Results_visualizer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Results_visualizer.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "JECHLE3B_xBn"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQbmKX87RiiX"
      },
      "source": [
        "metadata_dir = './uplifting.csv'\n",
        "metadata = pd.read_csv(metadata_dir)\n",
        "\n",
        "metadata_dir1 = './sad.csv'\n",
        "metadata1 = pd.read_csv(metadata_dir1)\n",
        "\n",
        "metadata_dir2 = './neutral.csv'\n",
        "metadata2 = pd.read_csv(metadata_dir2)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJ7A9nkGQCNo"
      },
      "source": [
        "#Custom Questionnaire Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYt1AgBO_xBr"
      },
      "source": [
        "# This is for our custom built questionnaire (16 questions)\n",
        "\n",
        "# Uplifting\n",
        "final_list = []\n",
        "for i in range(18,33):\n",
        "    txt = metadata.columns[i]\n",
        "    ax1 = metadata[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        if ax1[ii] == 'Definitely Not':\n",
        "            score+=-4\n",
        "        elif ax1[ii] == 'Probably Not':\n",
        "            score+=-2\n",
        "        elif ax1[ii] == 'Possibly':\n",
        "            score+=0\n",
        "        elif ax1[ii] == 'Probably':\n",
        "            score+=2\n",
        "        elif ax1[ii] == 'Definitely':\n",
        "            score+=4\n",
        "    final_list.append(score)\n",
        "\n",
        "    \n",
        "# Neutral\n",
        "final_list2 = []\n",
        "for i in range(18,33):\n",
        "    txt = metadata2.columns[i]\n",
        "    ax1 = metadata2[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        if ax1[ii] == 'Definitely Not':\n",
        "            score+=-4\n",
        "        elif ax1[ii] == 'Probably Not':\n",
        "            score+=-2\n",
        "        elif ax1[ii] == 'Possibly':\n",
        "            score+=0\n",
        "        elif ax1[ii] == 'Probably':\n",
        "            score+=2\n",
        "        elif ax1[ii] == 'Definitely':\n",
        "            score+=4\n",
        "    final_list2.append(score)\n",
        "    \n",
        "    \n",
        "# Sad    \n",
        "final_list1 = []\n",
        "for i in range(18,33):\n",
        "    txt = metadata1.columns[i]\n",
        "    ax1 = metadata1[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        if ax1[ii] == 'Definitely Not':\n",
        "            score+=-4\n",
        "        elif ax1[ii] == 'Probably Not':\n",
        "            score+=-2\n",
        "        elif ax1[ii] == 'Possibly':\n",
        "            score+=0\n",
        "        elif ax1[ii] == 'Probably':\n",
        "            score+=2\n",
        "        elif ax1[ii] == 'Definitely':\n",
        "            score+=4\n",
        "    final_list1.append(score)\n",
        "    \n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQVBr1-Q_xBr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "4f791c92-ab4a-4270-8812-e9ec2c5f5344"
      },
      "source": [
        "fig = plt.figure(figsize=(6,5), dpi=200)\n",
        "left, bottom, width, height = 0.1, 0.3, 2, 1.3\n",
        "ax = fig.add_axes([left, bottom, width, height]) \n",
        " \n",
        "width = 0.3\n",
        "ticks = np.arange(len(final_list))    \n",
        "\n",
        "ax.bar(ticks, final_list, width, label='Uplifting')\n",
        "\n",
        "ax.bar(ticks + 2*width, final_list2, width, align=\"center\",\n",
        "    label='Neutral')\n",
        "\n",
        "ax.bar(ticks + width, final_list1, width, align=\"center\",\n",
        "    label='Sad')\n",
        "\n",
        "\n",
        "ax.set_ylabel('Rating')\n",
        "ax.set_title('Rating on custom questionnaire')\n",
        "ax.set_xticks(ticks + width/2)\n",
        "ax.set_xticklabels(['Q1','Q2','Q3','Q4','Q5','Q6','Q7','Q8','Q9','Q10','Q11','Q12','Q13','Q14','Q15'])\n",
        "\n",
        "ax.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IV2Vrbf5HI2IW2apiAPuSrfDWkmdTSjNK1PBYoT28RN9S9qfhfvlXiWBOZ5tbaO8bEVuVEbaplOLnfE++cmOLUkovRsS9ZCuMQfY539fKJYtZNdTXVPHefyalNLNE/yeAj+btcld0a8m+hfatZfS/hcYrwHWW/WlYYfA3KaUlZVzzCFlguA+rhsGqec+VY08aVi59jWyr3RallFZGxI1kWzVD48+1JaV+p9+JiH8DW5MFQ4ex6u90W8cs57e/kTwAuTvwQWAgWViwuGjANoX2hyjxXrXBL8vo84lC++dljjuZhtV49wJK/R2QJEmSJKmmGPqTJEmSJEld1QrgrJTSDeV0joh1yEI2J9KwHWUp5fbrKKWCIZAFH+s0t4JUMbj4SJnzPkrlQn/FFZgebrFXYw/REPrbuUTfSrxHbbF7oT21nWNVVEppbkT8lazG/sDfIuJWYALZ1quLOnD61f2c60J/pT7np1vaNrbgjUL7yRZ7NVhQaK/2fZFvLbpj4dBfSl2TUno6IhYA66/uvKtpj0J7h4i4po3XrxcRfepWXKzyPVeO4n35aEsrgjbxUPH6iIiWVq3MdcRvUMXGjIihZFtvH0H5W7dX6m/ffygdRhwIbJU/XUYW+C9n7A8U2puuVnWSJEmSJK3BDP1JkiRJkqRaNY0sfFanL1kQrW7lpu7A9RGxRUrp7GaurxcR65GtCtTWlfvWLd2lQ7W2DWWdYhBqrWbOb1hoz23mfHNeKLNfOYrzP1/mNbML7VLhk0q8R20xuNB+tp1jdYTjyO71wWTfmZPyx/KI+DvwANmKepPyrVsrZU34nIuBrrb2b8990Z+G1fMA5pR53Rw6P/S3caG9F6u3jet6ZCv/1anWPVeO9t6XPcn+DrzdSv+O+A2qyJgRsRMwiewza4tK/e17o4yg5ZBCuyfZ1t9t1dbXJ0mSJEnSGq9b6S6SJEmSJElrpHtSSicXHqNSSh8j28bwF4V+34mIo0qM9b80BP6WAdcDh5GtLrQu0COlFCmloPE2n9X+t5XWVpcqV99Cu9xVt96twLzNzb+wxV6NFfuVCp9U4j1qi2I9lXyfKiKl9BTZqnNX0zg41APYFTiNLID1fEQcX8Gp17TPuTPvi75Nnpf7PSv3faqk/hUYo9H/0LyK91w52ntfQol7s8QqgKur3WNGRC/g1zQE4uYDF5FtWbwp2XbN3Qp/+75UuLxSf/ta3c49V/F7UpIkSZKkrsD/Y1eSJEmSJHUpKaVXIuJYshWyDswPj4uIP6aU3mjaPyI2AT6TP10JHJRSmtLKFNVe3a/SisG03mVe06fC89eFOsodt9jvnQrWUgnFepqGvTpaWUGclNKrwKkRcSbZtqsfJVsh8yM0bAO6CfCTiNghpXRqBWor3mdd4XNui6bhz96UFzCr5PesTql7pFjXaSmlH1Ri0irdc+Vo730JtXtv/j8aQuwvAh9OKb3cSv9q/e0r3pNvp5QqEQKUJEmSJKnmVft/jS5JkiRJklRxKaWVwPE0hAXWB77TQvePAZG3/1Ai8AcwrP0VrlFeK7SHlnlNuf3KMb/Q3qzMa4YX2q+11KlKXi20N2+xV3mK23OW8z/ebVMYJqW0NKV0f0rpopTSJ8i20D0Y+HOh2ykR8eG2jNuCrvY5t8VbZCuI1in39W9aRp9K3yPF+3ejMsZrk06+58rR3vtyGbUb+tuv0L6yROAPqve3r3hP9ouIcsPpkiRJkiR1aYb+JEmSJElSl5RSegG4snDo5IhoLsSycaE9q4yh925XYWuevxfa/1XmNbtVcP7HCu09y7ym2G9GBWuphL8W2vu2c6y3C+2BZfTfvj2TpZT+k1K6F9gfeKJw6lPNdW/j8F3tcy5bvr3rzMKh3UtdExFbUt5nXul75JFC+yNljNcuHXzPlaN4X+4WEd3LuKZ4Xz7WQdv3doaa+NuXhxHnFg6V+/shSZIkSVKXZuhPkiRJkiR1ZZfTsArT2sA3m+mzstBudQWhfIWhL5Q595LCdWuVeU01TC20D4mIVlcCi4i9qOyKT5ML7c9ExNol5t8V2KFwqNTKjJ3tj8DyvL1lRBzYWucSZhfaO0ZEtNQxd1Q75qqXUloKTCwcGtxMtyWFdjn3d/Fz/kREDGqtc0RsTLYCXHPX16LifXpsGf3L/Z2ZXWh/qLWO+Xen1OqT99Fw/+4ZETuWWUe7dNA9V46HgaV5e0PgkNY6R0Q34EuFQ7V8X7blb98uQGetvticuwvtr1atCkmSJEmS1iCG/iRJkiRJUpeVUnoDuLpw6CsRsWGTbs8W2p8osdLT5TQfRmnO64X2JmVeUw33Ai/l7b7A/7TUMSJ6ApdVeP6fA+/m7SHAmBLzFz/PKSmlf1a4nnZJKb0E/Kpw6LqIKPeeaeofNIRWhwAfb6ljRBxC6cDSenloqRzFrWXnNXO+rff3ROC5vN2LxqtwNpKHG6+mIdj1b+BPZcyxJruh0N49IloM/kXE+4FvlDlucWW+L7YyZg/gqlKDpZReBMbXXQb8NCL6lVNIRHRr+vta5XuupJTSmzT+vl4aEeu2csnJNKyWuBL4cSXqqJLi375DW+qUh92r/TovB1bk7ZERMarcC1tY4VeSJEmSpJpn6E+SJEmSJHV1V9AQKusNnN7k/GRgUd5+P3BLRAwodoiIfhHxY+BEYGGZ8xa3qjyyTRV3opTScmBs4dBXI+L7ecCuXh7m+TXZFsBLqZCU0tvAhYVD34qIC5uZfzDwOxq2Rl0OfLtSdVTYt4EFeXsY8JeWVvyLiAERcUJEXNL0XP7Z3FY49JOI+ECT6yMiPp/3K/W5HAY8HRFnRMTwFurpFREnA0cUDv+hma7F+/uwpp9XUymllcC3Coc+GxE/iYi+TeZfF7gJ+HTh8Dfz62tWSulp4ObCoesjYpWQXr4a3x+BPsCyMob+FQ0rtu0REd9rGlyOiKFkK6XtSXnf3bOBl/P2DsCjEdFa4HRoRHwD+CdwdJPTVbvn2uACGv5GbAXcFxFbNKmxW0R8nezvSZ3/TSnNrlAN1XBXof3FiDi9mXvn/WSB3Z0p/29fxaWU/g1cVDh0Y0RcFhEbNNc/InpExMcj4lYab+EsSZIkSVKX0aPaBUiSJEmSJHWklNLrEfG/wFn5oa9FxCUppQX5+Tci4jLgvPz854CDI+IR4EWyFdZGkIVwlpNtLXhLGVP/GqgLen0/Ig4GnqRx6ObifDXCaruebJW4w/Ln3wSOi4ipZOG1ocC+ZFskP0sWvqtbiawSYazLgL2AT+XPzwFOiogpwBtkK4DtS7ZCXJ0zU0qPsAZKKc2NiKOA35Ktnrg5cG9EPA88Svae9iULGH2IbEW737Uw3EXAZ8juv02Bv0fE/WSfQz+yINdmZPfmiWSfZWveB1xKtqLZHOBxGlZV24gsVLl+of/PUkoPNzPOH4DFwDr5a/hHfr+8CaS8z8SUUv2WrSml2yJib+Br+aHjgaPzz/lVYBCwH9l7U+fKlNJvSrymWnEasAewNdm9fHNEnAf8hex34YPAbmQr7P0GGAjs09qAKaXnI+JHNGx5ehZZoPIBsu1w3wd8BOhJtlriK5TYXjil9FJEHAbcA2yQ13tfRLxIdv/OJ7tnNwC2o/SWwVW758qRUvp3RBwP/AzoTvYZ/TMiHiRbZbIv8FEary74V5rfLr5mpJQm5vfJ3mT33GVkfx9nAG8BW5L9vnQn+1t4FbBKOLkTnQ8MJ1vRMsgC/KdExHSyz2kR2W/icLKwap/8utebDiRJkiRJUldg6E+SJEmSJL0XXE62LWMfsgDHN4BzC+cvIAsKfCF/vj5wcJMx3gS+BPy9zDlvJgvX1AUq9s0fRdeQhdqqKqWUIuJospo/kx8eCPy/Jl3/AYyk4X0CeLsC86+MiE8DPwBOIguZDKTxyl913gJGp5Rubu+8HSmlNCki9iILiO6YHx6WP5rzbnMHU0qzI+IIshBpb7Kw1f5Nur1Ndm/OKFHWu2ThqMifb5Y/mrMS+BEwuoW63oqI04Br8/G2yB9N55vY5LqTI+IVsmBnL2Bdmt9adAlwQUrpuyVeU83IA8YfIwt47pofbu59uxMYReOV2FpzOtnv1yfy55uxarDvbuDztLKtcpNap+WrDt5AFsSELPQ2spXLXgX+1eRY1e+5cqSUfhURC8lCs4PJ/t28ud9sgF8Ax6eUlrR1njXQUWThzp3z55uzaojzKbLVanfrxLpWkVJKwKiI+BtZAHA9sjDrnvmj2cuAhzqnQkmSJEmSOpehP0mSJEmS1OWllOZHxDjgjPzQKRFxeUrpzfz8CrLtDW8HTiDbwnY9skDeHLKQzo35CljDy5zzPxGxP3AcWXhuO7IwYaW2pKyolNJSshXCbiJbgW0PspXX3gCeAX4J3JRSWhgRxVW53qzQ/MvJPpcfAV8mCxptShYKWwA8TRZO+UlKqSZWbkopzYyInYDD88ceZIGiPmRBvWfJVk67C7ivlXHujYhtyO7fA8nelxVk9+ZdwLiU0pxS92ZK6Y6IGAJ8nGz1tx3JQlN121m/RfY+/xn4aUrpqRLj/SgiZgFfIfvObEIWTIwS112Ub7t5fP56Ns9reJPsPbkPuD6lNKe1cWpR/huyO1lw9nNkK5L1JwvMzSQLif46D+KWO+aSiPgk8FmyVdB2zsecl495M3BHW8bMx30e2D8i9iALfe1Ndu+tR7ay5OtkIb/pZEG7qfn3uDjGGnHPlfl67863s/0y8EmylRc3IFtd8CVgSl7jGrnC6OpIKb0aEXuSfRc/Q/Z3qjfZvfNPsu2jf5ZSWhQRVQ391UkpXR0RN5OFWA8gu6c2JFuJ9h3gBbJVdacC96SU5lanUkmSJEmSOlZk/wM5SZIkSZIkqTwR8RANKyvt3pVCMNKaIt+6tm57331TSlOrV40kSZIkSZLWJN2qXYAkSZIkSZJqR0QMI1thC2AZ2UpikiRJkiRJkqROYuhPkiRJkiRJZYlsb9CrgO75oQkppSVVLEmSJEmSJEmS3nMM/UmSJEmSJImIuCAivh4RG7RwfjgwATgsP7QCuKxzqpMkSZIkSZIk1elR7QIkSZIkSZK0RtgMOBe4LCJmAf8HvAX0BbYBdqJhhT+Ai1JK0zu9SkmSJEmSJEl6jzP0J0mSJEmSpKIeZAG/nVo4vxg4L6XkKn+SJEmSJEmSVAWG/iRJkiRJkgQwGpgCfAz4ILAhsAHZ6n4LgH8Ck4AbUkovV6tISZIkSZIkSXqvi5RStWuQJEmSJEmSJEmSJEmSJEll6FbtAiRJkiRJkiRJkiRJkiRJUnkM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCN6VLsArbkiohewff50PrCiiuVIkiRJkiRJkiRJkiRJUi3pDmyYt2ellJZWYlBDf2rN9sC0ahchSZIkSZIkSZIkSZIkSTXuw8D0Sgzk9r6SJEmSJEmSJEmSJEmSJNUIV/pTa+bXNR599FGGDBlSzVokSZIkSZIkSZIkSZIkqWa8/PLL7LbbbnVP57fWty0M/ak1K+oaQ4YMYejQodWsRZIkSZIkSZIkSZIkSZJq1YrSXcrj9r6SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNUIQ3+SJEmSJEmSJEmSJEmSJNWIHtUuQJIkSZIkSZIkSZIkSVJlLVmyhDfffJNFixaxYsWKapcj1aTu3bvTu3dvBgwYwNprr13tcuoZ+pMkSZIkSZIkSZIkSZK6iJQSL7/8Mm+99Va1S5Fq3vLly1m6dClvvPEG/fv3Z8iQIUREtcsy9CdJkiRJkiRJkiRJkiR1Fa+//voqgb8ePYwISatj+fLl9e233nqLnj17ssEGG1SxoozfaEmSJEmSJEmSJEmSJKkLWN8EKHQAACAASURBVLZsGfPnz69/PmjQIAYMGED37t2rWJVUu1asWMGbb77JvHnzAJg/fz79+vWjZ8+eVa2rW1VnlyRJkiRJkiRJkiRJklQR7777bn174MCBDBw40MCf1A7du3ev/y7VKX7PqsXQnyRJkiRJkiRJkiRJktQFLFy4sL7dr1+/KlYidS3F71Pxe1Ythv4kSZIkSZIkSZIkSZKkLmDZsmUARAS9evWqcjVS19GrVy8iAmj4nlWToT9JkiRJkiRJkiRJkiSpC1i5ciWQbUlaF1CS1H4RUb9Vdt33rJoM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/UmSJEmSJEmSJEmSJEmSVCMM/XWCiNgsIs6PiOkRMT8ilkTE3Ih4MCIuiIjtSlx/cERMiIgXImJp/t8JEXFwZ70GSZIkSZIkSZIkSZIkSWu2sWPHEhFERLPnR4wYQUQwYsSIFsdYsWIFV111Fbvtthv9+vWrH+/www9vcz3Dhw8nIhg1alSbr1XLDP11sIg4BXgKOA/YBdgA6AUMBfYCzgWOb+HabhFxPXAPcDiwCdAz/+/hwD0R8ZOI8HOUJEmSJEmSJEmSJEmSOtGoUaPqA3GzZ88u65q6ENzw4cM7tLb2+OxnP8vo0aOZNm0a77zzTrXLUTN6VLuAriwizgEuzJ8+DfwEmAa8BQwEdgJGAitbGOJi4Li8/RhwCfBv4H3AN/PrjwfmA9+p/CuQJEmSJEmSJEmSJElSVzX8W7+vdgkdZvb3Dql2CTXp4Ycf5vbbbwfgkEMOYfTo0QwePJiIoF+/fkC2WuD999/PPvvsw9SpU6tY7XuXob8OEhH70RD4+ylwfErpP026TQIui4iezVy/FXBG/nQ6sHdKaXH+fFpE3AncD+wKnBkRN6aUnqn065AkSZIkSZIkSZIkSZLUNZQK6f3pT38CoHv37vz85z+vD/qtrnJXQFTbuC1sB8i32x2XP50JHNdM4K9eSmlZM4dH0xDKPKUQ+Ku7ZhFwSv60B/CNdhUtSZIkSZIkSZIkSZIk6T3txRdfBGDw4MHtDvyp4xj66xgfB7bM299PKS1vy8UREcBh+dP/Syn9tbl++fF/5k8Py6+TJEmSJEmSJEmSJEmSpDZbunQpAGuttVaVK1FrDP11jCPz/ybg7rqDEbF+RGwZEeuXuH5zYOO8fX+JvnXnNwGGt7FOSZIkSZIkSZIkSZIkSVU2depUIoKIYOrUqaxcuZKf/OQn7Lnnnqy//vr06dOHHXfcke9+97ssWbJktecZMWIEEcGIESMaHa+b+5ZbbgHg+eefrz9W9xg1ahQRwf33Z3Gl+++/f5U+w4cPbzTu8OHD668t9ZoBbrvtNvbbbz823HBD1llnHbbeemu++c1vsmDBgpKvbc6cOZx00klsvvnmrL322my88cYcfvjhTJkyBYCxY8fWz1frepTuotWwe/7f2SmldyLiGODbwHZ1HSLiaeAnwNUppaVNrv9Aof1/JeYqnt8WeG71SpYkSZIkSZIkSZIkSZJUbcuWLeOQQw7h3nvvbXT88ccf5/HHH2f8+PFMmjSJjTbaqEoVVt7KlSv5/Oc/z/jx4xsdf/rpp7n00kuZMGECDz74YIuvefLkyRx22GG8++679cdefvllfve733HnnXdy0UUXdWj9nc2V/iosIroB2+RPX4uIq4CfUQj85bYCLgUmR8SAJueGFtovlJhybqG9aRtrHdraA+g6vwySJEmSJEmSJEmSJElSDTjnnHO49957+fjHP86ECROYPn06EyZM4IADDgDgqaee4lOf+hQrVqyo2JyzZs1i1qxZHHbYYQBsvPHG9cfqHhdffDGzZs1i1113BWDXXXddpc/EiRNXa/5zzz2X8ePHc/jhh/Ob3/yGv/3tb9xzzz0ccsghADzzzDN84xvfaPbaZ599lkMPPZR3332XHj16cMoppzBp0iSmTZvGTTfdxLbbbsvZZ5+9SoiylrnSX+X1pyFMuT3wYeBl4EzgHmBJfuz7ZCsC7gncCHy6MMa6hfa7tG5hod23jbXOLd1FkiRJkiRJkiRJkiRJUmeZNm0aJ5xwAtddd139sV122YXDDz+c448/nhtuuIHp06dz3XXX8dWvfrUic263Xbae2YAB2dpla621Vv2xok022YQ+ffoA0KdPn2b7rI6HH36Yiy66iLPPPrvR8YMOOoiDDjqIiRMncscdd/DDH/6QDTfcsFGf008/nYULswjV7bffzuGHH15/btddd+Woo45i33335ZFHHqlIrWsCV/qrvD6F9trAImDflNLPUkpvpJQWp5QeAD4GzMz7jYyI/2pyXZ1lJeYrbg28zuoWLUmSJEmSJEmSJEmSJKn6Bg8ezA9+8INmz1155ZX1obdrr722M8vqULvssgvf+c53VjkeEZx22mkALF++nL/85S+Nzr/00kvcddddABxxxBGNAn91evfuzY9//OMOqLp6DP1V3pImz69PKf2zaaeU0mKgGE09uoUxepaYr1ehvbisChtsWuLx4TaOJ0mSJEmSJEmSJEmSJKkdjjrqKHr37t3sub59+3LUUUcB8OSTT/LKK690Zmkd5phjjiEimj23yy671LefffbZRuemTJlSv83x5z//+RbH33HHHdlxxx0rUOmawdBf5b3T5HlrG1VPApbn7WLArjhGqS17iysLltoKuJGU0gutPYCu8asgSZIkSZIkSZIkSZIk1YgPf7j1dbp22223+vasWbM6upxOsc0227R4bv31169vv/NO42jWE088Ud8uhgObs+uuu65mdWseQ38VllJaCswvHJrbSt8lwGv50+Jm0y8U2kNLTLlpOXNJkiRJkiRJkiRJkiRJqpziynQppbKuqevX0qp2AIMGDWp1jMGDB9e3FyxYUNa8a7qWVjYE6NatIeJWt6pfnTfeeKO+XbftcUtKna8lhv46xpOFdvcSfevOLy8ce6rQbjnGuur5f5ToK0mSJEmSJEmSJEmSJKkC1llnnfr24sWLy7pm4cKFAPTp06fFPq0FAiUw9NdRHii0t2ipU0T0AzbIn75YOPUc8FLe3qfEXHsXrp9dfomSJEmSJEmSJEmSJEmSVldx29lXXnmlZP+lS5fy5ptvrnJtU6+++mqr4xTPtzbOe8F6661X354/f34rPUufryWG/jrGrwvtka30GwnURXMfrDuYsnU8f5c/3SYidm/u4vx43Up/v0vlrhMqSZIkSZIkSZIkSZIkqV122GGH+vbf/va3kv1nzpxZvz1t8dqmpk2b1uo4xfPbbbddyXkrbU1aifCDH/xgfbvUZzB9+vSOLqfTGPrrACmlx4E/5E8/GxH7Ne0TERsBF+VPlwE3NelyJVC3CfXVEbFO8WT+/Or86fK8vyRJkiRJkiRJkiRJkqROsM8++9CjRw8AfvnLX1Jqva7x48fXt/fbb5U4Ub3bb7+9xe2CFy5cyG233QbABz7wAYYMGdLWsttt7bXXBrKVC6ttxIgRdOuWReBuvfXWFvvNnDmTmTNndlZZHc7QX8cZDbxJ9h7fHRHfjYiPRsSuEfFVYBowNO97bkqpuL0vKaWngUvzp7sCD0XE0fn1RwMP5ccBLk0p/aujX5AkSZIkSZIkSZIkSZKkzODBgznyyCMBmDFjBt/73vda7Dt58mR+9KMfATB8+HAOPfTQFvu+8sornH766c2eO+2005g3bx4AJ5100uqW3i51QcNnn322ZNCxow0dOpRDDjkEgDvuuIPf/va3q/RZvHgxJ5xwQmeX1qF6VLuAriql9HREfAq4AxgMfCt/NOoGXJxSuqSFYc4GBgFfBnYCftl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\n",
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mclK5BH_P9Mh"
      },
      "source": [
        "#General Questionnaire Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EA79Uu3A_xBs"
      },
      "source": [
        "# This is for General questionnaire (15 questions)\n",
        "\n",
        "# Uplifting\n",
        "final_list = []\n",
