Simulation_Theory_Data_Cleaning_and_Analysis_ipynb.ipynb

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      "name": "Simulation_Theory_Data_Cleaning_and_Analysis.ipynb.ipynb",
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      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Tyler9937/Simulation-Theory-Survey/blob/master/Simulation_Theory_Data_Cleaning_and_Analysis_ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "sJbUL2FRMnwC",
        "colab_type": "text"
      },
      "source": [
        "# The following notebook contains all of the steps taken to create the Simulation Theory Data Storytelling Project\n",
        "\n",
        "These steps include but are not limited to:\n",
        "\n",
        "\n",
        "*   Data Cleaning\n",
        "*   Data Analysis\n",
        "*   Data Exploration\n",
        "*   Data interpretation\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
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              "headers": [
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              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "#importing csv file from local system\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-380e1833-9727-45ae-ae74-937371cf026e\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-380e1833-9727-45ae-ae74-937371cf026e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Are We in a Simulation Experiment.csv to Are We in a Simulation Experiment.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruWEVkujucxU",
        "colab_type": "code",
        "outputId": "7167ede5-edd1-4efa-b26b-6e28fc3a6579",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#importing needed libraies\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "import random\n",
        "\n",
        "#importing raw csv\n",
        "df_test= pd.read_csv('Are We in a Simulation Experiment.csv')\n",
        "\n",
        "#examining raw csv\n",
        "df_test.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>Timestamp</th>\n",
              "      <th>Do you personally believe in Simulation Theory (the idea that we live within a simulation)</th>\n",
              "      <th>What is your gender (if not listed click other and \"fill in the blank\" )</th>\n",
              "      <th>Does the world feel real to you</th>\n",
              "      <th>How old are you</th>\n",
              "      <th>On a scale of 1 to 5 how happy of a person are you (in general)</th>\n",
              "      <th>On a scale of 1 to 5 how confident of a person are you</th>\n",
              "      <th>Scale of 1 to 5 how sociable are you</th>\n",
              "      <th>What is your religion</th>\n",
              "      <th>Do you ever wake up and feel like you are still dreaming</th>\n",
              "      <th>Have you ever experienced \"Déjà Vu\"</th>\n",
              "      <th>How many friends do you have (people whom you talk to every so often)</th>\n",
              "      <th>Are you a fan of marijuana (weed)</th>\n",
              "      <th>Do you own a VR headset</th>\n",
              "      <th>How many books, articles, and videos have you read/ watched on the topic of Simulation Theory</th>\n",
              "      <th>Have you ever watched the movie \"The Matrix\"</th>\n",
              "      <th>If yes to previous question Red pill or Blue pill</th>\n",
              "      <th>Do you know who Nick Bostrom is</th>\n",
              "      <th>Have you ever witnessed an event that could be described as a \"glitch\" in our reality</th>\n",
              "      <th>If yes to the previous question you may describe it below. This is not required.</th>\n",
              "      <th>Are you a fan of Elon Musk</th>\n",
              "      <th>Do you own a Tesla</th>\n",
              "      <th>Would you consider yourself to think more logically or more abstractly</th>\n",
              "      <th>Do you believe in the existence of aliens</th>\n",
              "      <th>Are you familiar with the Fermi Paradox</th>\n",
              "      <th>Does the idea of living within a simulation change your perspective on life</th>\n",
              "      <th>How much time (if any) have you spent on the topic of Simulation Theory(research, conversations, thinking/thought experiments)</th>\n",
              "      <th>Is the concept of Simulation Theory hard for you to grasp</th>\n",
