Notebook.ipynb

{
   "cells": [
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "# Anxiety in Computer-Gamers: differences, similiratires and learnings\n",
            "\n",
            "# Overview \n",
            "\n",
            "In this project we decided to analyze anxiety in Gamers. We picked the dataset from kaggle because it intersected our personal interests. The data and survey can be found [here](https://www.kaggle.com/datasets/divyansh22/online-gaming-anxiety-data)\n",
            "\n",
            "The data was acquired by a survey published and shared online. This way everyone could participate. For us that also means taking into account that the distribution and answers can be scewed. \n",
            "\n",
            "## Motivation - "
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 1,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "<src.Dataset.Dataset object at 0x0000027DCA2FD600>\n"
               ]
            }
         ],
         "source": [
            "import matplotlib.pyplot as plt\n",
            "from src.Dataset import Dataset\n",
            "from src.Plotter import Plotter\n",
            "\n",
            "dataset = Dataset(\"data\\GamingStudy_data.csv\")\n",
            "dataframe = dataset.get_dataframe()\n",
            "print(dataset)\n",
            "plotter = Plotter(dataset)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "# Data Exploration\n",
            "\n",
            "Because the data was accumulated in a semi-professional way for a pre-study we had to clean it up and make some changes. \n",
            "\n",
            "Some columns could be answered with an open text field. Naturally the answeres in those columns are very diversified and hard to analyze. \n",
            "\n",
            "#### Affected Columns\n",
            "+ Whyplay\n",
            "+ Earnings \n",
            "+ League\n",
            "\n",
            "In the following we will explain if and how we used these columns. \n",
            "\n",
            "Stuff like deleted columns, general overview of the distribution (men women, games, platform) and problems with it \n",
            "\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Distribution of Participants \n",
            "### Gender\n",
            "\n",
            "'\"\"\"\n",
            "Put in reference to another survey\n",
            "\n",
            "\n",
            "\"\"\"'"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 2,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Gender\n",
                  "Male      12108\n",
                  "Female      688\n",
                  "Other        42\n",
                  "dtype: int64\n"
               ]
            },
            {
               "data": {
                  "image/png": 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QpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpgAAABwQpk7SsGFDmZmuuuoqX7cCAABKiVIfpqZNmyYz09tvv51n3eTJk2VmmjZtmg86AwAAF4NSH6YkaceOHbrzzjsVFBTkWVa2bFn1799f27dv92FnAADgQndBhKk1a9Zox44d6t27t2dZ7969tXPnTq1du9az7NZbb9W3336rlJQUJScnKyYmRk2aNClw21dccYXmz5+v1NRU7dmzR9OnT1f16tXP2bEAAIDS5YIIU9Lvb/fdd999nsf333+/pk6d6jWmQoUKGj9+vNq1a6eoqCjl5ORo9uzZ8vPzy3eboaGh+vrrr7Vu3TqFh4frtttuU0hIiD7++OMCeylTpoyCg4O9CgAAXJgCfd1AcZkxY4bGjh3ruYj8+uuv15133qkbb7zRM+bzzz/3es4DDzyg/fv3q0WLFtq0aVOebQ4aNEhr1qzRsGHDPMvuv/9+7dq1S5dddpl+++23fHsZOnSoRo4cWSzHBQAASrYL5szUgQMHNH/+fEVHR+u+++7T/PnzdeDAAa8xTZo00cyZM7V161YdPnxYCQkJkqQGDRrku82wsDDddNNNSk1N9dQvv/wiSWratOlpexk7dqwqVarkqbp16xbTUQIAgJLmgjkzJUlTp07Vm2++KUl67LHH8qyPiYnRzp079dBDD2n37t3y9/fXpk2bVKZMmXy35+/vr5iYGP35z3/Osy4pKem0fWRnZys7O7uIRwEAAEqTCypMffnll55gtGjRIq911apVU4sWLfTII49o2bJlkqTrr7++wO2tWbNGffr00bZt23TixIlz0zQAACjVLpi3+SQpJydHV1xxha644grl5OR4rcv9BN/DDz+spk2b6qabbtL48eML3N7kyZNVrVo1/etf/1K7du3UuHFj3XLLLXrvvffk739BTR0AACiiCy4R5F7bdCoz05133qmwsDBt3LhREyZM0ODBgwvcVlJSkq6//noFBARo0aJF2rhxoyZOnKjDhw/nCWsAAODi5CfJfN3EhS44OFhHjhzRm3GrlJ3D24UAABSX51q1P2fbzn39rlSpUr4nanJdcGemAAAAzifCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgAPCFAAAgINAXzdwMfnLtVFKTU31dRsAAKAYcWYKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAAWEKAADAQaCvG7iYjFmxVNk5J87pPp5r1f6cbh8AAHjjzBQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAICDIoWpBg0aKCgoqLh7AQAAKHUKHab8/Pz022+/qV69eueiHwAAgFKl0GHKzPTbb7+pevXq56IfAACAUqVIb/M9//zzeu2119SyZcvi7gcAAKBUCSzKkz744AOVL19e69evV3Z2tjIyMrzWc9YKAABcLIoUpp5++ulibgMAAKB0KlKYmj59enH3AQAAUCoV+T5TTZo00ejRozVr1izVrFlTknTrrbeqRYsWxdYcAABASVekMNWxY0f99NNPioiIUO/evVWxYkVJUuvWrfXSSy8Va4MAAAAlWZHC1Lhx4/TCCy+oc+fOys7O9iyPjY1V+/bti605AACAkq5IYapVq1aaPXt2nuX79+/nk3wAAOCiUqQwdejQIdWuXTvP8rZt2yoxMdG5KQAAgNKiSGFq1qxZevXVVxUSEiIzk7+/v6677jq9/vrrfNIPAABcVIoUpoYNG6YdO3YoMTFRFStWVFxcnL755hstX75cL7/8cnH3CAAAUGIV6T5Tx48f1z333KMXX3xRbdu2lb+/v9auXastW7YUd38AAAAlWpHCVK74+HjFx8cXVy8AAAClzlmHqb/97W9nvdHnnnuuSM0AAACUNmcdptq2bev1OCwsTAEBAdq8ebMkqVmzZjpx4oR+/PHH4u0QAACgBDvrMBUZGen59zPPPKPU1FRFR0fr0KFDkqQqVapo2rRp+vbbb4u9SQAAgJKqSJ/me+655zR06FBPkJJ+v/fUCy+8UOrf4ktISNBTTz3l6zYAAEApUaQwValSJYWEhORZXqtWLQUHB5/1dqZNmyYzy1NNmzYtSlsAAADnXZHC1OzZszVt2jT16dNHdevWVd26ddWnTx+99957+vzzzwu1rYULFyo0NNSrEhISitIWAADAeVekMDVw4EDNnz9fH3zwgbZv367t27dr5syZWrhwoR599NFCbSsrK0t79+71qpycHHXv3l2rV69WRkaGtm7dqhdffFEBAQGe55mZHn74YcXExOjo0aOKi4vTtddeq6ZNmyo2NlZpaWlavny5mjRp4nlOkyZNNGfOHO3Zs0epqalauXKloqKiCuyvUqVKeuedd7R3714dPnxYS5cuVevWrQs3YQAA4IJVpDCVkZGhxx57TNWrV1fbtm119dVXq1q1anrssceUnp7u3FTnzp31wQcfaNKkSWrRooUeeeQRDRgwQMOGDfMaN3z4cE2fPl1t2rTRL7/8olmzZumdd97R2LFjFR4eLkl68803PeMrVqyoBQsW6Oabb1bbtm21aNEixcTEqH79+qftZf78+QoNDVXXrl0VFhamNWvWaOnSpapateppn1OmTBkFBwd7FQAAuDA53bQzPT1dP/30k1MD3bt3V2pqqufxwoULFRISonHjxnm+5y8hIUHDhw/XX//6V40aNcozdtq0afrkk08kSa+++qpWrFih0aNHa/HixZKkiRMnatq0aZ7xGzZs0IYNGzyPhw8frl69eqlHjx6aPHlynt5uuukmtWrVSrVq1VJ2drYkafDgwerZs6f+8Ic/aMqUKfke09ChQzVy5MgizggAAChNihSmypcvryFDhigqKkq1atWSv7/3Ca7CXEAeGxurQYMGeR4fPXpUW7ZsUbt27bzORAUEBKhcuXIqV66cMjIyJMkrGO3du1eSvMLd3r17Va5cOQUHBys1NVXly5fXiBEj1L17d9WpU0eBgYEqV66cGjRokG9vYWFhqlixog4cOOC1vFy5cgUe49ixYzV+/HjP4+DgYCUmJp7NdAAAgFKmSGHq3XffVadOnTRjxgwlJSXJzIrcwNGjR7V161avZf7+/hoxYkS+F7NnZmZ6/n3s2DHPv3N7yG9Zbth77bXXdOutt+pPf/qTtmzZooyMDH366acqU6ZMvr35+/srKSlJN954Y551J98W4lTZ2dmeM1kAAODCVqQw1aVLF3Xr1k3Lly8v7n4kSWvWrNHll1+eJ2S56tChg95//33NmTNHklShQgU1atSowD5CQ0N1/Phxbd++vVh7AQAAF4YihamUlBQdPHiwuHvxGDVqlL744gvt3LlTn3zyiXJyctS6dWu1atVKw4cPL/J2t2zZot69eysmJkZmptGjR+d5i/JkX331lb7//nvNmTNHf/7zn7V582bVqVNHXbt21Zw5c/jqHAAAULRP8w0fPlyjRo1SuXLlirsfSdLixYvVvXt33XLLLVq1apVWrFihZ5991vns0DPPPKOUlBQtX75cMTExWrRokdasWVPgc7p27apvvvlGU6dO1a+//qoPP/xQjRo18lyjBQAALm5+kgp9wdOaNWvUtGlT+fn5adu2bV7XKUm/X7iN/woODtaRI0f0ZtwqZeecOKf7eq5V+3O6fQAALha5r9+VKlXyuvPAqYr0Nl/uNUcAAAAXuyKFqZPv9QQAAHAxK9I1U5JUuXJlPfDAAxozZoznbuBt27ZVnTp1iq05AACAkq5IZ6ZatWqlr776SocPH1ajRo00ZcoUpaSkqFevXmrYsKGio6OLu08AAIASqUhnpsaPH6/3339fzZo187qJ5sKFC9WxY8diaw4AAKCkK1KYateund555508yxMTExUaGurcFAAAQGlRpDCVmZmpSpUq5Vl++eWXa//+/c5NAQAAlBZFClNz587Viy++qMDA3y+5MjPVr19f48aN02effVasDQIAAJRkRQpTf/rTn1SzZk3t27dP5cqV09dff60tW7YoLS1Nw4YNK+4eAQAASqwifZovNTVVHTp00I033qiwsDD5+/trzZo1Wrp0aXH3BwAAUKIV6sxUUFCQunXr5nncuXNn1alTR6GhoeratateffVVlS1bttibBAAAKKkKdWbq3nvvVffu3TV//nxJ0uOPP65NmzYpIyNDktS8eXMlJSXpjTfeKPZGAQAASqJCnZm6++67NXXqVK9ld911lyIjIxUZGanBgwerb9++xdogAABASVaoMNWsWTP9+uuvnseZmZnKycnxPF65cqVatGhRfN0BAACUcIV6m69y5co6fvy453GtWrW81vv7+3PNFAAAuKgU6szUrl27dOWVV552fevWrbVr1y7npgAAAEqLQoWpBQsWaNSoUfmefQoKCtKIESM8F6cDAABcDAr1Nt+YMWPUt29fbd68WW+++aZ+/fVXmZmaN2+uxx9/XIGBgRozZsy56hUAAKDEKVSY2rdvn6677jq9/fbbGjdunPz8/CT9/nUyS5Ys0aOPPqp9+/adk0YBAABKokLfAX3btm3q0qWLqlatqksvvVSStGXLFqWkpBR7cwAAACVdkb5ORpJSUlK0atWq4uwFAACg1CnSFx0DAADgd4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4QpAAAAB4G+buBi8pdro5SamurrNgAAQDHizBQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAIADwhQAAICDiyJMRUdHKyUlxddtAACAC1CpClP16tXTu+++q8TERGVlZWnbtm164403VK1aNc+YhIQEPfXUUz7sEgAAXExKTZhq3LixVq9erWbNmql///669NJLNXDgQEVFRen7779X1apVz3tPgYGB532fAACg5LHSUAsWLLAdO3ZYUFCQ1/KQkBBLS0uzt956y2JjY+1Ukiw6OtpSUlKsc+fOFhcXZ6mpqbZw4UILDQ312taAAQMsLi7OMjIy7Oeff7ZBgwZ51jVs2NDMzO644w6LjY21jIwMGzBgwFn1HhwcbGZmwcHBPp9HiqIoiqLOrgrx+u37Zs9UVatWtRMnTtiQIUPyXf/OO+/YgQMHrFq1arZjxw574YUXLCQkxEJCQkz6PUxlZWXZ4sWLLSwszNq2bWubNm2yDz74wLONBx980BITE61Xr17WqFEj69WrlyUnJ9u9995r0n/DVHx8vGdM7dq18+2nTJkyFhwc7Kk6deoQpiiKoiiqlNUFFaauueYaMzO7/fbb813/9NNPm5lZzZo1LSEhwZ566imv9dHR0WZm1qRJE8+yQYMGWVJSkufx9u3b7c477/R63rBhw+y7774z6b9h6sknnzxjvyNGjMhzhowwRVEURVGlq842TJWaa6YK4ufnJ0n6/V29/B09elTx8fGex0lJSapVq5YkqUaNGmrQoIHee+89paameuqFF15Q06ZNvbazevXqM/YzduxYVapUyVN169YtymEBAIBSoFRcQb1lyxbl5OSoRYsWmjt3bp71zZs318GDB5WcnHzabRw7dszrsZnJ3//3LJn7vw899JB++OEHr3EnTpzwenz06NEz9pudna3s7OwzjgMAAKVfqTgzdfDgQS1ZskSPPvqogoKCvNaFhITo7rvv1kcffSTp9yATEBBQqO3v27dPu3btUpMmTbR161av2rZtW3EdBgAAuACVijAlSY8//rjKli2rRYsWqUOHDqpXr55uvfVWLVmyRImJiRo2bJgkadu2berYsaPq1Kmj6tWrn/X2R44cqaFDh+rJJ5/UZZddpiuvvFIDBgzQM888c64OCQAAXABKTZjasmWLwsPDtXXrVn300UfaunWr/vGPfyg2Nlbt27f33OH8xRdfVKNGjbR169YC3/Y71XvvvacHH3xQAwYM0E8//aSvv/5aAwYMUEJCwrk6JAAAcAHw0+9XouMcCg4O1pEjR1SpUiWlpqb6uh0AAHAWzvb1u9ScmQIAACiJCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOCFMAAAAOAn3dwMUkODjY1y0AAICzdLav24Sp86BatWqSpMTERB93AgAACis4OFipqamnXU+YOg8OHjwoSapbt26BPwx4Cw4OVmJiIvNWSMxb0TBvRcO8FQ3zVjS+mLfg4GDt3r27wDGEqfMoNTWVP5oiYN6KhnkrGuataJi3omHeiuZ8ztvZ7IcL0AEAABwQpgAAABwQps6DrKwsjRw5UllZWb5upVRh3oqGeSsa5q1omLeiYd6KpqTOm58k83UTAAAApRVnpgAAABwQpgAAABwQpgAAABwQpgAAABwQps6xQYMGKT4+XhkZGVq9erVuuOEGX7d03gwZMkQrV67UkSNHtHfvXs2ePVvNmjXLM27EiBFKTExUenq6YmNj1aJFC6/1ZcqU0aRJk7R//36lpaVp7ty5qlu3rteYKlWqaPr06Tp06JAOHTqk6dOnq3Llyuf0+M6XIUOGyMw0YcIEr+XMW1516tTRjBkzlJycrKNHj2rt2rW6+uqrvcYwb94CAgI0evRoxcfHKz09XVu3btXw4cPl5+fnNe5in7cOHTpo3rx5SkxMlJnp9ttvzzPmfM1R/fr1NW/ePKWlpWn//v2aOHGiLrnkkuI/6GJQ0LwFBgZq3Lhx2rBhg9LS0pSYmKh//vOfql27ttc2Ssu8GXVuqm/fvpaVlWUPPPCANW/e3CZMmGCpqalWv359n/d2PmrhwoUWHR1tLVq0sNatW1tMTIxt27bNypcv7xnz/PPP2+HDh61Xr17WsmVL+9e//mWJiYlWsWJFz5i33nrLdu7caVFRUdamTRtbunSprV271vz9/T1jFixYYBs2bLBrr73Wrr32WtuwYYPNmzfP53PgWuHh4RYfH2/r1q2zCRMmMG8FVJUqVSwhIcGmTp1q7dq1s4YNG1pkZKQ1adKEeSug/vKXv9j+/futa9eu1rBhQ+vTp48dOXLEnnzySebtpLrtttts9OjR1qtXLzMzu/32273Wn6858vf3tw0bNtjSpUutTZs2FhUVZbt27bJJkyb5fI4KO2+VKlWyxYsX2x133GHNmjWziIgI+/77723VqlVe2ygl8+b7yb5Qa8WKFfbWW295LYuLi7MxY8b4vDdfVI0aNczMrEOHDp5lu3fvtueff97zuEyZMpaSkmIPP/ywSb//sWVlZVnfvn09Y2rXrm3Hjx+3zp07myRr3ry5mZldc801njERERFmZtasWTOfH3dRq0KFCrZ582aLioqy2NhYrzDFvOWtsWPH2jfffFPgGOYtb8XExNi7777rtezTTz+16dOnM2+nqfzC1Pmao9tuu82OHz9utWvX9ozp16+fZWRkWHBwsM/nprDzdmqFh4ebmXlOOpSWeeNtvnPkkksuUVhYmBYvXuy1fPHixbruuut81JVv5Z5yzf3i58aNG6t27dpec5Sdna2vv/7aM0dhYWEqU6aM15ikpCRt3LjRM6Z9+/Y6dOiQVq5c6Rnzww8/6NChQ6V6ridPnqz58+dr6dKlXsuZt/z16NFDq1ev1scff6y9e/dqzZo1evDBBz3rmbf8LVu2TFFRUbrsssskSa1bt9YNN9ygBQsWSGLezsb5nKP27dtr48aNSkpK8oxZtGiRgoKCFBYWdk6P83yoXLmycnJydOjQIUmlZ974ouNzpEaNGgoMDNTevXu9lu/du1ehoaE+6sq3xo8fr2+//VabNm2SJM885DdHDRs29IzJysry/GGdPCb3+aGhodq3b1+e/e3bt6/UznW/fv109dVXq127dnnWMW/5a9KkiQYNGqTx48drzJgxuuaaazRp0iRlZWVpxowZzNtpvPrqq6pcubJ++eUXnThxQgEBARo2bJg+/PBDSfy+nY3zOUehoaF59nPo0CFlZWWV+nksW7asxo0bp1mzZnm+XLi0zBth6hwzM6/Hfn5+eZZdDN58803P/+M9VVHm6NQx+Y0vrXNdr149TZw4UZ07dy7wKxOYN2/+/v5avXq1hg0bJklat26dWrZsqUGDBmnGjBmeccybt379+umee+7RXXfdpU2bNqlNmzZ64403tHv3bk2fPt0zjnk7s/M1RxfiPAYGBurDDz+Uv7+/Hn300TOOL2nzxtt850hycrKOHz+eJ/HWqlUrTzq+0E2aNEk9evTQTTfdpMTERM/yPXv2SFKBc7Rnzx6VLVtWVapUKXBMSEhInv3WrFmzVM51WFiYQkJC9OOPP+rYsWM6duyYbrzxRj355JM6duyY55iYN29JSUmKi4vzWvbzzz+rQYMGkvh9O53XXntN48aN00cffaSNGzfqgw8+0IQJEzR06FBJzNvZOJ9ztGfPnjz7qVKlisqUKVNq5zEwMFAff/yxGjdurFtuucVzVkoqXfPm84vSLtRasWKFTZ482WvZpk2bLqoL0P/3f//Xdu3aZZdeemm+63fv3m2DBw/2PL7kkkvyvWjzjjvu8IwJDQ3N9+LDdu3aecZcc801pfLCVklWsWJFa9mypVetXLnSpk+fbi1btmTeTlMzZ87McwH6+PHj7bvvvuP3rYBKTk62gQMHei0bMmSIbd68mXk7TZ3uAvTzMUe5F1KHhoZ6xvTt27fUXoAeGBhon3/+uf30009Wo0aNPM8pRfPm+wm+UCv31gj33XefNW/e3MaPH2+pqanWoEEDn/d2Pmry5MmWkpJiHTt2tJCQEE8FBQV5xjz//POWkpJiPXv2tJYtW9rMmTPz/Tjxjh07LDIy0tq0aWNfffVVvh+LXbdunUVERFhERIStX7++1Hzk+mzq1E/zMW95Kzw83LKzs23o0KHWtGlT69+/v6Wlpdldd93FvBVQ06ZNs507d3pujdCzZ0/bt2+fjRs3jnk7qSpUqGBXXXWVXXXVVWZm9vTTT9tVV13l+dTZ+Zqj3I/4L1myxNq0aWORkZG2Y8eOEntrhILmLSAgwObMmWM7duyw1q1be71OXHLJJaVt3nw/2RdyDRo0yBISEiwzM9NWr17tdVuAC71OJzo62mvciBEjbPfu3ZaRkWH/+c9/PGdfcqts2bI2adIkS05OtqNHj9q8efOsXr16XmOqVq1qM2bMsMOHD9vhw4dtxowZVrlyZZ/PQXHVqWGKecu/unXrZhs2bLCMjAyLi4uzBx98MM8Y5s27KlasaBMmTLBt27ZZenq6bdmyxUaPHu31Ysa8yTp16pTvf8+mTZt23ueofv36FhMTY0ePHrXk5GSbNGmSlSlTxudzVNh5a9iw4WlfJzp16lSq5s3v//8BAACAIuACdAAAAAeEKQAAAAeEKQAAAAeEKQAAAAeEKQAAAAeEKQAAAAeEKQAAAAeEKQAoBDPT7bff7us2AJQghCkAOEnNmjX197//Xdu3b1dmZqaSkpL05Zdf6tprr5X0+5fZLly40MddAihJAn3dAACUJJ999pkuueQSRUdHKz4+XiEhIYqKilK1atUkqdi+YR7AhcXn391DURRVEqpy5cpmZtaxY8fTjjH77zffjxgx4ozfPzl48GDbunWrpaen27p166xPnz4+P06Kooq9fN4ARVFUiaiAgAA7cuSIjR8//rRfgHpymKpQoYLXN90/++yzlpaW5vmC25dfftni4uKsc+fO1rhxY4uOjraMjIwCwxpFUaWyfN4ARVFUianevXvbgQMHLD093ZYtW2avvPKKtWrVyrP+5DB1ckVERFh6errdcccdJsnKly9v6enpdu2113qNmzJlis2cOdPnx0lRVLGWzxugKIoqUVW2bFm7+eabbfjw4fbdd9/ZsWPHPG/d5Rem6tevb0lJSTZq1CjPsvDwcDMzS01N9aqsrCxbsWKFz4+RoqjiK7///wcA4DSmTJmiW265RY0aNZKZqWfPnpo7d64kqXz58vruu++UkJCg3r17e55zzTXX6IcfflCnTp2UmJjotb2srCzt2rXrvB4DgHOHT/MBwBnExcWpZ8+e+a774IMP5O/vrz/+8Y95npOZmakGDRrom2++OQ9dAvAVwhQA/L9q1arpk08+0dSpU7VhwwalpqYqPDxczz//vOdM1MlGjhypm2++WZ07d1bFihVVsWJFSdLhw4eVlpam119/XRMmTJC/v7+WLVumSpUq6brrrlNaWpqmT59+vg8PwDnk8/caKYqiSkKVKVPGxowZY6tXr7aUlBRLS0uzn3/+2UaNGmVBQUEmeV8zFRsbe8ZbIzzxxBP2888/W1ZWlu3du9cWLlxoHTp08PmxUhRVfMU1UwAAAA74OhkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAHhCkAAAAH/wckFbotjYlycwAAAABJRU5ErkJggg==",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plotter.distribution_plot(\"Gender\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Where is the data from ?  \n",
            "### Where did the participants found the survey ?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 3,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Reference\n",
                  "Reddit            12715\n",
                  "Other                57\n",
                  "TeamLiquid.net       51\n",
                  "CrowdFlower           2\n",
                  "dtype: int64\n"
               ]
            },
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plotter.distribution_plot(\"Reference\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Explanation of technical terms \n",
            "\n",
            "### SPIN \n",
            "SPIN stands for Social Phobia Inventory \n",
            "The SPIN is a standardized set of 17 question. After answering the questionnaire a “SPIN” value is calculated which is effective for screening for and measuring the severity of social anxiety disorder\n",
            "1. I am afraid of people in authority.\n",
            "2. I am bothered by blushing in front of people.\n",
            "3. Parties and social events scare me.\n",
            "4. I avoid talking to people I don’t know.\n",
            "5. Being criticized scares me a lot.\n",
            "6. I avoid doing things or speaking to people for fear of embarrassment.\n",
            "7. Sweating in front of people causes me distress.\n",
            "8. I avoid going to parties.\n",
            "9. I avoid activities in which I am the center of attention.\n",
            "10. Talking to strangers scares me.\n",
            "11. I avoid having to give speeches.\n",
            "12. I would do anything to avoid being criticized.\n",
            "13. Heart palpitations bother me when I am around people.\n",
            "14. I am afraid of doing things when people might be watching.\n",
