",
       "      <td>222763</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>301937</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion        anger  others     All\n",
       "introversion                        \n",
       "e              63309   79174  142483\n",
       "i             183835  222763  406598\n",
       "All           247144  301937  549081"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anger_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"anger\")\n",
    "anger_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 25.954564778748 , p_value:  3.2319200416247976e-05\n",
      "significant relationship between introversion and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(anger_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(emotion_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.9689418753154841 , p value:  3.5272242797034036e-07\n",
      "3.11% less probable to get a anger score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(anger_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "\n",
    "get_odds_ratio_meaning(anger_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__FEAR__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>emotion</th>\n",
       "      <th>fear</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>7582</td>\n",
       "      <td>134901</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>23450</td>\n",
       "      <td>383148</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>31032</td>\n",
       "      <td>518049</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion        fear  others     All\n",
       "introversion                       \n",
       "e              7582  134901  142483\n",
       "i             23450  383148  406598\n",
       "All           31032  518049  549081"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fear_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"fear\")\n",
    "fear_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 39.36530267488039 , p_value:  5.855167799042502e-08\n",
      "significant relationship between introversion and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(fear_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(fear_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.9183163715936107 , p value:  2.8482244534280183e-10\n",
      "8.17% less probable to get a fear score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(fear_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(fear_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__JOY__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>emotion</th>\n",
       "      <th>joy</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>42587</td>\n",
       "      <td>99896</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>109841</td>\n",
       "      <td>296757</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>152428</td>\n",
       "      <td>396653</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion          joy  others     All\n",
       "introversion                        \n",
       "e              42587   99896  142483\n",
       "i             109841  296757  406598\n",
       "All           152428  396653  549081"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joy_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"joy\")\n",
    "joy_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 434.73520052175456 , p_value:  8.662412556530452e-93\n",
      "significant relationship between introversion and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(joy_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(joy_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.1517691529077987 , p value:  1.2545261374257852e-95\n",
      "15.18% more probable to get a joy score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(joy_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(joy_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__LOVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>emotion</th>\n",
       "      <th>love</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>4156</td>\n",
       "      <td>138327</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>10741</td>\n",
       "      <td>395857</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>14897</td>\n",
       "      <td>534184</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion        love  others     All\n",
       "introversion                       \n",
       "e              4156  138327  142483\n",
       "i             10741  395857  406598\n",
       "All           14897  534184  549081"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "love_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"love\")\n",
    "love_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 30.266144675879268 , p_value:  4.320234000397897e-06\n",
      "significant relationship between introversion and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(love_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(love_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.1072920788960148 , p value:  4.803093629371854e-08\n",
      "10.73% more probable to get a joy score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(love_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(joy_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SADNESS__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>emotion</th>\n",
       "      <th>sadness</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>20130</td>\n",
       "      <td>122353</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>66038</td>\n",
       "      <td>340560</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>86168</td>\n",
       "      <td>462913</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion       sadness  others     All\n",
       "introversion                         \n",
       "e               20130  122353  142483\n",
       "i               66038  340560  406598\n",
       "All             86168  462913  549081"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sadness_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"sadness\")\n",
    "sadness_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 356.2560993048797 , p_value:  7.818700203738068e-76\n",
      "significant relationship between introversion and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(sadness_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(sadness_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.8484551258491644 , p value:  7.064728314233499e-81\n",
      "15.15% less probable to get a sadness score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(sadness_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(sadness_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SURPRISE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>emotion</th>\n",
       "      <th>surprise</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>introversion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>4719</td>\n",
       "      <td>137764</td>\n",
       "      <td>142483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>12693</td>\n",
       "      <td>393905</td>\n",
       "      <td>406598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>17412</td>\n",
       "      <td>531669</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion       surprise  others     All\n",
       "introversion                          \n",