              "      <th>Would you say you are a person of science (believe in facts backed up by repeated experiments and math)</th>\n",
              "      <th>What kind of emotion do you feel toward the idea of living in a simulation with no hope of ever escaping</th>\n",
              "      <th>Where did you first hear about Simulation Theory</th>\n",
              "      <th>Do you believe that you are in control of your life</th>\n",
              "      <th>Select several of the following that interest you most</th>\n",
              "      <th>There are several arguments for the theory that we are living in a simulation. Most of which conceptually almost definitely prove that we are in fact in a simulation. However, the purpose of this survey is to discover other insights. Thank you for taking the time to fill out this survey. Just for fun pick a random number 1-10.</th>\n",
              "      <th>Unnamed: 34</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019/11/12 12:30:09 AM EST</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Non-Religious</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4+</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Red pill (Knowledge)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>na</td>\n",
              "      <td>Yes</td>\n",
              "      <td>I wish...</td>\n",
              "      <td>Logically</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>6 to 12 hours</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Curiosity</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Technology;Art;Math;Science Fiction;Space;Busi...</td>\n",
              "      <td>7</td>\n",
              "      <td>July 16th</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019/11/12 1:06:58 AM EST</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Male</td>\n",
              "      <td>Unsure</td>\n",
              "      <td>21.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Christianity</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>25</td>\n",
              "      <td>Depends on the day</td>\n",
              "      <td>No</td>\n",
              "      <td>4+</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Red pill (Knowledge)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>I wish...</td>\n",
              "      <td>Abstractly</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2 to 6 hours</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Curiosity</td>\n",
              "      <td>Youtube</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sports</td>\n",
              "      <td>69</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019/11/12 1:50:31 AM EST</td>\n",
              "      <td>No</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Non-Religious</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Have not seen \"The Matrix\"</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>I wish...</td>\n",
              "      <td>Logically</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1 to 2 hours</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Curiosity</td>\n",
              "      <td>A Friend</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Technology;Movies;Science Fiction;Space;Video ...</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019/11/12 1:54:40 AM EST</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>Non-Religious</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4+</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Red pill (Knowledge)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Abstractly</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24+ hours</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>I don't think we ultimately can't escape this ...</td>\n",
              "      <td>Other</td>\n",
              "      <td>No</td>\n",
              "      <td>Technology;Art;Math;Pop-Culture</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019/11/12 1:57:08 AM EST</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>56.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Non-Religious</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4+</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Red pill (Knowledge)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>I witnessed another version of myself be murde...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Logically</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24+ hours</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Intrigue</td>\n",
