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": 2,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "<src.Dataset.Dataset object at 0x000001CA09E08280>\n",
                  "<class 'src.Dataset.Dataset'>\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": 3,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Gender\n",
                  "Female      688\n",
                  "Male      12108\n",
                  "Other        42\n",
                  "dtype: int64 Index(['Female', 'Male', 'Other'], dtype='object', name='Gender') [  688 12108    42]\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(\"Gender\", styling_params={\"title\": \"Gender Distributution\"})"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Where is the data from ?  \n",
            "### Where did the participants found the survey ?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 4,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Reference\n",
                  "CrowdFlower           2\n",
                  "Other                57\n",
                  "Reddit            12715\n",
                  "TeamLiquid.net       51\n",
                  "dtype: int64 Index(['CrowdFlower', 'Other', 'Reddit', 'TeamLiquid.net'], dtype='object', name='Reference') [    2    57 12715    51]\n"
               ]
            },
            {
               "data": {
                  "image/png": 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cuHEaPXq0hg4dqmbNmumqq65SZGSk8/rDTz75RIWFhXrvvffUokUL9erVS88++6zL/mbMmKEmTZpo0qRJat68ue69915FRkaetoedO3eqUaNGatu2rS655BL5+fmdr8MFAADlCGGxjJg8ebIeeeQRfffddxo8eLAiIyO1adMmrVixQpGRkUpOTpZ04ivs2267TS1btlRCQoJeffVVPffccy772rNnj/r376/bbrtNv/76q4YMGaIxY8ac9v2//PJLffvtt4qLi1NaWpruvffe83asAACg/PCSZDzdBMovh8OhI0eO6J3En5VfeNzT7QAAUKE807rLednvyc/vwMBAZWZmnnYsZxYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgNU5hcUmTZqoR48eLr9NBAAAABWHW2GxVq1aWrp0qX7//XfFxsaqbt26kqTZs2dr4sSJpdogAAAAPMetsBgTE6Njx44pODhYOTk5zvXz589Xz549S605AAAAeJavOy/q0aOHbrnlFqWkpLis/+OPPxQSElIqjQEAAMDz3DqzWLVqVZcziifVrl1beXl559wUAAAAyga3wuLKlSv14IMPOpeNMfLy8tKIESMUFxdXas0BAADAs9z6GnrEiBFavny52rdvLz8/P73xxhtq1aqVatWqpeuuu660ewQAAICHuHVmcevWrWrTpo3WrVunpUuXqmrVqlqwYIGuvvpq7dixo7R7BAAAgIe4dWZRklJTUzVu3LhSbAUAAABljVtnFiMjI3XXXXcVWX/XXXe5XMsIAACA8s2tsDhq1CilpaUVWb9//36NGTPmnJsCAABA2eBWWAwJCVFycnKR9bt27VJwcPA5NwUAAICywa2wuH//frVp06bI+rZt2+rgwYPn3BQAAADKBrfC4qeffqqpU6eqe/fu8vb2lre3t2644QZNmTJFn376aWn3CAAAAA9x627oF154QSEhIVq2bJmOHTsmSfL29tbcuXO5ZhEAAKACcSssFhQUaODAgRo7dqzatm2r3Nxcbdq0Sbt37y7t/gAAAOBBbj9nUZL++OMP/fHHH6XVCwAAAMoYt8Kit7e3IiMjFR4erssuu0ze3q6XPoaHh5dKcwAAAPAst8LilClTFBkZqW+++UabN2+WMaa0+wIAAEAZ4FZYHDhwoAYMGKAlS5aUdj8AAAAoQ9x6dE5+fr62b99e2r0AAACgjHErLE6aNEnDhg0r7V4AAABQxnhJOusLDhcsWKAbbrhBhw4d0pYtW1RQUOCyvX///qXVH8o4h8OhI0eOKDAwUJmZmZ5uBwAAlMDZfH67dc1iRkaGFi5c6FZzAAAAKD/cCosPPfRQafcBAACAMsitaxYlycfHR+Hh4Xr00UdVrVo1SVLdunVVtWrVUmsOAAAAnuXWmcXg4GB9++23Cg4OVuXKlbV06VJlZWVp5MiR8vf3V1RUVGn3CQAAAA9w68zilClTFB8fr5o1ayo3N9e5fuHChfz2FgAAgArErTOLXbt21XXXXVfkLuhdu3apfv36pdIYAAAAPM+tM4ve3t7y8fEpsr5BgwY8PgUAAKACcSssLl26VE8//bRz2RijqlWr6qWXXlJsbGxp9QYAAAAPc+uh3PXq1dMPP/yg48ePq1mzZoqPj1ezZs2Ulpambt266cCBA+ehVZRFPJQbAIDy52w+v90Ki5Lk7++vgQMHKjQ0VN7e3lq/fr0+/vhjHT161J3doZwiLAIAUP6c17Do6+urbdu2qU+fPtq6deu59IkKgLAIAED5czaf32d9zeKxY8dUuXJlGePWCUkAAACUI27d4PL222/rueeeK/aOaAAAAFQcbj1nsVOnTgoPD1ePHj20adMmZWdnu2zv379/qTQHAAAAz3IrLGZkZOjLL78s7V4AAABQxrgVFh966KHS7gMAAABlkFvXLEqSj4+PwsPD9eijj6patWqSpLp166pq1aql1hwAAAA8y62wGBwcrE2bNmnRokWaNm2aLr30UknSyJEjNXHixFJtEOXD+LXLPN0CAAA4D9wKi1OmTFF8fLxq1qyp3Nxc5/qFCxcqPDy81JoDAACAZ7l1zWLXrl113XXXqaCgwGX9rl27VL9+/VJpDAAAAJ7n1plFb2/vYp+x2KBBA36LBwAAQAXiVlhcunSpnn76aeeyMUZVq1bVSy+9pNjY2NLqDQAAAB7m1tfQw4cPV1xcnLZs2SJ/f3998sknatasmdLS0nTvvfeWdo8AAADwELfC4r59+9SuXTsNHDhQoaGh8vb21nvvvaePP/5YR48eLe0eAQAA4EGmJPXLL7+YGjVqGElm7NixpkqVKiV6HVWxy+FwGGOMeXvLOo/3QlEURVFUyerk57fD4Tjj2BJfs9iiRQvnA7ejo6OdD+IGAABAxVXir6E3bNigOXPmaNWqVfLy8tKzzz6rrKysYse+8sorpdYgAAAAPKfEYTEyMlIvvfSS+vTpI2OMevXqpWPHjhUZZ4whLAIAAFQQJQ6Lv//+u/NO5+PHjys8PFwHDhw4b40BAADA89y6G7q4B3IDAACg4nHrodyS9MADD2jVqlVKSUlRcHCwJOnpp5/W7bffXmrNAQAAwLPcCotDhgzR5MmTFRsbqxo1ajjPNKanp7v8ZhcAAACUb26FxaeeekqPPPKIxo8fr+PHjzvXx8fHq3Xr1qXWHAAAADzLrbDYqFEjJSQkFFmfl5fnfBYjAAAAyj+3wmJycrLatWtXZH2vXr20devWc+0JAAAAZYRbd0O/+eabmjZtmvz9/eXl5aWOHTvq3nvv1ZgxY/Twww+Xdo8AAADwELfC4gcffCBfX1+98cYbCggI0CeffKKUlBQ99dRT+vHHH0u7RwAAAHiI24/OmT17tho2bKjLLrtMQUFB6tixo66++mpt3769NPsDAACAB51VWKxevbrmzZun/fv3O88kHjp0SE888YS2b9+uzp0766GHHjpfvQIAAOACO6uvocePH69u3brpww8/VM+ePRUTE6OePXvK399fvXv31sqVK89XnwAAAPCAswqLt956qwYNGqRly5Zp+vTp2r59u37//XcNHz78fPUHAAAADzqrr6Hr1aunxMRESScen3P06FHNnj37vDQGAAAAzzursOjt7a2CggLn8vHjx5WdnV3qTQEAAKBsOKuvob28vPTBBx8oLy9PkuTv768ZM2YUCYz9+/cvvQ4BAADgMWcVFj/88EOX5Xnz5pVqMwAAAChbzios8lgcAACAi4vbD+UGAABAxUdYBAAAgBVhsQKIiIhQenq6p9sAAAAVEGGxDGnQoIFmz56tlJQU5eXlaefOnXrrrbdUq1Yt55jk5GQNGzbMg10CAICLCWGxjGjUqJHi4+PVvHlz3XvvvWratKmGDBmi8PBwrVmzRjVr1rzgPfn6ntX9TwAAoIIylOcrNjbW7N692/j7+7usr1OnjsnKyjLTp083cXFx5lSSTEREhElPTzc9evQwiYmJJjMz0yxZssQEBQW57CsyMtIkJiaa3Nxcs3XrVhMVFeXcFhISYowx5u677zZxcXEmNzfXREZGFunTz8/POBwOZ9WrV88YY8zbW9Z5fA4piqIoiipZORwOY4wxDoejJOM93/DFXjVr1jTHjx83o0aNKnb7zJkzzcGDB02tWrXM7t27zQsvvGDq1Klj6tSpY6QTYTEvL898//33JjQ01Fx99dVmy5YtZt68ec59DB482KSkpJg77rjDNGzY0Nxxxx0mLS3NPPjgg0b6v7C4Y8cO55i6desW6SU6OrpIYCUsUhRFUVT5KsJiOauOHTsaY4zp27dvsduffvppY4wxl156qUlOTjbDhg1z2R4REWGMMaZx48bOdVFRUWbfvn3O5V27dpmBAwe6vO755583q1evNtL/hcWhQ4eetlfOLFIURVFU+a+zCYtclFYOeHl5SZJOfOtcvOzsbO3YscO5vG/fPl122WWSpNq1ays4OFjvvfeeZs2a5Rzj6+urw4cPu+wnPj7+tL3k5+crPz//rI8BAACUT4TFMmD79u0qLCxUy5YttWjRoiLbr7zySh06dEhpaWnWfRQUFLgsG2Pk7X3i/qWT/33kkUf0008/uYw7fvy4y/Kpv+cbAABc3Lgbugw4dOiQli5dqscff1z+/v4u2+rUqaP7779f8+fPl3TizJ6Pj89Z7X///v36888/1bhxYyUlJbnUzp07S+swAABABURYLCOefPJJVa5cWd99952uv/56NWjQQLfccouWLl2qlJQUPf/885KknTt3qlu3bqpXr54uueSSEu9/3LhxGj16tIYOHapmzZrpqquuUmRkpIYPH36+DgkAAFQAhMUyYvv27Wrfvr2SkpI0f/58JSUl6f/9v/+nuLg4denSxfkbWl588UU1bNhQSUlJp/1a+lTvvfeeBg8erMjISG3atEkrVqxQZGSkkpOTz9chAQCACsBLJ+50AdzicDh05MgRvZP4s55q1dHT7QAAgBI4+fkdGBiozMzM047lzCIAAACsCIsAAACwIiwCAADAirAIAAAAK8IiAAAArAiLAAAAsCIsAgAAwIqwCAAAACvCIgAAAKwIiwAAALAiLAIAAMCKsAgAAAArwiIAAACsCIsAAACwIiwCAADAirAIAAAAK8IiAAAArAiLAAAAsCIsAgAAwIqwCAAAACvCIgAAAKwIiwAAALAiLAIAAMCKsAgAAAArwiIAAACsCIsAAACwIiwCAADAirAIAAAAK8IiSsWYzuGebgEAAJwHhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYfE8mTNnjhYuXFji8XFxcYqJiTmPHQEAAJy9ch8W69Spo6lTpyopKUlHjx7V7t27tXjxYt14442ebs3FnDlzZIwpUk2aNPF0awAAAFa+nm7gXISEhGj16tXKyMjQyJEjtXHjRlWqVEm33HKLpk2bphYtWhR5ja+vr44dO+aBbqUlS5Zo0KBBLusOHDjgkV7OxJPzBAAAyo5yfWZx+vTpMsaoY8eO+vLLL/XHH38oMTFRMTEx6ty5syTJGKPHHntM//nPf5SVlaUXXnhBkjRkyBBt375deXl5+u233/TAAw849ztx4kQtXrzYuTxs2DAZY9S7d2/nut9++02PPvqoJMnb21uTJk1Senq60tLS9Prrr8vLy6tIv3l5eUpNTXWpwsLCYo+tRo0a+vDDD3Xo0CFlZ2crNjZWTZs2dW7fv3+/7rzzTudyQkKCUlNTncudO3dWfn6+qlatKkkKDAzUzJkzlZqaqsOHD2vZsmVq06aNc3x0dLQSEhI0aNAgJSUlKS8vrwT/CwAAgIqu3IbFmjVrqmfPnpo2bZpycnKKbD98+LDzzy+99JIWLVqk1q1b6/3331e/fv00ZcoUTZo0SVdddZVmzpypOXPmqHv37pKk5cuX6/rrr3cGvrCwMB04cEBhYWGSTnz1fcUVV2jFihWSpGeeeUYPPfSQHn74YXXt2lW1atXSHXfccU7H98EHH6h9+/a6/fbb1aVLF3l5eSk2Nla+vidOBq9cudLZb40aNdSyZUtVqlTJeTa1e/fu+uWXX5SdnS1J+uabbxQUFKTevXsrNDRU69ev17Jly1SzZk3nezZt2lQDBgxQ//791a5du2L78vPzk8PhcCkAAFCxmfJYHTp0MMYY069fv9OOM8aYyZMnu6xbtWqVmTlzpsu6+fPnm6+//tpIMoGBgebYsWPmmmuuMZLMgQMHzHPPPWd++uknI8kMHDjQ7Nu3z/nalJQUM3LkSOeyj4+P2b17t1m4cKFz3Zw5c0xBQYHJzMx01meffebcHhcXZ2JiYowk07RpU2OMMV26dHFur1WrlsnOzjZ33XWXkWSefPJJs3HjRiPJ3H777WbdunXmiy++MFFRUUaS+fbbb81rr71mJJkbbrjBZGRkGD8/P5dj/uOPP8wjjzxiJJno6GiTl5dnateufdr5jI6ONsVxOBwe/5mgKIqiKKpk5XA4Svz5XW7PLJ4862eMOePY+Ph4l+UWLVpo9erVLutWr17tPCt35MgRbdiwQd27d1fr1q1VWFiomTNnqm3btqpWrZq6d+/uPKsYGBioevXqac2aNc59HT9+vMh7SifueG7Xrp2zhg4dWmy/LVq0UEFBgX766SfnukOHDmnbtm3OHpcvX65WrVrpkksuUVhYmJYvX67ly5crLCxMPj4+uvbaa509hoaGqlq1ajp48KAyMzOd1ahRI5cbbHbt2qW0tLTTzuVrr72mwMBAZ9WvX/+04wEAQPlWbm9w+eOPP1RYWKgWLVpo0aJFpx178qvYvzs1ZHp5ebmsW758ubp37678/HytWLFCGRkZ2rJli6677jp1795db7311ln3nJ2draSkpDOOK+56x1N73Lx5sw4ePKiwsDCFhYXpxRdf1J49e/T888+rQ4cOqlKlilatWiXpxDWV+/btc35t/XcZGRku/Z1Jfn6+8vPzzzgOAABUDOX2zGJ6erq+++47PfHEEwoICCiyvXr16tbXbt26VV27dnVZd+2112rr1q3O5ZPXLd54441avny5JGnFihUaOHCgy/WKR44c0d69e5031EiSj4+PQkND3T62xMREVapUSZ06dXKuq1Wrlpo3b+7S48qVK9W3b19dddVV+vHHH7Vp0yZVqlRJQ4YM0fr165WVlSVJWr9+vYKCgnTs2DElJSW51MGDB93uEwAAXBw8/r25u9WwYUOzd+9es3nzZnPnnXeapk2bmiuvvNI89dRTJjEx0Ugnrlns27evy+v69u1r8vLyzGOPPWaaNm1qhg8fbgoKCkxYWJhzzMnrFgsKCkzLli2NdOLawIKCApOamuqyv5EjR5qDBw+afv36mSuuuMLMnDnTHD58uMg1i39fPrX+fs2iJLNw4UKzefNmc91115k2bdqY2NhY8/vvvxtfX1/nmCeffNIUFBSYdevWOdctWLDAFBQUmNdff91l/ytXrjQJCQmmR48eJiQkxHTp0sW88sorJjQ01EgnrkVMSEg4r9c8UBRFURRVNuqiuGZRknbu3KlrrrlGcXFxmjRpkjZv3qylS5cqPDxcUVFR1tctWrRIw4YN04gRI7RlyxY99thjGjRokPNsoXTijGFCQoIOHTqkxMRESdKPP/4ob29vl3GSNGnSJM2dO1cffPCB1qxZo8zMzLP67S3FGTRokH755Rd9/fXXWrNmjby8vNS7d2+XZx/GxcXJ19fXeeZTOnH209fXt0iPvXv31sqVK/X+++/r999/16effqqGDRu6PG4HAADgVF46kRoBtzgcDh05ckSBgYHKzMz0dDsAAKAEzubzu1yfWQQAAMD5RVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQAAAFaERQAAAFj5eroBVAwOh8PTLQAAgBI6m89twiLOSa1atSRJKSkpHu4EAACcLYfDoczMzNOOISzinBw6dEiSVL9+/TP+sF3sHA6HUlJSmKszYJ5KhnkqGeap5JirkqlI8+RwOLR3794zjiMsolRkZmaW+//TXCjMVckwTyXDPJUM81RyzFXJVIR5Kmn/3OACAAAAK8IiAAAArAiLOCd5eXkaN26c8vLyPN1KmcdclQzzVDLMU8kwTyXHXJXMxThPXpKMp5sAAABA2cSZRQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEWck6ioKO3YsUO5ubmKj49X165dPd3SeTNq1CitW7dOR44cUWpqqhYuXKjmzZsXGRcdHa2UlBTl5OQoLi5OLVu2dNnu5+enqVOn6sCBA8rKytKiRYtUv359lzE1atTQ3LlzlZGRoYyMDM2dO1fVq1c/r8d3vowaNUrGGMXExLisZ56kevXq6aOPPlJaWpqys7OVkJCga665xmUM8yT5+PjolVde0Y4dO5STk6OkpCSNHTtWXl5eLuMutrm6/vrrtXjxYqWkpMgYo759+xYZc6Hm5PLLL9fixYuVlZWlAwcOaMqUKapUqVLpH7QbTjdPvr6+mjBhgjZu3KisrCylpKToww8/VN26dV32cTHM05kYinKnBgwYYPLy8szDDz9srrzyShMTE2MyMzPN5Zdf7vHezkctWbLEREREmJYtW5o2bdqYr776yuzcudMEBAQ4x4wcOdIcPnzY3HHHHaZVq1bm3//+t0lJSTHVqlVzjpk+fbrZs2ePCQ8PN+3atTPLli0zCQkJxtvb2zkmNjbWbNy40XTu3Nl07tzZbNy40SxevNjjc3C21b59e7Njxw6zYcMGExMTwzz9rWrUqGGSk5PN+++/bzp06GBCQkLMjTfeaBo3bsw8nVJjxowxBw4cML179zYhISGmf//+5siRI2bo0KEX9Vz17NnTvPLKK+aOO+4wxhjTt29fl+0Xak68vb3Nxo0bzbJly0y7du1MeHi4+fPPP83UqVM9PkdnmqfAwEDz/fffm7vvvts0b97cdOrUyaxZs8b8/PPPLvu4GObpDOXxBqhyWmvXrjXTp093WZeYmGjGjx/v8d4uRNWuXdsYY8z111/vXLd3714zcuRI57Kfn59JT083jz76qJFO/MWUl5dnBgwY4BxTt25dc+zYMdOjRw8jyVx55ZXGGGM6duzoHNOpUydjjDHNmzf3+HGXtKpWrWq2bdtmwsPDTVxcnEtYZJ5kXnvtNbNy5crTjmGeTtRXX31lZs+e7bLuiy++MHPnzmWu/lfFhcULNSc9e/Y0x44dM3Xr1nWOueeee0xubq5xOBwen5szzdOp1b59e2OMcZ74uBjn6dTia2i4pVKlSgoNDdX333/vsv7777/Xtdde66GuLqyTXy8cOnRIktSoUSPVrVvXZU7y8/O1YsUK55yEhobKz8/PZcy+ffu0efNm55guXbooIyND69atc4756aeflJGRUa7mdtq0afrmm2+0bNkyl/XM0wm333674uPj9dlnnyk1NVXr16/X4MGDnduZp/+zatUqhYeHq1mzZpKkNm3aqGvXroqNjZXEXBXnQs5Jly5dtHnzZu3bt8855rvvvpO/v79CQ0PP63GeD9WrV1dhYaEyMjIkMU+S5OvpBlA+1a5dW76+vkpNTXVZn5qaqqCgIA91dWFNnjxZP/74o7Zs2SJJzuMubk5CQkKcY/Ly8px/Cf19zMnXBwUFaf/+/UXeb//+/eVmbu+55x5dc8016tChQ5FtzNMJjRs3VlRUlCZPnqzx48erY8eOmjp1qvLy8vTRRx8xT3/z+uuvq3r16vrtt990/Phx+fj46Pnnn9enn34qiZ+p4lzIOQkKCiryPhkZGcrLyyt381a5cmVNmDBBn3zyiTIzMyUxTxJhEefIGOOy7OXlVWRdRfTOO+84z26cyp05OXVMcePLy9w2aNBAU6ZMUY8ePU7767Au9nny9vZWfHy8nn/+eUnShg0b1KpVK0VFRemjjz5yjrvY50k68Y+PBx54QPfdd5+2bNmidu3a6a233tLevXs1d+5c5zjmqqgLNScVYd58fX316aefytvbW48//vgZx19M88TX0HBLWlqajh07VuRfQ5dddlmRfzlVNFOnTtXtt9+uG264QSkpKc71f/31lySddk7++usvVa5cWTVq1DjtmDp16hR530svvbRczG1oaKjq1KmjX375RQUFBSooKFD37t01dOhQFRQUOI/hYp+nffv2KTEx0WXd1q1bFRwcLImfp7978803NWHCBM2fP1+bN2/WvHnzFBMTo9GjR0tiropzIefkr7/+KvI+NWrUkJ+fX7mZN19fX3322Wdq1KiRbr75ZudZRYl5OsnjF05S5bPWrl1rpk2b5rJuy5YtFfoGl7ffftv8+eefpmnTpsVu37t3rxkxYoRzuVKlSsVeUH733Xc7xwQFBRV7oXSHDh2cYzp27FguLrKXZKpVq2ZatWrlUuvWrTNz5841rVq1Yp7+Vx9//HGRG1wmT55sVq9ezc/TKZWWlmaGDBnism7UqFFm27ZtzNX/ynaDy4WYk5M3bgQFBTnHDBgwoEzeuFHcPPn6+poFCxaYTZs2mdq1axd5zcU4T8WUxxugymmdfHTOoEGDzJVXXmkmT55sMjMzTXBwsMd7Ox81bdo0k56ebrp162bq1KnjLH9/f+eYkSNHmvT0dNOvXz/TqlUr8/HHHxf7qIrdu3ebG2+80bRr187897//LfYRDBs2bDCdOnUynTp1Mr/++muZfXxHSerUu6GZpxN3XObn55vRo0ebJk2amHvvvddkZWWZ++67j3k6pebMmWP27NnjfHROv379zP79+82ECRMu6rmqWrWqadu2rWnbtq0xxpinn37atG3b1nkX74Wak5OPhFm6dKlp166dufHGG83u3bvLzCNhTjdPPj4+5j//+Y/ZvXu3adOmjcvf7ZUqVbqo5ukM5fEGqHJcUVFRJjk52Rw9etTEx8e7PEamopVNRESEy7jo6Gizd+9ek5uba5YvX+48m3ayKleubKZOnWrS0tJMdna2Wbx4sWnQoIHLmJo1a5qPPvrIHD582Bw+fNh89NFHpnr16h6fA3fr1LDIPJ2oW2+91WzcuNHk5uaaxMREM3jw4CJjmKcTZ6tjYmLMzp07TU5Ojtm+fbt55ZVXXD7ML8a5CgsLK/bvpDlz5lzwObn88svNV199ZbKzs01aWpqZOnWq8fPz8/gcnWmeQkJCrH+3h4WFXVTzdLry+t8fAAAAgCK4wQUAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYEVYBAAAgBVhEQDgZIxR3759Pd0GgDKEsAgAF5FLL71UM2bM0K5du3T06FHt27dP3377rTp37ixJCgoK0pIlSzzcJYCyxNfTDQAALpwvv/xSlSpVUkREhHbs2KE6deooPDxctWrVkiSlpqZ6uEMAZZHHf0E1RVEUdf6revXqxhhjunXrZh1jjDF9+/Y1kkx0dLQpTkREhHP8iBEjTFJSksnJyTEbNmww/fv39/hxUhRVusXX0ABwkcjKylJmZqb69esnPz+/M46fOHGigoKCnPXMM88oOztb8fHxkqR//etfGjRokKKiotSqVSvFxMRo3rx56tat2/k+FAAXmMcTK0VRFHVh6s477zQHDx40OTk5ZtWqVebVV181rVu3dm7/+5nFv1enTp1MTk6Oufvuu40kExAQYHJyckznzp1dxs2aNct8/PHHHj9OiqJKtTzeAEVRFHUBq3Llyuamm24yY8eONatXrzYFBQXOr5aLC4uXX3652bdvn3n55Zed69q3b2+MMSYzM9Ol8vLyzNq1az1+jBRFlV55/e8PAICL1KxZs3TzzTerYcOGMsaoX79+WrRokSQpICBAq1evVnJysu68807nazp27KiffvpJYWFhSklJcdlfXl6e/vzzzwt6DADOH+6GBoCLXGJiovr161fstnnz5snb21v/+Mc/irzm6NGjCg4O1sqVKy9AlwA8hbAIABeJWrVq6fPPP9f777+vjRs3KjMzU+3bt9fIkSOdZxL/bty4cbrpppvUo0cPVatWTdWqVZMkHT58WFlZWZo4caJiYmLk7e2tVatWKTAwUNdee62ysrI0d+7cC314AM4jj38XTlEURZ3/8vPzM+PHjzfx8fEmPT3dZGVlma1bt5qXX37Z+Pv7G8n1msW4uLgzPjrnqaeeMlu3bjV5eXkmNTXVLFmyxFx//fUeP1aKokqvuGYRAAAAVjxnEQAAAFaERQAAAFgRFgEAAGBFWAQAAIAVYREAAABWhEUAAABYERYBAABgRVgEAACAFWERAAAAVoRFAAAAWBEWAQAAYPX/AcBgFm3biFWGAAAAAElFTkSuQmCC",
                  "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": 5,
         "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": 5,
               "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": 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\")\\n'"
                  ]
               },
               "execution_count": 6,
               "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": 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": [
            "### Narcissist vs Non-Narcissist"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 8,
         "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": 8,
               "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": 9,
         "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\", styling_params={\"title\": \"Beispiel\" }) \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": 10,
         "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": [
            "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": 11,
         "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": 11,
               "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": 12,
         "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": 12,
               "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
}