        "for i in range(33,48):\n",
        "    txt = metadata.columns[i]\n",
        "    ax1 = metadata[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        if ax1[ii] == 'Definitely Not':\n",
        "            score+=-4\n",
        "        elif ax1[ii] == 'Probably Not':\n",
        "            score+=-2\n",
        "        elif ax1[ii] == 'Possibly':\n",
        "            score+=0\n",
        "        elif ax1[ii] == 'Probably':\n",
        "            score+=2\n",
        "        elif ax1[ii] == 'Definitely':\n",
        "            score+=4\n",
        "    final_list.append(score)\n",
        "\n",
        "    \n",
        "# Neutral\n",
        "final_list2 = []\n",
        "for i in range(33,48):\n",
        "    txt = metadata2.columns[i]\n",
        "    ax1 = metadata2[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        if ax1[ii] == 'Definitely Not':\n",
        "            score+=-4\n",
        "        elif ax1[ii] == 'Probably Not':\n",
        "            score+=-2\n",
        "        elif ax1[ii] == 'Possibly':\n",
        "            score+=0\n",
        "        elif ax1[ii] == 'Probably':\n",
        "            score+=2\n",
        "        elif ax1[ii] == 'Definitely':\n",
        "            score+=4\n",
        "    final_list2.append(score)\n",
        "    \n",
        "    \n",
        "# Sad    \n",
        "final_list1 = []\n",
        "for i in range(33,48):\n",
        "    txt = metadata1.columns[i]\n",
        "    ax1 = metadata1[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        if ax1[ii] == 'Definitely Not':\n",
        "            score+=-4\n",
        "        elif ax1[ii] == 'Probably Not':\n",
        "            score+=-2\n",
        "        elif ax1[ii] == 'Possibly':\n",
        "            score+=0\n",
        "        elif ax1[ii] == 'Probably':\n",
        "            score+=2\n",
        "        elif ax1[ii] == 'Definitely':\n",
        "            score+=4\n",
        "    final_list1.append(score)\n",
        "    \n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0o3Jstcq_xBs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "1a7f367a-dfef-499c-bb97-53705ed2a127"
      },
      "source": [
        "fig = plt.figure(figsize=(6,5), dpi=200)\n",
        "left, bottom, width, height = 0.1, 0.3, 2, 1.3\n",
        "ax = fig.add_axes([left, bottom, width, height]) \n",
        " \n",
        "width = 0.3\n",
        "ticks = np.arange(len(final_list))    \n",
        "\n",
        "ax.bar(ticks, final_list, width, label='Uplifting')\n",
        "\n",
        "ax.bar(ticks + 2*width, final_list2, width, align=\"center\",\n",
        "    label='Neutral')\n",
        "\n",
        "ax.bar(ticks + width, final_list1, width, align=\"center\",\n",
        "    label='Sad')\n",
        "\n",
        "\n",
        "ax.set_ylabel('Rating')\n",
        "ax.set_title('Rating on General| questionnaire')\n",
        "ax.set_xticks(ticks + width/2)\n",
        "ax.set_xticklabels(['Q1','Q2','Q3','Q4','Q5','Q6','Q7','Q8','Q9','Q10','Q11','Q12','Q13','Q14','Q15'])\n",
        "\n",
        "ax.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXipT5N7_xBs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17b2eadf-f59f-4827-b155-6aeb74e67c4a"
      },
      "source": [
        "# This is for the rating based questions\n",
        "\n",
        "# Uplifting\n",
        "final_list = []\n",
        "for i in range(6,15):\n",
        "    txt = metadata.columns[i]\n",
        "    ax1 = metadata[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        score+=ax1[ii]\n",
        "\n",
        "    final_list.append(score/size_temp)\n",
        "    \n",
        "# Neutral\n",
        "final_list2 = []\n",
        "for i in range(6,15):\n",
        "    txt = metadata2.columns[i]\n",
        "    ax1 = metadata2[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        score+=ax1[ii]\n",
        "    final_list2.append(score/size_temp)\n",
        "\n",
        "# sad\n",
        "final_list1 = []\n",
        "for i in range(6,15):\n",
        "    txt = metadata1.columns[i]\n",
        "    ax1 = metadata1[txt]\n",
        "    size_temp = ax1.size\n",
        "    score = 0\n",
        "    for ii in range(size_temp):\n",
        "        score+=ax1[ii]\n",
        "    final_list1.append(score/size_temp)\n",
        "\n",
        "print(final_list1)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6.75, 3.5, 6.3125, 4.8125, 2.75, 4.25, 6.4375, 6.375, 2.25]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6oJ1itG9eOG"
      },
      "source": [
        "#shivam function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWZoZ6SeOczm"
      },
      "source": [
        "def shivam_function(arousal_list,altruistic_list,data_type,column_no1,column_no2):    \n",
        "    forbidden_i=[34,38,40,42,44]\n",
        "    for row in data_type.iterrows(): #altruistic\n",
        "        arousal_list.append(sum(row[1][column_no1])) #moved\n",
        "        score=0\n",
        "        for i in column_no2:\n",
        "            if i in forbidden_i:\n",
        "              if txt == 'Definitely Not' :\n",
        "                  score+=4\n",
        "              elif txt == 'Probably Not':\n",
        "                  score+=2              \n",
        "              elif txt == 'Possibly':\n",
        "                score+=0\n",
        "              elif txt == 'Probably':\n",
        "                  score+=-2\n",
        "              elif txt == 'Definitely':\n",
        "                  score+=-4\n",
        "              continue\n",
        "\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely Not' :\n",
        "                score+=-4\n",
        "            elif txt == 'Probably Not':\n",
        "                score+=-2\n",
        "            elif txt == 'Possibly':\n",
        "                score+=0\n",
        "            elif txt == 'Probably':\n",
        "                score+=2\n",
        "            elif txt == 'Definitely':\n",
        "                score+=4\n",
        "        altruistic_list.append(score)\n",
        "    return arousal_list, altruistic_list\n",
        "academic = [19,28,29,30,31,33,38,48]\n",
        "community = [20,23,24,36,37,39,40,42,43,44,45,46]\n",
        "fnf = [18,21,22,27,35]\n",
        "general = [25,26,32,34,41,47]\n",
        "thought = [19,22,24,26,27,31,35,40,41,43,44,45,46,47,48]\n",
        "action = [18,20,21,23,25,28,29,30,32,33,34,36,37,38,39,42]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5NvQNxpRJqr"
      },
      "source": [
        "# Personal things at stake"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MAgoE03RJqr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33926e68-8e22-4e46-8ad1-4f2ce5c1e49d"
      },
      "source": [
        "#OUR CUSTOM QUESTIONS\n",
        "arousal_list = []\n",
        "altruistic_list=[]\n",
        "for i in range(6,15):\n",
        "    arousal_list = []\n",
        "    altruistic_list=[]\n",