              "      <td>Nick Bossom</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Technology;Movies;Science Fiction;Space;Business</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Timestamp  ... Unnamed: 34\n",
              "0  2019/11/12 12:30:09 AM EST  ...   July 16th\n",
              "1   2019/11/12 1:06:58 AM EST  ...         NaN\n",
              "2   2019/11/12 1:50:31 AM EST  ...         NaN\n",
              "3   2019/11/12 1:54:40 AM EST  ...         NaN\n",
              "4   2019/11/12 1:57:08 AM EST  ...         NaN\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jAV58nHHv2n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "#importing csv with simplified column names\n",
        "#Removing wordy names\n",
        "df= pd.read_csv('Are We in a Simulation Experiment.csv',skiprows=1, names=['timestamp','q1','q2','q3','q4','q5',\n",
        "                                                                'q6','q7','q8','q9','q10','q11','q12','q13','q14',\n",
        "                                                                'q15','q16','q17','q18','q19','q20','q21','q22','q23',\n",
        "                                                                'q24','q25','q26','q27','q28','q29','q30','q31','q32','q33','Unnamed'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-NIztxpMjUM",
        "colab_type": "text"
      },
      "source": [
        "# The following cell houses all of the dataframe cleaning steps needed to clean this dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7z3qUz-NEcN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Beginning the dataframe cleaning process\n",
        "#Below is all of the cleaning needed for the first visualzaton\n",
        "\n",
        "#dropping uneeded columns\n",
        "df = df.drop(columns=['timestamp','Unnamed','q2','q4','q8','q9','q10','q16','q18','q19','q21','q22','q28','q30','q32','q33'])\n",
        "\n",
        "#replacing strings with binary codes\n",
        "df = df.replace({'No':0, 'Yes':1,'Unsure':0,'Depends on the day':1,\n",
        "                 \"I don't dislike him\":1,'I wish...':1,})\n",
        "\n",
        "#cleaning column q14 (knowledge)\n",
        "def int_string(var):\n",
        "  return int(var.strip('+'))\n",
        "\n",
        "df['q14'] = df['q14'].apply(int_string)\n",
        "\n",
        "#cleaning column q26 (knowledge)\n",
        "def change_func(var):\n",
        "  if var == '0 to 10 minutes':\n",
        "    return int(var.replace('0 to 10 minutes','0'))\n",
        "  elif var == '10 to 30 minutes':\n",
        "    return int(var.replace('10 to 30 minutes','1'))\n",
        "  elif var == '30 to 60 minutes':\n",
        "    return int(var.replace('30 to 60 minutes','2'))\n",
        "  elif var == '1 to 2 hours':\n",
        "    return int(var.replace('1 to 2 hours','3'))\n",
        "  elif var == '2 to 6 hours':\n",
        "    return int(var.replace('2 to 6 hours','4'))\n",
        "  elif var == '6 to 12 hours':\n",
        "    return int(var.replace('6 to 12 hours','5'))\n",
        "  elif var == '12 to 24 hours':\n",
        "    return int(var.replace('12 to 24 hours','6'))\n",
        "  elif var == '24+ hours':\n",
        "    return int(var.replace('24+ hours','7'))\n",
        "  else:\n",
        "    pass\n",
        "\n",
        "df['q26'] = df['q26'].apply(change_func)\n",
        "\n",
        "#cleaning column q29 (depression)\n",
        "def q29_func(x):\n",
        "  if x != 'Depression' or x != 'Anxiety' or x != 'Sadness':\n",
        "    return int(x = '0')\n",
        "  elif x == 'Depression':\n",
        "    return int(x.replace('Depression','3'))\n",
        "  elif x == 'Anxiety':\n",
        "    return int(x.replace('Anxiety','2'))\n",
        "  elif x == 'Sadness':\n",
        "    return int(x.replace('Sadness','1'))\n",
        "  else:\n",
        "    pass\n",
        "\n",
        "df['q29'] = df['q29'].apply(q29_func)\n",
        "\n",
        "#cleaning column q5 (Depression)\n",
        "def q5_func(x):\n",
        "  if x == 5:\n",
        "    return int(x = 1)\n",
        "  elif x == 4:\n",
        "    return int(x = 2)\n",
        "  elif x == 3:\n",
        "    return int(x = 3)\n",
        "  elif x == 2:\n",
        "    return int(x = 4)\n",
