survey.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "responses_df = pd.read_csv(\"responses.csv\")\n",
    "responses_df = responses_df.drop_duplicates([\"email\"], keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "figs_dir = \"images\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of unique participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "699"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "responses_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_groups = [\"Under 20 years old\", \"20 - 29 years old\", \"30 - 39 years old\", \"40 - 49 years old\", \"50 - 59 years old\", \"60 years or older\"]\n",
    "y1 = [sum(responses_df[\"age\"] == i) for i in age_groups]\n",
    "y2 = [(val/responses_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xticks(rotation=-20, ha=\"left\")\n",
    "\n",
    "ax1.set_xlabel('Age')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(age_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "ax2.set_ylabel('Number of participants')  \n",
    "ax2.bar(age_groups, y1)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Age.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dlnUy1mlBPQ3cKmku1dr1ANg+fCg1jYiIceXGspzHv1CN4HsS+FmdjHUC1JzyiIiIGBTbHyub35J0ObB2mUF9QHVG8c1ekcpFRMT4JundwJuoBthdCwxPgJI0DTiBlw4RrNWHGBER45ekfwZeBZxdkj4q6a22Dxsob50uvu8AxwInA7sCB9N5Tr2IiIi+dgFe31oWXtJsYEGdjHVG8U2yPReQ7fttH0eNpXojIiKAXwCbt+1vxnB18QHPSFoFuEfSx4FfAxsNuooRETEebQDcJak1cu+PgeslzQGwvU+3jHUC1CepVj88HDieqvV00ApVNyIixovPDzVjnVF88wFKK+pw208M9cUiImJ8sf3joeYd8BqUpOmSFlD1GS6Q9POyznxERETP1OniOwP4mO2fAEh6E9XIvq17WbGIiBjf6ozie6IVnABsXwukmy8iIroq0+Mh6UtDLaNOC+pnkk6jusnKwPuAeZK2B7B981BfPCIixqwpknYB9pF0Dn3un60TO+oEqG3L87F90v+UKmDlnqiIiOjr88DRwKbAV/ocqxU76ozi23VIVYuIiHHL9vnA+ZL+1vbxQymja4CS9AHb/yrpyC4v3jciRkRELMf28ZL2Ad5SkubZvqRO3v5aUJPL81orUrmIiBi/JJ0A7AicVZKOkLSz7WMGyts1QNk+rTx/YVhqGRER49HewLZlVd3WZLG3AAMGqDo36s4uqyG29teTdMYKVDYiIsaXddu216mbqc4ovq1tP9rasf2IpO0GU7OIiBi3TgBukXQ11VDzt1Cj9QT1AtQqktaz/QiApPXr5JO0BnANsHo5/3zbx0raAjgHWB+4Gfig7efqVDYiIkYX22dLmkc1i7mAo2z/T528dQLUScB1ks4v+wcAf18j37PAbraflLQacK2kfweOBE62fY6kbwGHAt+sU9mIiBh9bC8G5gw234DXoGyfCbwHeAhYArzb9vdq5LPtJ8vuauXRujmrFexmA+8abKUjImLs6+8+qLVtP1669P4H+H7bsfVtPzxQ4ZJWBW6iWo/+G8AvgUdtLy2nLAI2WYH6R0TEGNVfF9/3gXdQBRi3pavsbzlQ4bafB7YtowAvAl7X6bROeSXNBGYCTJw4caCX6mrq0ZcOOe9Ycd+Je490FSJiHCrrCN5m+/VDyd+1i8/2O8rzFra3bHtsYXvA4NSnrEeBecBOwLqSWoFxU+DBLnlm2Z5ue/qECXUulUVExGBJ2kzS1ZLuknSHpCNK+vqSrpJ0T3ler6RL0tckLZR0W2vi8E7KvU8/l7T5UOpW5z6ouXXSOpzz8tb9U5ImAW8F7gKuBvYvpx0EXDyYCkdExLBaCnza9uuoGhGHSdqKaqLXubanAXPLPsCewLTymMnAg9ymAHdImitpTutRp2L9XYNaA1gT2LBEztZU6WsDr6hR9hRgdrkOtQpwnu1LJN0JnCPpi1R3E59ep6IRETH8ygi7xWX7CUl3UY0N2BeYUU6bTdULdlRJP9O2gZ9KWlfSlFJOJ0Oejai/vrOPAp+kCkY3sSxAPU414KFftm8DXnJDr+17qeZlioiIlWOCpBvb9mfZntX3JElTqb63bwA2bgUd24slbVRO2wR4oC1ba7BbxwBl+8eSXglMs/0fktYEVq1V6W4HbJ8i6VTgs0OdKj0iIhphqe3p/Z0g6WXABcAnywjurqd2SOs42K2U+/+pugLXB/6QKph9C9h9oEr3ew2qjMLba6BCIiJi9CqTKVwAnGX7wpL8kKQp5fgUqvtgoWoxbdaWvetgt+IwYGeq3jds3wNs1M/5LxpwkARwpaT3qJ9wGhERo1P5bj8duKvPOn9zqAaywfID2uYAHyqj+XYCHuvn+hPAs+3T2ZVR3F1bXO3qjN8+kmptqKWSnqHcB2V77TovEBERjbYz8EFggaRbS9pngROB8yQdCvyKapo7gMuoetYWAk8DBw9Q/o8lfRaYJOltwMeAH9apWJ0l37NgYUTEGGX7WjpfV4IO14nK6L3DBvESR1PNubqAavDdZcC362SsdQdsGWY+DVijrZLXDKKCERExDtl+oSxSeANV194vSpAbUJ1lMz4CHEF1IexWqhu5rqea9DUiIqIrSXtTjdr7JVVLbQtJH7X97wPlrTNI4giqdTzut70r1Rj536xAfSMiYvw4CdjV9gzbuwC7AifXyVgnQD1j+xkASavbvht4zZCrGhER48kS2wvb9u9l2ZD1ftW5BrWozKn3A+AqSY/Q/5j3iIgY5yS9u2zeIeky4Dyqa1AHAPPrlFFnFN9+ZfO4sqb8OsDlg69uRESMI+9s234I2KVs/wZYr04BA00W+xdUiw0uAE63/eOh1TMiIsYT2wPdHzWg/lpQs4HfAz+hml59K6oBExEREbVI2gL4BDCVtphje5+B8vYXoLay/YbyAqcDP1uxakZExDj0A6qplH4IvDCYjP0FqN+3NmwvzVR8ERExBM/Y/tpQMvYXoLaR9HjZFtU8So+TufgiIqK+UyQdC1wJPNtKtH3zQBn7Ww+q1oJSERER/XgD1WS0u7Gsi8/UmI2o1lx8ERERQ7QfsGX7kht11ZlJIiIiYqh+Dqw7lIxpQUVERC9tDNwtaT7LX4NaoWHmERERK+rYoWZMgIqIiJ5ZkRmIEqAiIqJnJD1BNWoPYCKwGvBUnVuVEqAiIqJnbK/Vvi/pXcCOdfL2bBSfpM0kXS3pLkl3SDqipK8v6SpJ95TnWrPaRkTE6Gf7B9Rckb2XLailwKdt3yxpLeAmSVcBHwbm2j5R0tHA0cBRPaxHRESMkLZ1oaBqFE1nWZdfv3oWoGwvBhaX7Sck3QVsAuwLzCinzQbmkQAVETFWta8LtRS4jyoODGilXIOSNBXYDrgB2LgEL2wvlrRRlzwzgZkAEydOXBnVjIiIYbYi60L1PEBJehlwAfBJ24/XnRXd9ixgFsDkyZNrNQcjIqIZJH2+n8O2ffxAZfR0qiNJq1EFp7NsX1iSH5I0pRyfAizpZR0iImJEPNXhAXAoNS/r9KwFpaqpdDpwl+2vtB2aAxwEnFieL+5VHSIiYmTYPqm1XQbKHQEcDJwDnNQtX7tedvHtTDXF+gJJt5a0z1IFpvMkHQr8Cjigh3WIiIgRIml94Ejg/VSD4ra3/Ujd/L0cxXct1eKGnezeq9eNiIiRJ+kfgXdTjSV4g+0nB1tGltuIiIhe+DTwCuBzwIOSHi+PJ9pWa+9XpjqKiIhhZ3uFG0BpQUVERCMlQEVERCMlQEVERCMlQEVERCMlQEVERCMlQEVERCMlQEVERCMlQEVEjHOSzpC0RNLtbWkdVz9X5WuSFkq6TdL2vapXAlRERHwXeHuftKOpVj+fBswt+wB7AtPKYybwzV5VKgEqImKcs30N8HCf5H2pJnilPL+rLf1MV34KrNtaQmm4JUBFREQny61+DrRWP98EeKDtvEUlbdhlLr6IiLFvgqQb2/ZnlVXLh6LTKhU9WfU8ASoiYuxbanv6IPM8JGmK7cV9Vj9fBGzWdt6mwIPDUcm+0sUXERGdtFY/h+VXP58DfKiM5tsJeKzVFTjc0oKKiBjnJJ0NzAA2lLQIOJbuq59fBuwFLASeplrGvScSoCIixjnbB3Y59JLVz20bOKy3Naqkiy8iIhopASoiIhopASoiIhopASoiIhopASoiIhqpZwFqMLPjRkRE9NXLFtR3qT87bkRExHJ6FqAGOTtuRETEclb2Nahus+O+hKSZkm6UdOPSpUtXWgUjIqIZGjtIwvYs29NtT58wIRNeRESMNys7QD3UWtiqz+y4ERERy1nZAarb7LgRERHL6eUw87OB64HXSFpUZsQ9EXibpHuAt5X9iIiIl+jZxZ3BzI4bERHRV2MHSURExPiWABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY2UABUREY00YaQrEBExkKlHXzrSVRhR952490hXYUSkBRUREY00IgFK0tsl/ULSQklHj0QdIiKi0tTv5JUeoCStCnwD2BPYCjhQ0lYrux4REdHs7+SRaEHtCCy0fa/t54BzgH1HoB4REdHg7+SRGCSxCfBA2/4i4E/6niRpJjCz7FrS71ZC3XphArB0JCugL43kqw+LEX0Px8D7Bw34HI5yo/0zOEnSjW37s2zPKtu1vpNHwkgEKHVI80sSqjdvVodzRxVJN9qePtL1GM3yHq64vIcrZoy/f7W+k0fCSHTxLQI2a9vfFHhwBOoREREN/k4eiQA1H5gmaQtJE4E/B+aMQD0iIqLB38krvYvP9lJJHweuAFYFzrB9x8qux0o06rspGyDv4YrLe7hixuz71+TvZNmN6GqMiIhYTmaSiIiIRkqAioiIRkqA6oek5yXd2vaY2sPX+rCkU3tVftNIsqTvte1PkPQbSZcMkG/GQOeMdZI2lXSxpHsk/VLSKZImStpW0l5t5x0n6TMjWM/W38/tkv5N0pqDzP9mSXeUMib1qp59XnOGpD8d7LHojQSo/v3O9rZtj/tGukJjyFPA69u+eN4G/HoE6zMqSBJwIfAD29OAVwMvA/4e2BbYq5/sg32tVVewiNbfz+uB54C/6FO+JPX3HfR+4J9KGQPeqF+jvDpmAN2CUH/HGk/SqFu9IgFqkCStKukfJc2XdJukj5b0GZJ+LOk8Sf8l6URJ75f0M0kLJP1hOe+dkm6QdIuk/5C0cYfXeLmkC8przJe088r+OVeSfwda6wgcCJzdOiBpR0nXlffpOkmv6ZtZ0mRJZ5T36BZJjZiepcd2A56x/R0A288DnwI+AnwZeF9pcbyvnL+VpHmS7pV0eKsQSR8on81bJZ3WCkaSnpT0d5JuAN44jPX+CfAqSVMl3SXpn4Gbgc0k7SHpekk3l5bWyyR9BHgv8HlJZ5W6/VXb390XStpLymt/UUn3SfpCKXuBpNeW9PUl/aCU9VNJW5cekr8APlXelze3lfOSY5JeKWluKWOupM37/tDdPseqekwulHS5qpbwl0v6qpK+W1qdCyR9StJGkm4qx7dR1fuwedn/paQ1u31nqGpFz5J0JXDmcPwiVyrbeXR5AM8Dt5bHRSVtJvC5sr06cCOwBdV/V48CU0r6r4EvlPOOAL5attdj2ejJjwAnle0PA6eW7e8DbyrbmwN3jfR70YP39klga+B8YI3yHs8ALinH1wYmlO23AheU7fZz/gH4QNleF/gvYPJI/2w9ft8OB07ukH5LOXZqW9pxwHXl87gh8FtgNeB1wA+B1cp5/wx8qGwbeO9w/Y7L8wTgYuAvganAC8BO5diGwDWt3xtwFPD5sv1dYP+yvQfVUG9R/WN9CfCWvuV1qMN9wCfK9seAb5ftrwPHlu3dgFvb3rPPdClruWPlPTyobB9C1artm6fb5/jDwL3AOuXzfz9VcN0BuKot/7rl+Y5S1sep7lt6P/BK4PpyvON3RqnzTcCkkf7sDuUx6pp8K9nvbG/bJ20PYGtJ+5f9dYBpVF0Y820vhuo/G+DKcs4CYNeyvSlwrqQpwETgvzu87lup/vNt7a8taS3bTwzDz9QYtm8r/5keCFzW5/A6wGxJ06i+NFfrUMQewD5adp1lDcofZ08q3Ayi8zQ03dIvtf0s8KykJcDGwO5UX4Tzy2dsErCknP88cMEw1XWSpFvL9k+A04FXAPfb/mlJ34lqBu3/LHWZCFzfoaw9yuOWsv8yqr+7X/Upr5MLy/NNwLvL9puA9wDY/pGkDSStM7gfjze2lfc9qhZsX/19jufafgxA0p1UAecOYEtJXwcuZdl3yHXAzlRB+R+At1P9zn9Sjnf8zijbc1yji7SJEqAGT1T/kV2xXKI0A3i2LemFtv0XWPZefx34iu05Jc9xHV5jFeCNo/VDNUhzgH+iahlt0JZ+PHC17f1KEJvXIa+A99j+RW+r2Ch3UL5YWyStTfXf9/Mdzm//TD5P9TkUMNv2MR3Of8ZVt+FweMk/eOUL9Kn2JKoWw4EDlCXgBNun9Slvap/yOmm9B62fv1VeXyt6U2in/D0n5mwAAAN6SURBVP19jl/yu7H9iKRtgD8DDqPq5jyEKhC9mSqIXUzV0jRVSxK6fGd0eL9HlVyDGrwrgL+UtBqApFdLmjyI/OuwbDDAQV3OuZKqKU95jb6tuLHkDODvbC/ok97+Pn24S94rgE+o/BVK2q4nNWyWucCakj4ELw5kOImqO+whYK3uWZcrY39JG5Uy1pf0yt5Ud0A/BXaW9KpSlzUlvbrDeVcAh0h6WTlvk1b9h+gaqm6y1j+X/2v7ceAJur+HfY9dRzUtEKWsazvkqfM5fpGkDYFVbF8A/C2wfVt9PwDcY/sF4GGqATH/WY6Pye+MBKjB+zZwJ3CzpNuB0xhcS/Q44N8k/QT43y7nHA5MLxdf76TP6KexxPYi26d0OPRl4ARJ/0k1/Uonx1N1mdxWfhfH96iajeHqwsJ+wAGS7qG67vYM8FngaqpunvZBEp3KuBP4HHClpNuAq6iuna50tn9D9cV9dqnLT4HXdjjvSqrrLNdLWkB17bJOMO7mOMrfGHAiy/5Z/CGwX99BEl2OHQ4cXMr4INW15r7qfI7bbQLMK12j3wWOAfCyEcTXlOdrgUdtP1L2a31nSHqFpL7d6Y2VqY4iIqKR0oKKiIhGSoCKiIhGSoCKiIhGSoCKiIhGSoCKiIhGSoCKcU3SxpK+r2quuptUzQm33zCUO+5nXY9YUQlQMW6VG3x/AFxje0vbO1DdeLnpCNQls7pE9JEAFePZbsBztr/VSrB9v+2vq/9Z6+dJOl/S3ZLOapvJ4u0l7VqWzdHWddZ1VTNa/5ukH7JszrWIKPJfW4xnf0S1REMnhwKP2f5jSatTTWbaCiLblbwPUk01s7OkG4F/oQp6C4Fz28r6G+BHtg+RtC7wM0n/UY69Edja9sPD+YNFjAUJUBGFpG9QzXL9HNXyB91mrf+Z7UUlz61USz48Cfy37XtK+r9SLc0C3Wddh2qi1ASniA4SoGI8W25mcNuHlck6b6RaxqHOrPXtM2R3mzes46zrkv6EUTzTdESv5RpUjGc/AtaQ9JdtaWuW58HOWn83sIXKyslUa1y1jMdZ1yNWWAJUjFtlZvB3AbtI+m9JPwNmU621M6hZ620/Q9Wld2kZJHF/2+FxN+t6xHDIbOYREdFIaUFFREQjJUBFREQjJUBFREQjJUBFREQjJUBFREQjJUBFREQjJUBFREQj/R8QRHbanbuytwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_groups = [\"Female\", \"Male\", \"Other\", \"Prefer not to answer.\"]\n",