       "e                 4719  137764  142483\n",
       "i                12693  393905  406598\n",
       "All              17412  531669  549081"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"surprise\")\n",
    "surprise_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 12.43298336116779 , p_value:  0.014405823238360033\n",
      "significant relationship between introversion and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(surprise_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(surprise_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.0630200273097963 , p value:  0.00045618988587195735\n",
      "6.3% more probable to get a surprise score if e\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(surprise_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(surprise_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Intuition (n) Sensing (s)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>222250</td>\n",
       "      <td>28089</td>\n",
       "      <td>136147</td>\n",
       "      <td>13041</td>\n",
       "      <td>79070</td>\n",
       "      <td>15484</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>24894</td>\n",
       "      <td>2943</td>\n",
       "      <td>16281</td>\n",
       "      <td>1856</td>\n",
       "      <td>7098</td>\n",
       "      <td>1928</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>31032</td>\n",
       "      <td>152428</td>\n",
       "      <td>14897</td>\n",
       "      <td>86168</td>\n",
       "      <td>17412</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     anger   fear     joy   love  sadness  surprise     All\n",
       "intuition                                                         \n",
       "n          222250  28089  136147  13041    79070     15484  494081\n",
       "s           24894   2943   16281   1856     7098      1928   55000\n",
       "All        247144  31032  152428  14897    86168     17412  549081"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_contingency_table = get_trait_emotion_contingency_table(\"n\", \"s\", \"intuition\")\n",
    "emotion_contingency_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ANGER__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 12.43298336116779 , p_value:  0.014405823238360033\n",
      "significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "anger_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"anger\")\n",
    "anger_contingency_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 1.5599847144153975 , p_value:  0.815965432851861\n",
      "non significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(anger_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(anger_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.9887833822354483 , p value:  0.2124471025209872\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(anger_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__FEAR__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>emotion</th>\n",
       "      <th>fear</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>28089</td>\n",
       "      <td>465992</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>2943</td>\n",
       "      <td>52057</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>31032</td>\n",
       "      <td>518049</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     fear  others     All\n",
       "intuition                       \n",
       "n          28089  465992  494081\n",
       "s           2943   52057   55000\n",
       "All        31032  518049  549081"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fear_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"fear\")\n",
    "fear_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 10.365887191284395 , p_value:  0.034695366625143664\n",
      "significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(fear_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(anger_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.0662196731284856 , p value:  0.001230886059159256\n",
      "6.62% more probable to get a fear score if n\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(fear_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(fear_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__JOY__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>emotion</th>\n",
       "      <th>joy</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>136147</td>\n",
       "      <td>357934</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>16281</td>\n",
       "      <td>38719</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>152428</td>\n",
       "      <td>396653</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion       joy  others     All\n",
       "intuition                        \n",
       "n          136147  357934  494081\n",
       "s           16281   38719   55000\n",
       "All        152428  396653  549081"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joy_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"joy\")\n",
    "joy_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 103.32948651141055 , p_value:  1.9222375928201533e-21\n",
      "significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(joy_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(joy_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.9045824928223446 , p value:  5.27203885793983e-24\n",
      "9.54% less probable to get a fear score if n\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(joy_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(fear_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__LOVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>emotion</th>\n",
       "      <th>love</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>13041</td>\n",
       "      <td>481040</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>1856</td>\n",
       "      <td>53144</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>14897</td>\n",
       "      <td>534184</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     love  others     All\n",
       "intuition                       \n",
       "n          13041  481040  494081\n",
       "s           1856   53144   55000\n",
       "All        14897  534184  549081"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "love_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"love\")\n",
    "love_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 101.32113616091851 , p_value:  5.146998125858703e-21\n",
      "significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(love_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(love_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.7762577902332303 , p value:  1.8323754409138122e-22\n",
      "22.37% less probable to get a love score if n\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(love_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(love_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SADNESS__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>emotion</th>\n",
       "      <th>sadness</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>79070</td>\n",
       "      <td>415011</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>7098</td>\n",
       "      <td>47902</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>86168</td>\n",
       "      <td>462913</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion    sadness  others     All\n",