        "  elif x == 1:\n",
        "    return int(x = 5)\n",
        "  else:\n",
        "    pass\n",
        "\n",
        "df['q5'] = df['q5'].apply(q5_func)\n",
        "\n",
        "#cleaning column q3 (Depression)\n",
        "def q3_func(x):\n",
        "  if x == 1:\n",
        "    return int(x = 0)\n",
        "  else:\n",
        "    return int(x = 1)\n",
        "\n",
        "df['q3'] = df['q3'].apply(q3_func)\n",
        "\n",
        "#cleaning column q11 (social)\n",
        "def q11_func(x):\n",
        "  x = str(x)\n",
        "  return float(x.replace('+',''))\n",
        "\n",
        "df['q11'] = df['q11'].apply(q11_func)\n",
        "\n",
        "#cleaning column q25 (confidence)\n",
        "def q25_func(x):\n",
        "  if x == 1:\n",
        "    return int(x = 0)\n",
        "  else:\n",
        "    return int(x = 1)\n",
        "\n",
        "df['q25'] = df['q25'].apply(q11_func)\n",
        "\n",
        "#cleaning column q27 (confidence)\n",
        "def q27_func(x):\n",
        "  if x == 1:\n",
        "    return int(x = 0)\n",
        "  else:\n",
        "    return int(x = 1)\n",
        "\n",
        "df['q27'] = df['q27'].apply(q11_func)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJ11scXitvCF",
        "colab_type": "code",
        "outputId": "9c993cc2-87e1-4603-ef00-9d1dcf8af246",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Examining the cleaned dataframe\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>q1</th>\n",
              "      <th>q3</th>\n",
              "      <th>q5</th>\n",
              "      <th>q6</th>\n",
              "      <th>q7</th>\n",
              "      <th>q11</th>\n",
              "      <th>q12</th>\n",
              "      <th>q13</th>\n",
              "      <th>q14</th>\n",
              "      <th>q15</th>\n",
              "      <th>q17</th>\n",
              "      <th>q20</th>\n",
              "      <th>q23</th>\n",
              "      <th>q24</th>\n",
              "      <th>q25</th>\n",
              "      <th>q26</th>\n",
              "      <th>q27</th>\n",
              "      <th>q29</th>\n",
              "      <th>q31</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  q1  q3  q5  q6  q7   q11 q12 q13  q14  ... q17 q20 q23 q24  q25  q26  q27  q29  q31\n",
              "0  1   0   2   5   3   8.0   0   1    4  ...   1   1   1   1  1.0    5  0.0    0    1\n",
              "1  1   1   2   2   4  25.0   1   0    4  ...   1   1   1   0  0.0    4  0.0    0    1\n",
              "2  0   0   3   3   2   5.0   1   0    0  ...   0   1   1   1  0.0    3  0.0    0    1\n",
              "3  1   1   3   4   3   1.0   0   0    4  ...   1   1   1   1  1.0    7  0.0    0    0\n",
              "4  1   0   2   4   4   2.0   0   1    4  ...   1   1   1   1  1.0    7  0.0    0    1\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJnJrCS7Eg44",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Creating several new columns to simplify analysis\n",
        "df['knowledge'] = df['q17'] + df['q14'] + df['q26']\n",
        "\n",
        "df['depression'] = df['q3'] + df['q5'] + df['q29']\n",
        "\n",
        "df['social'] = df['q7'] + df['q11']\n",
        "\n",
        "df['confidence'] = df['q6'] + df['q25'] + df['q27'] + df['q31']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7M_aL7SmtJk3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating the needed subsets to answer or main questions\n",
        "st_no = df[df['q1'] == 0]\n",
        "st_yes = df[df['q1'] == 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLxc3eeCxJZx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Taking even samples from each group for equal comparison\n",
        "st_no_sample = st_no[0:50]\n",
        "st_yes_sample = st_yes[0:50]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9anuCnEMMLN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#saving vales that will be graphable\n",
        "knowledge_total_yes = st_yes_sample['knowledge'].sum()\n",
        "knowledge_total_no = st_no_sample['knowledge'].sum()\n",
        "\n",
        "depression_total_yes = st_yes_sample['depression'].sum()\n",
        "depression_total_no = st_no_sample['depression'].sum()\n",
        "\n",
        "social_total_yes = st_yes_sample['social'].sum()\n",
        "social_total_no = st_no_sample['social'].sum()\n",
        "\n",
        "confidence_total_yes = st_yes_sample['confidence'].sum()\n",