        "    arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,[i],thought)#happy\n",
        "    arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,[i],thought) #sad\n",
        "    arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,[i],thought) #neutral\n",
        "    arousal_list = np.asarray(arousal_list)\n",
        "    altruistic_list = np.asarray(altruistic_list)\n",
        "    r = np.corrcoef(arousal_list, altruistic_list)\n",
        "    print(r[0,1])\n",
        "# arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,[12],range(18,33)) #happy\n",
        "# arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,range(6,15),range(18,33)) #sad\n",
        "# arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,range(6,15),range(18,33)) #neutral\n",
        "\n",
        "# arousal_list = np.asarray(arousal_list)\n",
        "# altruistic_list = np.asarray(altruistic_list)\n",
        "# print(arousal_list)\n",
        "# print(altruistic_list)\n",
        "# r = np.corrcoef(arousal_list, altruistic_list)\n",
        "# print(r[0,1])\n",
        "\n",
        "# fig = plt.figure(figsize=(6,5), dpi=200)\n",
        "# left, bottom, width, height = 0.1, 0.3, 2, 1.3\n",
        "# ax = fig.add_axes([left, bottom, width, height]) \n",
        " \n",
        "# width = 0.3\n",
        "# ticks = np.arange(len(arousal_list))    \n",
        "\n",
        "# y=arousal_list-np.mean(arousal_list)\n",
        "# ax.bar(ticks, y/max(y), width, label='arousal list')\n",
        "\n",
        "# x=altruistic_list-np.mean(altruistic_list)\n",
        "# ax.bar(ticks + width, x/max(x), width, align=\"center\",\n",
        "#     label='altruistic list')\n",
        "\n",
        "\n",
        "# ax.set_ylabel('Rating')\n",
        "# ax.set_title('Rating on General| questionnaire')\n",
        "# ax.set_xticks(ticks + width/2)\n",
        "# ax.set_xticklabels(range(1,len(arousal_list)+1))\n",
        "\n",
        "# ax.legend(loc='best')\n",
        "# plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2167848634718954\n",
            "0.031181831448307655\n",
            "0.2533170537733071\n",
            "0.22002530446989393\n",
            "0.04146569073021194\n",
            "0.32516419598202323\n",
            "0.47943800960985583\n",
            "0.33281400862233496\n",
            "-0.08572684693536749\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfmWdUoF_xBt"
      },
      "source": [
        "# Measures of Self Rating vs custom set of questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "7iY50r07JbP3",
        "outputId": "09a768f8-5682-4f78-bfac-8968088a6146"
      },
      "source": [
        "#OUR CUSTOM QUESTIONS\n",
        "arousal_list = []\n",
        "altruistic_list=[]\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,range(6,15),range(18,33)) #happy\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,range(6,15),range(18,33)) #sad\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,range(6,15),range(18,33)) #neutral\n",
        "\n",
        "arousal_list = np.asarray(arousal_list)\n",
        "altruistic_list = np.asarray(altruistic_list)\n",
        "print(arousal_list)\n",
        "print(altruistic_list)\n",
        "r = np.corrcoef(arousal_list, altruistic_list)\n",
        "print(r[0,1])\n",
        "\n",
        "fig = plt.figure(figsize=(6,5), dpi=200)\n",
        "left, bottom, width, height = 0.1, 0.3, 2, 1.3\n",
        "ax = fig.add_axes([left, bottom, width, height]) \n",
        " \n",
        "width = 0.3\n",
        "ticks = np.arange(len(arousal_list))    \n",
        "\n",
        "y=arousal_list-np.mean(arousal_list)\n",
        "ax.bar(ticks, y/max(y), width, label='arousal list')\n",
        "\n",
        "x=altruistic_list-np.mean(altruistic_list)\n",
        "ax.bar(ticks + width, x/max(x), width, align=\"center\",\n",
        "    label='altruistic list')\n",
        "\n",
        "\n",
        "ax.set_ylabel('Rating')\n",
        "ax.set_title('Rating on General| questionnaire')\n",
        "ax.set_xticks(ticks + width/2)\n",
        "ax.set_xticklabels(range(1,50))\n",
        "\n",
        "ax.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[43 73 53 58 54 61 45 64 77 22 62 47 69 66 59 73 57 43 43 49 40 44 41 23\n",
            " 35 54 55 62 52 42 15 50 47 29 33 36 28 61 50 43 14 27 25 68  9 43 38 53\n",
            " 30]\n",
            "[ 22  14  22 -10   0  -4  16   0  16  -2  -4   6  14  16 -10  34  28  18\n",
            "   8  18  10  18  32 -14  16  28  42  32   6  30  22  14  32  20  32   2\n",
            "  12   2  38  42  16   0  22  26  16  42  48  14 -10]\n",
            "0.03668012500117641\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9OeKAiVQl__"
      },
      "source": [
        "# General Questions vs Measures of Self Rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "LyS4dOjsQNUX",
        "outputId": "572f147c-b8a6-467b-a95c-977801e26500"
      },
      "source": [
        "#OUR CUSTOM QUESTIONS\n",
        "arousal_list = []\n",
        "altruistic_list=[]\n",
        "#arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,range(6,15),range(33,48)) #happy\n",
        "#arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,range(6,15),range(33,48)) #sad\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,range(6,15),range(33,48)) #neutral\n",
        "\n",
        "arousal_list = np.asarray(arousal_list)\n",
        "altruistic_list = np.asarray(altruistic_list)\n",
        "print(arousal_list)\n",
        "print(altruistic_list)\n",
        "r = np.corrcoef(arousal_list, altruistic_list)\n",
        "print(r[0,1])\n",
        "\n",
        "fig = plt.figure(figsize=(6,5), dpi=200)\n",
        "left, bottom, width, height = 0.1, 0.3, 2, 1.3\n",
        "ax = fig.add_axes([left, bottom, width, height]) \n",
        " \n",
        "width = 0.3\n",
        "ticks = np.arange(len(arousal_list))    \n",
        "\n",
        "y=arousal_list-np.mean(arousal_list)\n",
        "ax.bar(ticks, y/max(y), width, label='arousal list')\n",
        "\n",
        "x=altruistic_list-np.mean(altruistic_list)\n",
        "ax.bar(ticks + width, x/max(x), width, align=\"center\",\n",
        "    label='altruistic list')\n",
        "\n",
        "\n",
        "ax.set_ylabel('Rating')\n",
        "ax.set_title('Rating on General| questionnaire')\n",
        "ax.set_xticks(ticks + width/2)\n",
        "ax.set_xticklabels(range(1,50))\n",
        "\n",
        "ax.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[29 33 36 28 61 50 43 14 27 25 68  9 43 38 53 30]\n",
            "[10 12  8  0 10  8 14  2 12  6  6  4  6  4 16  2]\n",
            "0.40829641912291015\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZD64XV4RJqr"
      },
      "source": [
        ""
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdYCjAcdRu6F"
      },
      "source": [
        "#GENERAL HEALTH QUESTIONNAIRE VS CUSTOM\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezSPBUcRRx77"
      },
      "source": [
        "def shivam_function_GHQvsAltruistic(arousal_list,altruistic_list,data_type,column_no1,column_no2):    \n",