       "intuition                         \n",
       "n            79070  415011  494081\n",
       "s             7098   47902   55000\n",
       "All          86168  462913  549081"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sadness_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"sadness\")\n",
    "sadness_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 359.01541170752785 , p_value:  1.9828489662304126e-76\n",
      "significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(sadness_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(sadness_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.285789225591748 , p value:  1.0798647021885275e-83\n",
      "28.58% more probable to get a sadness score if n\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(sadness_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(sadness_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SURPRISE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>emotion</th>\n",
       "      <th>surprise</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intuition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>15484</td>\n",
       "      <td>478597</td>\n",
       "      <td>494081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>1928</td>\n",
       "      <td>53072</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>17412</td>\n",
       "      <td>531669</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion    surprise  others     All\n",
       "intuition                          \n",
       "n             15484  478597  494081\n",
       "s              1928   53072   55000\n",
       "All           17412  531669  549081"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"surprise\")\n",
    "surprise_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 22.251255917566706 , p_value:  0.0001786098512468883\n",
      "significant relationship between intuition and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(surprise_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(surprise_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.8905772879003868 , p value:  3.211400115212131e-06\n",
      "10.94% less probable to get a sadness score if n\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(surprise_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(sadness_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Feeling (f) and Thinking (t)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>123270</td>\n",
       "      <td>16130</td>\n",
       "      <td>72927</td>\n",
       "      <td>8755</td>\n",
       "      <td>46914</td>\n",
       "      <td>8136</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>123874</td>\n",
       "      <td>14902</td>\n",
       "      <td>79501</td>\n",
       "      <td>6142</td>\n",
       "      <td>39254</td>\n",
       "      <td>9276</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>31032</td>\n",
       "      <td>152428</td>\n",
       "      <td>14897</td>\n",
       "      <td>86168</td>\n",
       "      <td>17412</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion   anger   fear     joy   love  sadness  surprise     All\n",
       "feeling                                                         \n",
       "f        123270  16130   72927   8755    46914      8136  276132\n",
       "t        123874  14902   79501   6142    39254      9276  272949\n",
       "All      247144  31032  152428  14897    86168     17412  549081"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_contingency_table = get_trait_emotion_contingency_table(\"f\", \"t\", \"feeling\")\n",
    "emotion_contingency_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ANGER__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>123270</td>\n",
       "      <td>152862</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>123874</td>\n",
       "      <td>149075</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>301937</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion   anger  others     All\n",
       "feeling                        \n",
       "f        123270  152862  276132\n",
       "t        123874  149075  272949\n",
       "All      247144  301937  549081"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anger_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"anger\")\n",
    "anger_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 30.52331808841977 , p_value:  3.829221982804482e-06\n",
      "significant relationship between feeling and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(anger_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(anger_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.970470894544833 , p value:  3.3333479128901926e-08\n",
      "2.95% less probable to get a anger score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(anger_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(anger_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__FEAR__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>emotion</th>\n",
       "      <th>fear</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>16130</td>\n",
       "      <td>260002</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>14902</td>\n",
       "      <td>258047</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>31032</td>\n",
       "      <td>518049</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion   fear  others     All\n",
       "feeling                       \n",
       "f        16130  260002  276132\n",
       "t        14902  258047  272949\n",
       "All      31032  518049  549081"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fear_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"fear\")\n",
    "fear_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 37.521752532338205 , p_value:  1.4062455486759764e-07\n",
      "significant relationship between feeling and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(fear_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(fear_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.0742662555796638 , p value:  9.049755142810937e-10\n",
      "7.43% more probable to get a fear score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(fear_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(fear_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__JOY__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>emotion</th>\n",
       "      <th>joy</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>72927</td>\n",
       "      <td>203205</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>79501</td>\n",
       "      <td>193448</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>152428</td>\n",
       "      <td>396653</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     joy  others     All\n",
       "feeling                        \n",
       "f         72927  203205  276132\n",
       "t         79501  193448  272949\n",
       "All      152428  396653  549081"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joy_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"joy\")\n",
    "joy_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 505.09827232859345 , p_value:  5.288906503194323e-108\n",
      "significant relationship between feeling and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(joy_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(fear_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.8732641089661902 , p value:  7.147795633145081e-112\n",