        "confidence_total_no = st_no_sample['confidence'].sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LWdFaGRSpUC",
        "colab_type": "code",
        "outputId": "1b3703b7-c3a1-4e5b-f535-a3d66bdd509f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Examining dataset with added clolumns\n",
        "df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>q1</th>\n",
              "      <th>q3</th>\n",
              "      <th>q5</th>\n",
              "      <th>q6</th>\n",
              "      <th>q7</th>\n",
              "      <th>q11</th>\n",
              "      <th>q12</th>\n",
              "      <th>q13</th>\n",
              "      <th>q14</th>\n",
              "      <th>q15</th>\n",
              "      <th>q17</th>\n",
              "      <th>q20</th>\n",
              "      <th>q23</th>\n",
              "      <th>q24</th>\n",
              "      <th>q25</th>\n",
              "      <th>q26</th>\n",
              "      <th>q27</th>\n",
              "      <th>q29</th>\n",
              "      <th>q31</th>\n",
              "      <th>knowledge</th>\n",
              "      <th>depression</th>\n",
              "      <th>social</th>\n",
              "      <th>confidence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  q1  q3  q5  q6  q7   q11  ... q29 q31  knowledge depression social confidence\n",
              "0  1   0   2   5   3   8.0  ...   0   1         10          2   11.0          7\n",
              "1  1   1   2   2   4  25.0  ...   0   1          9          3   29.0          3\n",
              "2  0   0   3   3   2   5.0  ...   0   1          3          3    7.0          4\n",
              "3  1   1   3   4   3   1.0  ...   0   0         12          4    4.0          5\n",
              "4  1   0   2   4   4   2.0  ...   0   1         12          2    6.0          6\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkOkBG4tNEfC",
        "colab_type": "text"
      },
      "source": [
        "# The Following is the ploting of our first visualization  comparing Personalities against people who belived in Simulation Theroy Vs people who did not."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJ1rnE2iI1p-",
        "colab_type": "code",
        "outputId": "b2b266ed-1e73-431e-d85a-713253088a4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        }
      },
      "source": [
        "# data to plot\n",
        "n_groups = 4\n",
        "means_yes = (knowledge_total_yes, depression_total_yes,\n",
        "               social_total_yes, confidence_total_yes)\n",
        "means_no = (knowledge_total_no, depression_total_no,\n",
        "               social_total_no, confidence_total_no)\n",
        "\n",
        "\n",
        "# create plot\n",
        "fig, ax = plt.subplots()\n",
        "index = np.arange(n_groups)\n",
        "bar_width = 0.4\n",
        "opacity = 0.7\n",
        "\n",
        "rects1 = plt.bar(index, means_yes, bar_width,\n",
        "alpha=opacity,\n",
        "color='b',\n",
        "label='Real',\n",
        "zorder =3,)\n",
        "\n",
        "rects2 = plt.bar(index + bar_width, means_no, bar_width,\n",
        "alpha=opacity,\n",
        "color='g',\n",
        "label='Fake',\n",
        "zorder = 3)\n",
        "\n",
        "\n",
        "# Set axes background color\n",
        "ax.set(facecolor='white')\n",
        "\n",
        "ax.axhline(y=1, color='#ABABAB')\n",
        "\n",
        "ax.grid(color='#D7D7D7', linestyle='-', linewidth=2)\n",
        "\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['left'].set_visible(False)\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['bottom'].set_visible(False)\n",
        "\n",
        "# Set subtitle\n",
        "ax.text(s='Does personality affect if you believe in ST', x=-.3, y=750, fontsize=12,fontstyle='italic')\n",
        "\n",
        "\n",
        "plt.xlabel('Personality Categories', fontsize=12, fontweight='bold')\n",
        "plt.ylabel('Points Earned', fontsize=12, fontweight='bold')\n",
        "plt.title(\"Personality Vs. Simulation Theory Beliefs\", fontsize=12, fontweight='bold', x=.35, y=1.1)\n",
        "plt.xticks(index + bar_width, ('Knowledge', 'Depression', 'Social', 'Confidence'))\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ffvsttWrVio4dO1Zs/R0dHcnKyorMzMxoxowZtHTpUvL09CQAFBUVJeR7/vw5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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmJij6xk3ILi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating confidence intervales function\n",