    "y1 = [sum(responses_df[\"gender\"] == i) for i in gender_groups]\n",
    "y2 = [(val/responses_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Gender')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(gender_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "ax2.set_ylabel('Number of participants') \n",
    "ax2.bar(gender_groups, y1)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Gender.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Education\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "education_groups = [\"Less than a high school diploma\", \"High school diploma\", \"Bachelor's degree\", \"Master's degree\", \"Doctorate\"]\n",
    "y1 = [sum(responses_df[\"education\"] == i) for i in education_groups]\n",
    "y2 = [(val/responses_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xticks(rotation=-20, ha=\"left\")\n",
    "\n",
    "ax1.set_xlabel('Education')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(education_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "ax2.set_ylabel('Number of participants')  \n",
    "ax2.bar(education_groups, y1)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Education.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of personal computers groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "responses_df[\"pc_num\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc_num_groups = [0, 1, 2, 3, 4]\n",
    "y1 = [sum(responses_df[\"pc_num\"] == i) for i in pc_num_groups]\n",
    "y2 = [(val/responses_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Number of owned personal computers')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(pc_num_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "ax2.set_ylabel('Number of participants') \n",
    "ax2.bar(pc_num_groups, y1)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Num_of_PC.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main personal computer usage time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_time_df = responses_df[responses_df[\"pc1_usage_time\"].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_time_df[\"pc1_usage_time\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_time_groups = range(25)\n",
    "y1 = [sum(pc1_time_df[\"pc1_usage_time\"] == i) for i in day_time_groups]\n",
    "y2 = [(val/pc1_time_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Number of hours main personal computer is used in a day')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(day_time_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Number of participants') \n",