            "15. Being embarrassed or looking stupid are among my worst fears.\n",
            "16. I avoid speaking to anyone in authority.\n",
            "17. Trembling or shaking in front of others is distressing to me. \n",
            "### GAD \n",
            "is a mental and behavioral, disorder, specifally an anxiety disorder characterized by excessive, uncontrollable and often irrational worry about events or activities. There are specific questionaires you can use to evaluate the disorder. In the questionnaire the minimum is 0 and maximum is 21 \n",
            "#### Worries of concern\n",
            "- Health\n",
            "- Finances\n",
            "- Death\n",
            "- Family\n",
            "- Relationships\n",
            "- Work\n",
            "#### Symptoms \n",
            "- Excessive worry\n",
            "- Restlessness,\n",
            "- Low Concentration\n",
            "- Trouble sleeping\n",
            "- Exhaustion / Fatigablity\n",
            "- Irritability\n",
            "- Sweating\n",
            "- Trembling (Muscle contraction)\n",
            "In the questionnaire the question target these symptoms and worries and summarize them into a score between 0 and 21. \n",
            "### SWL\n",
            "#### Explanation\n",
            "The survey has 5 questions. You fill it in yourself (not a psychiatrist).\n",
            "For each question, you choose any integer between 1 (highly disagree) to 7 (highly agree).\n",
            "In general, lower numbers mean you are less satisfied with life in a certain way.\n",
            "This means you can score between 5 (least satisfied) to 35 (most satisfied).\n",
            "#### Interpretation\n",
            "The (total) SWL score can be interpreted as:\n",
            "\n",
            "- 31 - 35 Extremely satisfied\n",
            "- 26 - 30 Satisfied\n",
            "- 21 - 25 Slightly satisfied\n",
            "- 20 Neutral\n",
            "- 15 - 19 Slightly dissatisfied\n",
            "- 10 - 14 Dissatisfied\n",
            "- 5 - 9 Extremely dissatisfied\n",
            "\n",
            "A more detailed interpretation can be found [here](http://labs.psychology.illinois.edu/~ediener/Documents/Understanding%20SWLS%20Scores.pdf).\n",
            "\n",
            "Residents of developed nations (e.g. DE) usually score 20-24.\n",
            "#### Questions \n",
            "____ In most ways my life is close to my ideal.<br>\n",
            "____ The conditions of my life are excellent.<br>\n",
            "____ I am satisfied with my life.<br>\n",
            "____ So far I have gotten the important things I want in life.<br>\n",
            "____ If I could live my life over, I would change almost nothing.<br>\n",
            "\n",
            "---"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "# Analysis\n",
            "\n",
            "## Preprocessing \n",
            "* Explained new columns and why we did that *\n",
            "\n",
            "Some columns gave the options to write individual responses. Naturally those are not useful in data analysis. In some cases we cleaned the columns and changes the unusual cases to \"Other\"/\"NA\"\n",
            "### Cleaned Columns\n",
            "+ \"Whyplay\" \n",
            "+ Accept \n",
            "## Normalizing the Data \n",
            "\n",
            "### Creating [\"Is_narcissist\"]\n",
            "### Creating [\"Anxiety_score\"]"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 4,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "0        0.202288\n",
                     "1        0.517320\n",
                     "2        0.497993\n",
                     "3        0.272969\n",
                     "4        0.533567\n",
                     "           ...   \n",
                     "13459    0.212092\n",