        "def confidence_interval(data, confidence=0.95):\n",
        "  data = np.array(data)\n",
        "  mean = np.mean(data)\n",
        "  n = len(data)\n",
        "  stderr = np.std(data, ddof=1)/np.sqrt(n)\n",
        "  moe = stderr * stats.t.ppf((1 + confidence) / 2.0, n-1)\n",
        "  return (mean - moe,mean,mean + moe)\n",
        "\n",
        "#creating new vars from CI\n",
        "ci_q12 = confidence_interval(st_yes['q12'])\n",
        "ci_q13 = confidence_interval(st_yes['q13'])\n",
        "ci_q15 = confidence_interval(st_yes['q15'])\n",
        "ci_q17 = confidence_interval(st_yes['q17'])\n",
        "ci_q20 = confidence_interval(st_yes['q20'])\n",
        "ci_q24 = confidence_interval(st_yes['q24'])\n",
        "ci_q23 = confidence_interval(st_yes['q23'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouyndMj9LxJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#saving the means into variables\n",
        "a = st_yes['q12'].mean()\n",
        "b = st_yes['q13'].mean()\n",
        "c = st_yes['q15'].mean()\n",
        "d = st_yes['q17'].mean()\n",
        "e = st_yes['q20'].mean()\n",
        "f = st_yes['q24'].mean()\n",
        "g = st_yes['q23'].mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FP7JLnupNW-y",
        "colab_type": "text"
      },
      "source": [
        "# The Following is the ploting of our second visualization comparing percents of people that said yes to certain question in the survey who belived in Simulation Theory."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gt8wnasF7hDB",
        "colab_type": "code",
        "outputId": "a4a3e3d1-b636-4c33-e74a-a12e3191a4d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        }
      },
      "source": [
        "# data to plot\n",
        "\n",
        "#Creating a series of the object means\n",
        "means = pd.Series([a,b,c,d,e,f,g], \n",
        "                  index=range(1,8))\n",
        "\n",
        "#Creating the plotable confidence intervales\n",
        "ci = [(ci_q12[1] - ci_q12[2]), (ci_q13[1] - ci_q13[2]), (ci_q15[1] - ci_q15[2]),\n",
        "      (ci_q17[1] - ci_q17[2]),(ci_q20[1] - ci_q20[2]),(ci_q23[1] - ci_q23[2]),(ci_q24[1] - ci_q24[2])]\n",
        "\n",
        "#Creating the object names\n",
        "objects = ('Fan of \\nWeed', 'Owns\\nVR','Seen\\nMatrix', 'Knows of\\nNick B.','Fan of\\nElon M.','Knows of\\nFermi-\\nParadox','Believes\\n in Aliens')\n",
        "\n",
        "#saving the index distance as a var\n",
        "index = np.arange(len(objects))\n",
        "\n",
        "#creating needed vars\n",
        "bar_width = .8\n",
        "opacity = .5\n",
        "\n",
        "#Creating subplots\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "#creating main bar plots\n",
        "react1 =plt.bar(index + bar_width, means, bar_width,\n",
        "alpha=opacity,\n",
        "color='b',\n",
        "label='ST is Fake',\n",
        "zorder = 3,\n",
        "align = 'center',\n",
        "yerr=ci)\n",
        "\n",
        "# Set subtitle\n",
        "ax.text(s='Is there correlation between ST and these categories', x=.2, y=1.08, fontsize=12,fontstyle='italic')\n",
        "\n",
        "#Set tick marks and labels\n",
        "plt.xticks(index + bar_width, objects)\n",
        "plt.xlabel('Categories', fontsize=12, fontweight='bold')\n",
        "plt.ylabel('Percentage of People', fontsize=12, fontweight='bold')\n",
        "plt.title(\"People Who Believe in ST Vs Select Categories\", fontsize=12, fontweight='bold', x=.35, y=1.1)\n",
        "\n",
        "\n",
        "# Set axes background color\n",
        "ax.set(facecolor='white')\n",
        "\n",
        "#creating a grid\n",
        "ax.grid(color='#D7D7D7', linestyle='-', linewidth=2)\n",
        "\n",
        "#removes grid borders\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['left'].set_visible(False)\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['bottom'].set_visible(False)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ru3btos8++0xyz6riowi8lBU1sFM+Szpw4EBKTU0lExMT0SbKl0pTUlLIxcVFsjzFPbM/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\nRKSPUGInIiIi0kcosRMRERHpI/4PrSj1ACRjK1IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEjSkBk6LlAR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}