      "12.67% less probable to get a joy score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(joy_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(joy_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__LOVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>emotion</th>\n",
       "      <th>love</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>8755</td>\n",
       "      <td>267377</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>6142</td>\n",
       "      <td>266807</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>14897</td>\n",
       "      <td>534184</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion   love  others     All\n",
       "feeling                       \n",
       "f         8755  267377  276132\n",
       "t         6142  266807  272949\n",
       "All      14897  534184  549081"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "love_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"love\")\n",
    "love_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 440.50311191877415 , p_value:  4.907394251055286e-94\n",
      "significant relationship between feeling and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(love_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(love_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.4223926903265642 , p value:  2.806794994844547e-98\n",
      "42.24% more probable to get a love score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(love_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(love_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SADNESS__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>emotion</th>\n",
       "      <th>sadness</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>46914</td>\n",
       "      <td>229218</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>39254</td>\n",
       "      <td>233695</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>86168</td>\n",
       "      <td>462913</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion  sadness  others     All\n",
       "feeling                         \n",
       "f          46914  229218  276132\n",
       "t          39254  233695  272949\n",
       "All        86168  462913  549081"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sadness_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"sadness\")\n",
    "sadness_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 705.8148959420819 , p_value:  1.9192538747303224e-151\n",
      "significant relationship between feeling and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(sadness_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(sadness_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.218482362340401 , p value:  1.0960460975180236e-155\n",
      "21.85% more probable to get a sadness score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(sadness_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(sadness_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SURPRISE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>emotion</th>\n",
       "      <th>surprise</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeling</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>8136</td>\n",
       "      <td>267996</td>\n",
       "      <td>276132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>9276</td>\n",
       "      <td>263673</td>\n",
       "      <td>272949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>17412</td>\n",
       "      <td>531669</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion  surprise  others     All\n",
       "feeling                          \n",
       "f            8136  267996  276132\n",
       "t            9276  263673  272949\n",
       "All         17412  531669  549081"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"surprise\")\n",
    "surprise_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 91.3398375822486 , p_value:  6.836649658706688e-19\n",
      "significant relationship between feeling and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(surprise_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(surprise_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.8629538059371703 , p value:  1.2498492754681423e-21\n",
      "13.7% less probable to get a sadness score if f\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(surprise_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(sadness_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Judgement (j) and Perspective (p)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>fear</th>\n",
       "      <th>joy</th>\n",
       "      <th>love</th>\n",
       "      <th>sadness</th>\n",
       "      <th>surprise</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>65309</td>\n",
       "      <td>11580</td>\n",
       "      <td>55008</td>\n",
       "      <td>5896</td>\n",
       "      <td>31226</td>\n",
       "      <td>6772</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>181835</td>\n",
       "      <td>19452</td>\n",
       "      <td>97420</td>\n",
       "      <td>9001</td>\n",
       "      <td>54942</td>\n",
       "      <td>10640</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>31032</td>\n",
       "      <td>152428</td>\n",
       "      <td>14897</td>\n",
       "      <td>86168</td>\n",
       "      <td>17412</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     anger   fear     joy   love  sadness  surprise     All\n",
       "judgement                                                         \n",
       "j           65309  11580   55008   5896    31226      6772  175791\n",
       "p          181835  19452   97420   9001    54942     10640  373290\n",
       "All        247144  31032  152428  14897    86168     17412  549081"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_contingency_table = get_trait_emotion_contingency_table(\"p\", \"j\", \"judgement\")\n",
    "emotion_contingency_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ANGER__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>65309</td>\n",
       "      <td>110482</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>181835</td>\n",
       "      <td>191455</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>247144</td>\n",
       "      <td>301937</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     anger  others     All\n",
       "judgement                        \n",
       "j           65309  110482  175791\n",
       "p          181835  191455  373290\n",
       "All        247144  301937  549081"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anger_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"anger\")\n",
    "anger_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 6452.458182677085 , p_value:  0.0\n",
      "significant relationship between judgement and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(anger_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(anger_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  0.6224016544404365 , p value:  0.0\n",
      "37.76% less probable to get a anger score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(anger_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(anger_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__FEAR__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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>emotion</th>\n",