        "    forbidden_i=[34,38,40,42,44]\n",
        "    for row in data_type.iterrows(): #altruistic\n",
        "        #print(row[1][column_no1])\n",
        "        #arousal_list.append(sum(row[1][column_no1])) #moved\n",
        "        score1=0\n",
        "        score2=0\n",
        "\n",
        "        for i in column_no1:\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score2+=-4\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score2+=-2\n",
        "            elif txt=='Possibly':\n",
        "                score2+=0\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score2+=2\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score2+=4\n",
        "        arousal_list.append(score2)\n",
        "\n",
        "        for i in column_no2:\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score1+=-4\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score1+=-2\n",
        "            elif txt=='Possibly':\n",
        "                score1+=0\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score1+=2\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score1+=4\n",
        "        altruistic_list.append(score1)\n",
        "    return arousal_list, altruistic_list\n",
        "academic = [19,28,29,30,31,33,38,48]\n",
        "community = [20,23,24,36,37,39,40,42,43,44,45,46]\n",
        "fnf = [18,21,22,27,35]\n",
        "general = [25,26,32,34,41,47]\n",
        "thought = [19,22,24,26,27,31,35,40,41,43,44,45,46,47,48]\n",
        "action = [18,20,21,23,25,28,29,30,32,33,34,36,37,38,39,42]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "90G-KcF4R9kN",
        "outputId": "a1aab5c6-5758-4218-f802-20ed369a31c4"
      },
      "source": [
        "#GHQ\n",
        "\"\"\"\n",
        "print(\"1)Arousal \\n2)Pleasantness \\n3)Moved \\n4)Uplifted \\n5)Optimistic about humanity \\nWarm feeling \\nwanting to help others \\nWanting to become a better person \\nHappy \")\n",
        "\n",
        "print(\"happy\")\n",
        "for i in range(6,15):\n",
        "    arousal_list = []\n",
        "    altruistic_list=[]\n",
        "    arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,[i],range(49,61))#happy\n",
        "    #arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,[i],range(49,61)) #sad\n",
        "    #arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,[i],range(49,61)) #neutral\n",
        "    arousal_list = np.asarray(arousal_list)\n",
        "    altruistic_list = np.asarray(altruistic_list)\n",
        "    r = np.corrcoef(arousal_list, altruistic_list)\n",
        "    print(r[0,1])\n",
        "\n",
        "print(\"sad\")\n",
        "for i in range(6,15):\n",
        "    arousal_list = []\n",
        "    altruistic_list=[]\n",
        "    #arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,[i],range(49,61))#happy\n",
        "    arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,[i],range(49,61)) #sad\n",
        "    #arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,[i],range(49,61)) #neutral\n",
        "    arousal_list = np.asarray(arousal_list)\n",
        "    altruistic_list = np.asarray(altruistic_list)\n",
        "    r = np.corrcoef(arousal_list, altruistic_list)\n",
        "    print(r[0,1])\n",
        "\n",
        "print(\"neutral\")\n",
        "for i in range(6,15):\n",
        "    arousal_list = []\n",
        "    altruistic_list=[]\n",
        "    #arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,[i],range(49,61))#happy\n",
        "    #arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,[i],range(49,61)) #sad\n",
        "    arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,[i],range(49,61)) #neutral\n",
        "    arousal_list = np.asarray(arousal_list)\n",
        "    altruistic_list = np.asarray(altruistic_list)\n",
        "    r = np.corrcoef(arousal_list, altruistic_list)\n",
        "    print(r[0,1])\n",
        "    \"\"\"\n",
        "\n",
        "arousal_list = []\n",
        "altruistic_list=[]\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,action,range(49,61))#happy\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,action,range(49,61)) #sad\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,action,range(49,61)) #neutral\n",
        "arousal_list = np.asarray(arousal_list)\n",
        "altruistic_list = np.asarray(altruistic_list)\n",
        "r = np.corrcoef(arousal_list, altruistic_list)\n",
        "print(r[0,1])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-075923554aa8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0marousal_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0maltruistic_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0marousal_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maltruistic_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshivam_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marousal_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maltruistic_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m49\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m61\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#happy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0marousal_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maltruistic_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshivam_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marousal_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maltruistic_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetadata1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m49\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m61\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#sad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0marousal_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maltruistic_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshivam_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marousal_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maltruistic_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmetadata2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m49\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m61\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#neutral\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-dd8e99e812aa>\u001b[0m in \u001b[0;36mshivam_function\u001b[0;34m(arousal_list, altruistic_list, data_type, column_no1, column_no2)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mforbidden_i\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m34\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m38\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m44\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#altruistic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0marousal_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn_no1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#moved\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mscore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumn_no2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yC3wawwIeEKD"
      },