    "ax2.bar(day_time_groups, y1)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Main_PC_Time.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main personal computer OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_os_df = responses_df[responses_df[\"pc1_usage_time\"].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "os_groups = [\"Windows\", \"macOS\", \"Linux\", \"Other\"]\n",
    "y1 = [sum(pc1_os_df[\"pc1_os\"] == i) for i in os_groups]\n",
    "y2 = [(val/pc1_os_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Operating Systems')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(os_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Number of participants') \n",
    "ax2.bar(os_groups, y1)\n",
    "ax2.tick_params(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main personal computer CPU utilization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_cpu_df = responses_df[responses_df[\"pc1_cpu\"].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_cpu_df[\"pc1_cpu\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpu_groups = range(0, 100, 10)\n",
    "x_ticks = [\"0\", \"<=10\", \">10 and <=20\", \">20 and <=30\", \">30 and <=40\", \">40 and <=50\", \">50 and <=60\", \n",
    "           \">60 and <=70\", \">70 and <=80\", \">80 and <=90\", \">90 and <=100\"]\n",
    "y1 = [sum((pc1_cpu_df['pc1_cpu'] == 0))]\n",
    "for i in cpu_groups:\n",
    "    y1.append(sum((pc1_cpu_df['pc1_cpu'] > i) & (pc1_cpu_df['pc1_cpu'] <= i+10)))\n",
    "y2 = [(val/pc1_cpu_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xticks(rotation=-30, ha=\"left\")\n",
    "\n",
    "ax1.set_xlabel('Main computer CPU utilization')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(x_ticks, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "ax2.set_ylabel('Number of participants')  \n",
    "ax2.bar(x_ticks, y1)\n",
    "ax2.tick_params(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All personal computers time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_time_df = responses_df[responses_df[\"pc1_usage_time\"].notna()][\"pc1_usage_time\"]\n",
    "pc2_time_df = responses_df[responses_df[\"pc2_usage_time\"].notna()][\"pc2_usage_time\"]\n",
    "pc3_time_df = responses_df[responses_df[\"pc3_usage_time\"].notna()][\"pc3_usage_time\"]\n",
    "pc4_time_df = responses_df[responses_df[\"pc4_usage_time\"].notna()][\"pc4_usage_time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_time_df = pd.concat([pc1_time_df, pc2_time_df, pc3_time_df, pc4_time_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_time_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_time_groups = range(25)\n",
    "y1 = [sum(pc_time_df == i) for i in day_time_groups]\n",
    "y2 = [(val/pc_time_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Number of hours main personal computer is used in a day')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(day_time_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Number of participants') \n",