                     "13460    0.601914\n",
                     "13461    0.125210\n",
                     "13462    0.591783\n",
                     "13463    0.243231\n",
                     "Length: 12838, dtype: float64"
                  ]
               },
               "execution_count": 4,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "# Executing and showing new columns \n",
            "dataset.get_combined_anxiety_score(dataset.get_dataframe())"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Creating [\"Is_competetive\"]"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q1 -  Which gamers are more anxiety prone ? \n",
            "\n",
            "Text .......\n",
            "\n",
            "We compare\n",
            "\n",
            "### Women vs Men \n",
            "\n",
            "Explanation \n",
            "![Example Plot](https://cdn.discordapp.com/attachments/806128836332879924/1127988009627832320/Unbenannt.png)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 5,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'# SIDE BY SIDE PLOTS \\n# LEFT = LINE Graph distribution of Anxiety Score Related to Group\\n# RIGHT = Stacked Bars comparing the GROUP with = \\n# 1.[Work] - 4 Bars\\n# 2.[Degree] - 5 Bars\\n# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\\n'"
                  ]
               },
               "execution_count": 5,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\n",
            "\"\"\"# SIDE BY SIDE PLOTS \n",
            "# LEFT = LINE Graph distribution of Anxiety Score Related to Group\n",
            "# RIGHT = Stacked Bars comparing the GROUP with = \n",
            "# 1.[Work] - 4 Bars\n",
            "# 2.[Degree] - 5 Bars\n",
            "# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\n",
            "\"\"\"\n",
            "#"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Competetive vs Easy Going Players \n",
            "Explanation "
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 6,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'# SIDE BY SIDE PLOTS \\n# LEFT = LINE Graph distribution of Anxiety Score Related to Group\\n# RIGHT = Stacked Bars comparing the GROUP with = \\n# 1.[Work] - 4 Bars\\n# 2.[Degree] - 5 Bars\\n# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")'"
                  ]
               },
               "execution_count": 6,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\"\"\"# SIDE BY SIDE PLOTS \n",
            "# LEFT = LINE Graph distribution of Anxiety Score Related to Group\n",
            "# RIGHT = Stacked Bars comparing the GROUP with = \n",
            "# 1.[Work] - 4 Bars\n",
            "# 2.[Degree] - 5 Bars\n",
            "# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\"\"\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Narcissist vs Non-Narcissist"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 7,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'\"# SIDE BY SIDE PLOTS \\n# LEFT = LINE Graph distribution of Anxiety Score Related to Group\\n# RIGHT = Stacked Bars comparing the GROUP with = \\n# 1.[Work] - 4 Bars\\n# 2.[Degree] - 5 Bars\\n# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")'"
                  ]
               },
               "execution_count": 7,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\"\"\"\"# SIDE BY SIDE PLOTS \n",
            "# LEFT = LINE Graph distribution of Anxiety Score Related to Group\n",
            "# RIGHT = Stacked Bars comparing the GROUP with = \n",
            "# 1.[Work] - 4 Bars\n",
            "# 2.[Degree] - 5 Bars\n",
            "# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\"\"\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q2 - Correlations between played hours and one's well being.\n",