       "      <th>fear</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>11580</td>\n",
       "      <td>164211</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>19452</td>\n",
       "      <td>353838</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>31032</td>\n",
       "      <td>518049</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     fear  others     All\n",
       "judgement                       \n",
       "j          11580  164211  175791\n",
       "p          19452  353838  373290\n",
       "All        31032  518049  549081"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fear_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"fear\")\n",
    "fear_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 424.61164034606634 , p_value:  1.3358464125777227e-90\n",
      "significant relationship between judgement and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(fear_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(fear_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.2827632942165925 , p value:  4.313499328144991e-92\n",
      "28.28% more probable to get a fear score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(fear_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(fear_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__JOY__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>emotion</th>\n",
       "      <th>joy</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>55008</td>\n",
       "      <td>120783</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>97420</td>\n",
       "      <td>275870</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>152428</td>\n",
       "      <td>396653</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion       joy  others     All\n",
       "judgement                        \n",
       "j           55008  120783  175791\n",
       "p           97420  275870  373290\n",
       "All        152428  396653  549081"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joy_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"joy\")\n",
    "joy_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 1607.7303352788167 , p_value:  0.0\n",
      "significant relationship between judgement and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(joy_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(joy_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.2896634514198468 , p value:  0.0\n",
      "28.97% more probable to get a joy score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(joy_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(joy_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__LOVE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>emotion</th>\n",
       "      <th>love</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>5896</td>\n",
       "      <td>169895</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>9001</td>\n",
       "      <td>364289</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>14897</td>\n",
       "      <td>534184</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion     love  others     All\n",
       "judgement                       \n",
       "j           5896  169895  175791\n",
       "p           9001  364289  373290\n",
       "All        14897  534184  549081"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "love_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"love\")\n",
    "love_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 402.3981025481383 , p_value:  8.436093432255556e-86\n",
      "significant relationship between judgement and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(love_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(love_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.4045337288339137 , p value:  1.752466616264798e-86\n",
      "40.45% more probable to get a love score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(love_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(love_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SADNESS__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>emotion</th>\n",
       "      <th>sadness</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>31226</td>\n",
       "      <td>144565</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>54942</td>\n",
       "      <td>318348</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>86168</td>\n",
       "      <td>462913</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion    sadness  others     All\n",
       "judgement                         \n",
       "j            31226  144565  175791\n",
       "p            54942  318348  373290\n",
       "All          86168  462913  549081"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sadness_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"sadness\")\n",
    "sadness_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 837.4488888596765 , p_value:  5.932648379797089e-180\n",
      "significant relationship between judgement and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(sadness_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(sadness_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.2515576410689253 , p value:  5.261838446874063e-181\n",
      "25.16% more probable to get a sadness score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(sadness_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(sadness_contingency_table, oddsratio, pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SURPRISE__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>emotion</th>\n",
       "      <th>surprise</th>\n",
       "      <th>others</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>judgement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>6772</td>\n",
       "      <td>169019</td>\n",
       "      <td>175791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>10640</td>\n",
       "      <td>362650</td>\n",
       "      <td>373290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>17412</td>\n",
       "      <td>531669</td>\n",
       "      <td>549081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion    surprise  others     All\n",
       "judgement                          \n",
       "j              6772  169019  175791\n",
       "p             10640  362650  373290\n",
       "All           17412  531669  549081"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_contingency_table = get_emotion_contingency_table(emotion_contingency_table, \"surprise\")\n",
    "surprise_contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi squared: 390.7506860810716 , p_value:  2.7711087792032237e-83\n",
      "significant relationship between judgement and emotion\n"
     ]
    }
   ],
   "source": [
    "g, p, dof, expctd = chi2_contingency(surprise_contingency_table)\n",
    "\n",
    "print(\"chi squared:\", g, \", p_value: \", p)\n",
    "get_chi2_significance(surprise_contingency_table, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds ratio:  1.36561247485323 , p value:  2.915335697037656e-84\n",
      "36.56% more probable to get a surprise score if j\n"
     ]
    }
   ],
   "source": [
    "oddsratio, pvalue = stats.fisher_exact(surprise_contingency_table.iloc[:2, :2])\n",
    "print(\"odds ratio: \", oddsratio, \", p value: \", pvalue)\n",
    "\n",
    "get_odds_ratio_meaning(surprise_contingency_table, oddsratio, pvalue)"
   ]
  }
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