    "ax2.bar(day_time_groups, y1)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Main_PC_Time.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All personal computers CPU utilization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_cpu_df = responses_df[responses_df[\"pc1_cpu\"].notna()][\"pc1_cpu\"]\n",
    "pc2_cpu_df = responses_df[responses_df[\"pc2_cpu\"].notna()][\"pc2_cpu\"]\n",
    "pc3_cpu_df = responses_df[responses_df[\"pc3_cpu\"].notna()][\"pc3_cpu\"]\n",
    "pc4_cpu_df = responses_df[responses_df[\"pc4_cpu\"].notna()][\"pc4_cpu\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_cpu_df = pd.concat([pc1_cpu_df, pc2_cpu_df, pc3_cpu_df, pc4_cpu_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.544117647058822"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_cpu_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpu_groups = range(0, 100, 10)\n",
    "x_ticks = [\"0\", \"<=10\", \">10 and <=20\", \">20 and <=30\", \">30 and <=40\", \">40 and <=50\", \">50 and <=60\", \n",
    "           \">60 and <=70\", \">70 and <=80\", \">80 and <=90\", \">90 and <=100\"]\n",
    "y1 = [sum((pc_cpu_df == 0))]\n",
    "for i in cpu_groups:\n",
    "    y1.append(sum((pc_cpu_df > i) & (pc_cpu_df <= i+10)))\n",
    "y2 = [(val/pc_cpu_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xticks(rotation=-30, ha=\"left\")\n",
    "\n",
    "ax1.set_xlabel('Main computer CPU utilization')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(x_ticks, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "ax2.set_ylabel('Number of participants')  \n",
    "ax2.bar(x_ticks, y1)\n",
    "ax2.tick_params(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All personal computer OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc1_os_df = responses_df[responses_df[\"pc1_os\"].notna()][\"pc1_os\"]\n",
    "pc2_os_df = responses_df[responses_df[\"pc2_os\"].notna()][\"pc2_os\"]\n",
    "pc3_os_df = responses_df[responses_df[\"pc3_os\"].notna()][\"pc3_os\"]\n",
    "pc4_os_df = responses_df[responses_df[\"pc4_os\"].notna()][\"pc4_os\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_os_df = pd.concat([pc1_os_df, pc2_os_df, pc3_os_df, pc4_os_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "os_groups = [\"Windows\", \"macOS\", \"Linux\", \"Other\"]\n",
    "y1 = [sum(pc_os_df == i) for i in os_groups]\n",
    "y2 = [(val/pc_os_df.shape[0])*100 for val in y1]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Operating Systems')\n",
    "ax1.set_ylabel('Participants (%)')\n",
    "ax1.bar(os_groups, y2)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Number of participants')  # we already handled the x-label with ax1\n",
    "ax2.bar(os_groups, y1)\n",
    "ax2.tick_params(axis='y')"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}