            "**Maybe we can even add if hours watching Streams effect it**\n",
            "\n",
            "For research question two we wanted to know if there is a correlation \n",
            "between played hours and the player's well being. We went into the question\n",
            "with the expectation that players which play longer hours are more anxiety prone\n",
            "and less satisfied with life than those who play less. If that would be the \n",
            "case, a positive correlation of hours played and our combined anxiety score \n",
            "variable would be expected. We want to take a look at the data using a scatter-\n",
            "plot, showing the correlation of both variables of interest, using the\n",
            "plot_scatterplot() function of our Plotter class:\n",
            "code below: plotter.plot_scatterplot(\"Hours\", \"Anxiety_score\")"
         ]
      },
      {
         "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": [
            "plotter.plot_scatterplot(\"Hours\", \"Anxiety_score\") \n",
            "\n",
            "#Still needs to be prettier"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q3 - Effect of the reason for playing on the satisfaction with life "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "In this question, we visualise and discuss how a player's reason for playing and their satisfaction with life score (SWL) effect each other.\n",
            "\n",
            "Although a description of the columns are not given, we briefly describe them as follows:\n",
            "* \"improving\": players are competitive and derive satisfaction from outperforming themselves and others. \n",
            "* \"winning\": players are more competitive than those who wish to improve, and derive immense satisfaction from outperforming. Players who play to win experience games more intensely than those in other categories.\n",
            "* \"having fun\": players are not competitive. They are not particularly invested in improving or the outcome of the game, but instead play as a form of recreation. This does not imply the intensity or difficulty of a game is easy; a challenging game can still be fun as long as players derive satisfaction not from the outcome, but from the gameplay or environs (friends, etc).\n",
            "* \"relaxing\": players are playing to relax, and may play games to reduce their anxiety.\n",
            "* \"all of the above\": players in this category are generally competitive but also see the importance of enjoying the game itself."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 9,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Category distribution:\n",
                  "whyplay\n",
                  "having fun          5105\n",
                  "improving           4661\n",
                  "winning             1977\n",
                  "relaxing             623\n",
                  "other                424\n",
                  "all of the above      48\n",
                  "dtype: int64\n"
               ]
            },
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 800x600 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "\"\"\" Horizontal bar chart, one row for every reason for with top width\n",
            "# Anxiety colored in for the amount of anxiety in that group \n",
            "\"\"\"\n",
            "print(\"Category distribution:\")\n",
            "print(dataframe.groupby(\"whyplay\").size().sort_values(ascending=False))\n",
            "\n",
            "fig, ax = plt.subplots(figsize=(8,6))\n",
            "order = [\"relaxing\", \"having fun\", \"other\", \"improving\", \"winning\"]\n",
            "fig.suptitle(\"\")\n",
            "dataframe[dataframe[\"whyplay\"] != \"other\"].boxplot(column=[\"SWL_T\"], by=\"whyplay\", ax=ax)\n",