      "source": [
        "def shivam_function_UltavsGHQ(arousal_list,altruistic_list,data_type,column_no1,column_no2):    \n",
        "    forbidden_i=[34,38,40,42,44]\n",
        "    for row in data_type.iterrows(): #altruistic\n",
        "        #print(row[1][column_no1])\n",
        "        #arousal_list.append(sum(row[1][column_no1])) #moved\n",
        "        score1=0\n",
        "        score2=0\n",
        "\n",
        "        for i in column_no1:\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score2+=-4\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score2+=-2\n",
        "            elif txt=='Possibly':\n",
        "                score2+=0\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score2+=2\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score2+=4\n",
        "        arousal_list.append(score2)\n",
        "\n",
        "        for i in column_no2:\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score1+=-4\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score1+=-2\n",
        "            elif txt=='Possibly':\n",
        "                score1+=0\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score1+=2\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score1+=4\n",
        "        altruistic_list.append(score1)\n",
        "    return arousal_list, altruistic_list\n",
        "academic = [19,28,29,30,31,33,38,48]\n",
        "community = [20,23,24,36,37,39,40,42,43,44,45,46]\n",
        "fnf = [18,21,22,27,35]\n",
        "general = [25,26,32,34,41,47]\n",
        "thought = [19,22,24,26,27,31,35,40,41,43,44,45,46,47,48]\n",
        "action = [18,20,21,23,25,28,29,30,32,33,34,36,37,38,39,42]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIzsf-4beWMl",
        "outputId": "1fb69fec-e9b7-40e0-fa28-1c86a9308b71"
      },
      "source": [
        "arousal_list1 = []\n",
        "arousal_list2 = []\n",
        "arousal_list3 = []\n",
        "altruistic_list2=[]\n",
        "altruistic_list1=[]\n",
        "altruistic_list3=[]\n",
        "arousal_list1,altruistic_list1 = shivam_function_UltavsGHQ(arousal_list1,altruistic_list1,metadata,range(33,48),range(49,61))#happy\n",
        "arousal_list2,altruistic_list2 = shivam_function_UltavsGHQ(arousal_list2,altruistic_list2,metadata1,range(33,48),range(49,61)) #sad\n",
        "#arousal_list,altruistic_list = shivam_function(arousal_list3,altruistic_list3,metadata2,range(33,48),range(49,61)) #neutral\n",
        "arousal_list1 = np.asarray(arousal_list1)\n",
        "altruistic_list1 = np.asarray(altruistic_list1)\n",
        "arousal_list2 = np.asarray(arousal_list2)\n",
        "altruistic_list2 = np.asarray(altruistic_list2)\n",
        "\n",
        "r = np.corrcoef(arousal_list1, altruistic_list1)\n",
        "avg=np.mean(arousal_list1)\n",
        "print(np.mean(altruistic_list1[arousal_list1<avg]),altruistic_list1[arousal_list1<avg].size)\n",
        "print( sum(altruistic_list1[arousal_list1<avg]-np.mean(altruistic_list1))/altruistic_list1.size )    \n",
        "\n",
        "r = np.corrcoef(arousal_list2, altruistic_list2)\n",
        "avg=np.mean(arousal_list2)\n",
        "print(np.mean(altruistic_list2[arousal_list2>avg]),altruistic_list2[arousal_list2>avg].size)\n",
        "\n",
        "print(sum(altruistic_list2[arousal_list2>avg]-np.mean(altruistic_list2))/altruistic_list2.size )    \n",
        "#print(r[0,1])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-4.5 8\n",
            "-3.003460207612457\n",
            "2.5 8\n",
            "2.375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPTaSap0Ctpj"
      },
      "source": [
        "#MOSR VS MENTAL HEALTH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txQgYFWtDiu4"
      },
      "source": [
        "def shivam_function_MOSRvsGHQ(arousal_list,altruistic_list,data_type,column_no1,column_no2):    \n",
        "    forbidden_i=[34,38,40,42,44]\n",
        "    score1=0\n",
        "    for row in data_type.iterrows(): #altruistic\n",
        "        arousal_list.append(sum(row[1][column_no1])) #moved\n",
        "        for i in column_no2:\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score1+=-4\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score1+=-2\n",
        "            elif txt=='Possibly':\n",
        "                score1+=0\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score1+=2\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score1+=4\n",
        "        altruistic_list.append(score1)\n",
        "    return arousal_list, altruistic_list"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQEf7yTygpmG",
        "outputId": "dd644119-a029-4628-cbf8-58b1a298d9e9"
      },
      "source": [
        "arousal_list = []\n",
        "altruistic_list=[]\n",
        "i=[14]\n",
        "arousal_list,altruistic_list = shivam_function_MOSRvsGHQ(arousal_list,altruistic_list,metadata,i,range(49,61))#happy\n",
        "arousal_list,altruistic_list = shivam_function_MOSRvsGHQ(arousal_list,altruistic_list,metadata1,i,range(49,61)) #sad\n",
        "arousal_list,altruistic_list = shivam_function_MOSRvsGHQ(arousal_list,altruistic_list,metadata2,i,range(49,61)) #neutral\n",
        "arousal_list = np.asarray(arousal_list)\n",
        "altruistic_list = np.asarray(altruistic_list)\n",
        "r = np.corrcoef(arousal_list, altruistic_list)\n",
        "print(r[0,1])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.24384964461319028\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agwwlj-Pm0Yg"
      },
      "source": [
        "#Finding mean age and mean mental health shit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4CnrAfOD5cS",
        "outputId": "a110e25c-fbda-40b2-d764-8a7777cfa41b"
      },
      "source": [
        "def mentalhealth(data_type):    \n",
        "    a=[]\n",
        "    score1=0\n",
        "    score2=0\n",
        "    score3=0\n",
        "    score4=0\n",
        "    score5=0\n",
        "    score6=0\n",
        "    for row in data_type.iterrows(): #altruistic\n",
        "        for i in range(49,61):\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score1+=-4\n",
        "                score2+=1\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score1+=-2\n",
        "                score3+=1\n",
        "            elif txt=='Possibly':\n",
        "                score1+=0\n",
        "                score4+=1\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score1+=2\n",
        "                score5+=1\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score1+=4\n",
        "                score6+=1\n",
        "            a.append(score1)\n",
        "    return a,score1,score2,score3,score4,score5,score6\n",
        "a=[]\n",
        "data=a\n",
        "score1=0\n",
        "score2=0\n",
        "score3=0\n",
        "score4=0\n",
        "score5=0\n",
        "score6=0\n",
        "a,score1,score2,score3,score4,score5,score6=mentalhealth(metadata)\n",
        "\n",
        "\n",
        "data=a\n",
        "a=(data - np.min(data)) / (np.max(data) - np.min(data))\n",
        "\n",
        "print(score1,score2,score3,score4,score5,score6)\n",
        "#print(a)\n",
        "print(np.mean(a))\n",
        "print(sum(a)/a.size)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32 28 68 0 76 32\n",
            "0.43137254901960786\n",
            "0.43137254901960775\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nfRjLFl77Kl"
      },
      "source": [
        "#SPIRITUALITY VS ALTRUISM\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHZUv62M9AVI",
        "outputId": "6432af3d-4a53-4c10-c7b5-6d85dd990571"
      },
      "source": [
        "def shivam_function(arousal_list,altruistic_list,data_type,column_no1,column_no2):    \n",
        "    forbidden_i=[34,38,40,42,44]\n",
        "    for row in data_type.iterrows(): #altruistic\n",
        "        score=0\n",
        "        for i in column_no1:\n",
        "            if i in forbidden_i:\n",
        "              if txt == 'Definitely Not' or txt=='Man':\n",
        "                  score+=4\n",
        "              elif txt == 'Probably Not':\n",
        "                  score+=2              \n",
        "              elif txt == 'Possibly':\n",
        "                score+=0\n",
        "              elif txt == 'Probably':\n",
        "                  score+=-2\n",
        "              elif txt == 'Definitely' or txt=='Woman':\n",
        "                  score+=-4\n",
        "              continue\n",
        "\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Not at all'  or txt=='Woman' :\n",
        "                score+=-4\n",
        "            elif txt == 'Slightly':\n",
        "                score+=-2\n",
        "            elif txt == 'Somewhat':\n",
        "                score+=0\n",
        "            elif txt == 'Very':\n",
        "                score+=2\n",
        "            elif txt == 'Extremely' or txt=='Man':\n",
        "                score+=4\n",
        "        arousal_list.append(score)\n",
        "\n",
        "\n",
        "        score=0\n",
        "        for i in column_no2:\n",
        "            if i in forbidden_i:\n",
        "              if txt == 'Definitely Not' :\n",
        "                  score+=4\n",
        "              elif txt == 'Probably Not':\n",
        "                  score+=2              \n",
        "              elif txt == 'Possibly':\n",
        "                score+=0\n",
        "              elif txt == 'Probably':\n",
        "                  score+=-2\n",
        "              elif txt == 'Definitely':\n",
        "                  score+=-4\n",
        "              continue\n",
        "\n",
        "            txt = row[1][i]\n",
        "            if txt == 'Definitely not' or txt== 'Much less than usual' or txt== 'Much more than usual' or txt== 'Much less useful' or txt== 'Much less than capable':\n",
        "                score+=-4\n",
        "            elif txt == 'Probably not' or txt=='Less than usual' or txt== 'Rather more than usual' or txt== 'Less useful than usual' or txt== 'Less so than usual' or txt== 'Rather more than usual':\n",
        "                score+=-2\n",
        "            elif txt=='Possibly':\n",
        "                score+=0\n",
        "            elif txt == 'Probably' or txt== 'Same as usual' or txt== 'No more than usual' or txt== 'About same as usual':\n",
        "                score+=2\n",
        "            elif txt == 'Definitely' or txt== 'Better than usual' or txt== 'Not at all' or txt== 'More so than usual':\n",
        "                score+=4\n",
        "        altruistic_list.append(score)\n",
        "    return arousal_list, altruistic_list\n",
        "academic = [19,28,29,30,31,33,38,48]\n",
        "community = [20,23,24,36,37,39,40,42,43,44,45,46]\n",
        "fnf = [18,21,22,27,35]\n",
        "general = [25,26,32,34,41,47]\n",
        "thought = [19,22,24,26,27,31,35,40,41,43,44,45,46,47,48]\n",
        "action = [18,20,21,23,25,28,29,30,32,33,34,36,37,38,39,42]\n",
        "\n",
        "arousal_list = []\n",
        "altruistic_list=[]\n",
        "i=[2]\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata,i,range(18,48))#happy\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata1,i,range(18,48)) #sad\n",
        "arousal_list,altruistic_list = shivam_function(arousal_list,altruistic_list,metadata2,i,range(18,48)) #neutral\n",
        "arousal_list = np.asarray(arousal_list)\n",
        "altruistic_list = np.asarray(altruistic_list)\n",
        "print(arousal_list,arousal_list)\n",
        "r = np.corrcoef(arousal_list, altruistic_list)\n",
        "print(r[0,1])\n",
        "\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 4  4  4  4  4 -4  4  4 -4  4  4  4 -4 -4  4  4 -4  4  4  4  4  4  4  0\n",
            "  4  4  4 -4  4  4 -4 -4  4  4  4  4  4  4 -4  4  4  4  4  4  4  4  4 -4\n",
            "  4] [ 4  4  4  4  4 -4  4  4 -4  4  4  4 -4 -4  4  4 -4  4  4  4  4  4  4  0\n",
            "  4  4  4 -4  4  4 -4 -4  4  4  4  4  4  4 -4  4  4  4  4  4  4  4  4 -4\n",
            "  4]\n",
            "-0.08788928123752883\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiHyheJT_Ord",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "567dae71-562b-4422-9a5c-e1bb799f7fbe"
      },
      "source": [
        "from scipy.stats import f_oneway\n",
        "\n",
        "def p_func(data_type,rating,listie):\n",
        "    for row in data_type.iterrows(): \n",
        "#         print(row[1][rating])\n",
        "        listie.append(row[1][rating])\n",
        "    return listie\n",
        "\n",
        "elev = []\n",
        "cont = []\n",
        "sad = []\n",
        "typie = 14\n",
        "\n",
        "elev = p_func(metadata,typie,elev)\n",
        "cont = p_func(metadata2,typie,cont)\n",
        "sad = p_func(metadata1,typie,sad)\n",
        "\n",
        "print(np.std(np.asarray(elev)))\n",
        "print(np.std(np.asarray(cont)))\n",
        "print(np.std(np.asarray(sad)))\n",
        "\n",
        "stat, p = f_oneway(elev, cont, sad)\n",
        "\n",
        "def prep(p):\n",
        "    return (1/(1+pow((p/(1-p)),2/3)))\n",
        "\n",
        "pp = prep(p)\n",
        "print(stat,pp)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.2478354962115543\n",
            "2.384848003542364\n",
            "1.5612494995995996\n",
            "25.543516955654425 0.9999893876904209\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjBJr68aYE9G",
        "outputId": "911d573e-883a-4c09-87bf-6955f34adcaf"
      },
      "source": [
        "from scipy.stats import f_oneway\n",
        "\n",
        "def p_func(data_type,rating,listie):\n",
        "    for row in data_type.iterrows(): \n",
        "#         print(row[1][rating])\n",
        "        listie.append(row[1][rating])\n",
        "    return listie\n",
        "\n",
        "elev = []\n",
        "cont = []\n",
        "sad = []\n",
        "typie = 14\n",
        "\n",
        "elev = p_func(metadata,typie,elev)\n",
        "cont = p_func(metadata2,typie,cont)\n",
        "sad = p_func(metadata1,typie,sad)\n",