            "pass"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "As seen in this plot, we can discover the following:\n",
            "* On average, those who play to have fun are more satisfied with life than any other group.\n",
            "    * We find this outcome reasonable. Those that are more satisfied with life generally do not rely so much on gaming as a means of fulfillment.\n",
            "* As expected, those who play to win are the least satisfied with their lives, as they disproportionately value being the best over enjoying the game.\n",
            "* Interestingly, those who play to relax are also less satisfied with their lives on average. This may be because this category of players are not satisfied with life and use gaming as a means to destress.\n",
            "* Those who selected \"all of the above\" have a much smaller range of SWL metrics. This is due to the small sample size."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Effects of income level (`work`) and education level (`Degree`) on the reason to play"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 10,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'#Overlaying Histogram \\n\\n# Histogram for the income level Y = %, X = low to high \\n# One in Green for the income \\n# One in Red for the Anxiety for those people '"
                  ]
               },
               "execution_count": 10,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\n",
            "\"\"\"#Overlaying Histogram \n",
            "\n",
            "# Histogram for the income level Y = %, X = low to high \n",
            "# One in Green for the income \n",
            "# One in Red for the Anxiety for those people \"\"\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q4 - Gamers from different countries "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "1. Do they play different games ? \n",
            "    1. Are they reacting differently to those games \n",
            "2. Is the amount of educated players similar "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "![Scatter](https://cdn.discordapp.com/attachments/1127973734884581386/1127973829344493679/image.png)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 11,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'#### Analyze the countries amounting to Top 7 or 90% of the survey. \\n\\n\\n#Q4.MAP PLOT = Most played game per country (Dont do it if its League everywhere. )\\n#Q4 MAP PLOT = Heat Map with redder areas for more Anxiety in the country. \\n#Q1.2 Grouped Bar Chart with the top game next to the \"Anxiety Score\"\\n\\n#2 Scatter PLot like in the example '"
                  ]
               },
               "execution_count": 11,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\"\"\"#### Analyze the countries amounting to Top 7 or 90% of the survey. \n",
            "\n",
            "\n",
            "#Q4.MAP PLOT = Most played game per country (Dont do it if its League everywhere. )\n",
            "#Q4 MAP PLOT = Heat Map with redder areas for more Anxiety in the country. \n",
            "#Q1.2 Grouped Bar Chart with the top game next to the \"Anxiety Score\"\n",
            "\n",
            "#2 Scatter PLot like in the example \"\"\""
         ]
      }
   ],
   "metadata": {
      "kernelspec": {
         "display_name": "base",
         "language": "python",
         "name": "python3"
      },
      "language_info": {
         "codemirror_mode": {
            "name": "ipython",
            "version": 3
         },
         "file_extension": ".py",
         "mimetype": "text/x-python",
         "name": "python",
         "nbconvert_exporter": "python",
         "pygments_lexer": "ipython3",
         "version": "3.10.9"
      },
      "orig_nbformat": 4
   },
   "nbformat": 4,
   "nbformat_minor": 2
}