        "\n",
        "print(np.std(np.asarray(elev)))\n",
        "print(np.std(np.asarray(cont)))\n",
        "print(np.std(np.asarray(sad)))\n",
        "\n",
        "stat, p = f_oneway(elev, cont, sad)\n",
        "\n",
        "def prep(p):\n",
        "    return (1/(1+pow((p/(1-p)),2/3)))\n",
        "\n",
        "pp = prep(p)\n",
        "print(stat, pp)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.2478354962115543\n",
            "2.384848003542364\n",
            "1.5612494995995996\n",
            "25.543516955654425 0.9999893876904209\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4eTGKd6Y2Gp",
        "outputId": "4c775b84-7e40-4b22-e940-32bbfd2f5fd0"
      },
      "source": [
        "temp = [i for i in range(6,15)]\n",
        "elev = []\n",
        "cont = []\n",
        "sad = []\n",
        "\n",
        "#for each i have to get from all csv\n",
        "#concat all of them again\n",
        "\n",
        "def p_func_new(rating, listie):\n",
        "    for row in metadata.iterrows(): \n",
        "#         print(row[1][rating])\n",
        "        listie.append(row[1][rating])\n",
        "    for row in metadata1.iterrows(): \n",
        "#         print(row[1][rating])\n",
        "        listie.append(row[1][rating])\n",
        "    for row in metadata2.iterrows(): \n",
        "#         print(row[1][rating])\n",
        "        listie.append(row[1][rating]) \n",
        "    return listie\n",
        "\n",
        "listforeachi = [p_func_new(i, []) for i in range(6, 15)]\n",
        "print(listforeachi)\n",
        "print(len(listforeachi[0]))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3, 9, 7, 6, 5, 4, 2, 8, 9, 1, 6, 5, 6, 5, 8, 9, 6, 6, 7, 9, 7, 7, 7, 1, 8, 8, 8, 9, 8, 7, 2, 7, 7, 3, 1, 3, 3, 6, 1, 3, 2, 3, 2, 7, 1, 2, 5, 6, 8], [6, 9, 9, 9, 8, 9, 8, 9, 9, 7, 8, 7, 9, 8, 8, 9, 7, 3, 2, 1, 3, 3, 3, 5, 1, 9, 2, 9, 3, 3, 4, 3, 2, 5, 9, 5, 6, 7, 9, 5, 5, 5, 5, 7, 1, 7, 9, 7, 1], [4, 9, 7, 7, 4, 8, 2, 7, 9, 1, 9, 3, 7, 7, 7, 8, 6, 5, 7, 9, 7, 7, 6, 1, 7, 7, 8, 8, 7, 8, 1, 7, 6, 3, 1, 1, 3, 7, 6, 5, 1, 1, 1, 7, 1, 1, 1, 2, 2], [5, 9, 5, 6, 6, 6, 3, 7, 9, 2, 8, 4, 8, 8, 8, 8, 6, 5, 8, 9, 1, 2, 5, 5, 1, 3, 8, 7, 6, 2, 1, 8, 6, 3, 1, 2, 3, 7, 7, 5, 1, 2, 1, 8, 1, 4, 2, 5, 2], [3, 6, 4, 6, 5, 7, 5, 6, 7, 2, 6, 8, 7, 8, 6, 9, 6, 4, 1, 1, 3, 6, 2, 1, 1, 7, 2, 2, 6, 2, 1, 2, 3, 3, 5, 7, 3, 7, 2, 5, 1, 1, 3, 7, 1, 8, 5, 6, 1], [6, 5, 6, 6, 5, 8, 6, 6, 9, 2, 6, 3, 9, 8, 4, 9, 7, 5, 2, 1, 3, 3, 3, 1, 6, 9, 8, 9, 6, 2, 2, 4, 4, 3, 1, 1, 1, 7, 8, 5, 1, 1, 2, 8, 1, 6, 1, 5, 5], [6, 8, 5, 5, 7, 7, 7, 6, 7, 1, 6, 5, 7, 7, 6, 6, 7, 5, 7, 9, 8, 6, 6, 1, 5, 6, 8, 8, 7, 8, 2, 9, 8, 3, 5, 9, 3, 8, 7, 5, 1, 7, 3, 9, 1, 9, 5, 8, 4], [5, 9, 4, 6, 7, 6, 5, 8, 9, 1, 6, 7, 8, 7, 6, 7, 6, 6, 7, 9, 7, 7, 6, 1, 5, 4, 8, 9, 6, 8, 1, 9, 9, 3, 5, 6, 3, 6, 1, 5, 1, 4, 5, 7, 1, 3, 5, 6, 4], [5, 9, 6, 7, 7, 6, 7, 7, 9, 5, 7, 5, 8, 8, 6, 8, 6, 4, 2, 1, 1, 3, 3, 7, 1, 1, 3, 1, 3, 2, 1, 1, 2, 3, 5, 2, 3, 6, 9, 5, 1, 3, 3, 8, 1, 3, 5, 8, 3]]\n",
            "49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWvY4jJkacXs"
      },
      "source": [
        "varforeachi = [pow(np.std(np.asarray(x)), 2) for x in listforeachi]"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntCNvzUIdP9W",
        "outputId": "d8807ebe-19be-4848-fd78-fc2bd9b37089"
      },
      "source": [
        "ssforeachi = [np.sum(pow(x, 2) for x in li) for li in listforeachi]"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EU3XQQo-ddCJ",
        "outputId": "fe3029ae-3d6d-4e11-8f72-f4fd3b87fc48"
      },
      "source": [
        "ssforeachi"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1741, 2058, 1655, 1509, 1188, 1419, 1995, 1802, 1318]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ey2myVWXdgfS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M95UFPwFbq6g",
        "outputId": "12a6e231-bc50-42dd-a78b-0ff7fc87e5b0"
      },
      "source": [
        "len(varforeachi)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wW9DR1_fbv_7",
        "outputId": "95c3b6ce-8ea2-4f66-80a0-57c8451642a4"
      },
      "source": [
        "avinash = []\n",
        "for i in listforeachi:\n",
        "  avinash = avinash + i\n",
        "\n",
        "\n",
        "\n",
        "res = pow(np.std(np.asarray(avinash)), 2)\n",
        "print(res)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6.920264704521265\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRuVNOuvdnau",
        "outputId": "d446d891-ba7f-493c-ca45-12ccce74cbdb"
      },
      "source": [
        "ssforall = np.sum(pow(x, 2) for x in avinash)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnLbeDmld5yj",
        "outputId": "7f9d6199-c1fc-406a-e560-eee5f5297db0"
      },
      "source": [
        "ssforall"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14685"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbDfNEUdd7xb"
      },
      "source": [
        "loay = [i/ssforall for i in ssforeachi]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNk7lJjNeBDE",
        "outputId": "a6012558-9b2a-4ab6-a00f-a822a30176b1"
      },
      "source": [
        "loay"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11855635001702418,\n",
              " 0.14014300306435137,\n",
              " 0.11270003404834865,\n",
              " 0.10275791624106231,\n",
              " 0.08089887640449438,\n",
              " 0.09662921348314607,\n",
              " 0.13585291113381,\n",
              " 0.12271024855294518,\n",
              " 0.08975144705481784]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsPypfpMcqDx",
        "outputId": "457571a3-68a1-4d83-8cae-93bcb7d334c3"
      },
      "source": [
        "pprint(varforeachi)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6.722199083715118, 7.454394002498956, 7.952519783423574, 7.005414410662223, 5.8775510204081645, 7.117867555185338, 4.958767180341525, 5.506872136609746, 6.739691795085381]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-U0InMcScVOw",
        "outputId": "566724fc-cab6-4b32-c6d6-7ad7086582b5"
      },
      "source": [
        "result = [i/res for i in varforeachi]\n",
        "print(result)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.971378895278855, 1.077183362311087, 1.1491641032499664, 1.012304400160492, 0.849324595425973, 1.028554232981142, 0.7165574428246623, 0.7957603316838304, 0.9739066470509562]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8vU02C_dmAU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2srKalAlZrbX"
      },
      "source": [
        "allvarfori = [x for x in elev]\n",
        "for i in range(len(allvarfori)):\n",
        "  allvarfori[i].append(cont[i])\n",
        "  allvarfori[i].append(sad[i])\n",
        "newtemp = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gR4s_RROdkaz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo1I3cwiZt8c"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2fTZAxNY13B"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Jr2q9elYxMd"
      },
      "source": [
        ""
      ],
      "execution_count": 21,
      "outputs": []
    }
  ]
}