gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exp_df = pd.read_excel('ea_test_experiment_analysis.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first = ['Q1', 'Q2', 'Q3', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q11', 'Q12', 'Q13']\n",
    "\n",
    "new = exp_df[first]\n",
    "new[new >= 1] = 1\n",
    "new.fillna(0, inplace=True)\n",
    "new['num_responses'] = new.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section, the task is it to create a follow-up proposal test found in Section 3. I mentioned earlier that there were problems with the collection of data and that the responses may be unreliable. The below plot shows that certain questions were answered less than half of others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9795b00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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47bST2Xffp3Lwwa+et8dvxSCwvL55XXj5Ko2qV73qr7j++n/j9NNPYWxsbF6HAMxxEMzU\nT5yZm8rLzwFuzsxPTrrNEuBa4GmZuXFz99+3l2GSNNXrXncoRx31Jj7zmQvn/bFnffvoDP3EG4HV\nEbE4Ii4HVk65zUuAK4E9asgsSSNl06ZNrFlzNscd9z4+/OEzGRsbm9fHn8srgun6iVdFxDrgXOAk\n4OVTbvMg8ELgR1UFlaT5UuUy7HO5r/PPP5fly1ewcuXB/PrXd3L++efx9re/q7IMs5nLIFjKzP3E\n92dmRsRBky/IzO8ARMSCoRNK0jxauvRJfPS4V1Z+n5vzjnf8zcP/PvzwIyt97LmYyyDYbD/xLLcd\nHzCXGtClsvqpupobup0dRjP/nns+s4EkzZnLIJixnzgz75vltr4i6JCulNVP1eU+hS5nB/M3raoh\nPOvB4s30Ey+MiF1mubmvCCSp5eb09tHN9BNvLC8/ZYbb2XUoSS03THn9QP3E07EovXl+D6T+asWZ\nxZbXN2siv2X1Uj+1YhBYXt+srueXNByLaSSp5xwEktRzDgJJ6rkF4+O+1V+S+sxXBJLUcw4CSeo5\nB4Ek9ZyDQJJ6zkEgST3nIJCknmt0iYmywezvgacD9wNHZOa6JjNNFRHPAc7MzBdExD7A54CHgJsy\n85jyOkcCbwE2Aadl5tcj4jHARcDuwG+AQzPzrnnMvQ3wWYqGuYXAacDPOpR/K+BTQJR53wY80JX8\nZa7dgeuBF1HUt3Yp+4+A9eWHvwBO71j+44FXAttSPMdc3ZX8EXEocBjFMv7bUzw/7g98pK78Tb8i\nOBjYLjP3A04Azmk4z6NExHEUT0bblZvOAd6XmQcAW0XEn0fEHsA7gOdRLNV9RkRsCxwF/CQzVwAX\nAifOc/zXA78uH/9lwHkdy78SGM/M5eVjn96l/OUgPh/YUG7qUvbtADLzwPK/N3cs/wHA88rnlecD\ne3cpf2ZekJkvyMwDKXrf3wl8oM78TQ+C5cA3ADLzh8Czm43z//wc+ItJHz8rM68p/30F8GLgT4Fr\nM3MsM38D3EIxwR/+3Mrrvmh+Ij/sn3nkB2BrYAx4ZlfyZ+ZXKf7SAXgicA8dyg98GPg48CuKpr4u\nZX86sENEXBkR3y5fFXcp/0uBmyLiK8DXgMvoVn4AIuLZwB9n5qep+bmn6UHwWB55+QkwVu4SaIXM\nvITiCXTC5OrN31Lk34lHfw6/A3aesn3iuvMmMzdk5u8jYifgYuD9dCg/QGY+FBGfA9YAX6Aj+SPi\nMOCOzPwWj2Se/HPd2uylDcBZmflSir8u/5GOfO1Li4FnAa/mkfxd+vpPOAE4eZrtledv+kn3NxSh\nJ2yVmQ81FWYOJmfbCbiX4nN47JTt9/Doz23iuvMqIvYCrgIuyMwv0bH8AJl5GLAM+DTF/tIJbc5/\nOPDiiPguxV9onweWTMnY1uwAaymePMnMWyi6yveYdHnb898FXFn+pbyW4vjjzpMub3t+ImJnYFlm\nXl1uqvV3t+lB8K/AQQAR8Vzgp83GmdWPI2JF+e+XA9cA1wHLI2Jh+c17CnAT8H3Kz638/zVT76xO\n5f7DK4H3ZuYF5eYbOpT/9eUBPyh+kR8Eri/3/0KL82fmAeU+3hcANwJvAK7oytceeBNwNkBE/AHF\nk803u/C1L11LWa1b5t8B+E6H8gOsAL4z6eNaf3cbXXRu0ruG/qTcdHg5wVsjIp4IfDEz94uIJ1Mc\nPN4W+E/gyMwcj4g3A2+lePl8WmZ+JSK2By4AHk/xbpdDMnPe+iAj4iPAXwM3l7nGgWOBczuSfxHw\nD8CeFO9uO6P8XD7dhfyTPo+rKN7xNE53fna2pfjaP5HiL9H3UvyV3ZmvfUScCRxY5joBuLVj+d8D\nbMzMNeXHtT73uPqoJPVc07uGJEkNcxBIUs85CCSp5xwEktRzDgJJ6jkHgST1nINAknrOQSBJPfd/\n23hdGfHu7cEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x979ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(new.sum().sort_values())\n",
    "df.columns = ['x']\n",
    "df.drop(df.tail(1).index, inplace=True)\n",
    "df.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below plots show that most people answered 7/12 or 9/12 questions. I would suggest asking less questions to possibly get a more even amount of responses across all questions. Based on the previous task, I would keep the 3 responses of Yes, No, and Other as it simplifies the process. I would also conduct this experiment sometime inbetween the enrollment end/start to see if there is more separation of knowledge between the treatment groups. With more time away from the deadlines, healthcare may not be on the minds of people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000009BE6EB8>]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+A5wBzANmAMsi4quZuW3spUuSxmK0WwD7A38QEbcDU4B3A/Myc2n9+K3Aq6m2BpZl5nZg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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9bcc780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new.hist('num_responses', bins=13, range=(0,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0     2007\n",
       "9.0     1985\n",
       "2.0      610\n",
       "1.0      403\n",
       "4.0      316\n",
       "3.0      250\n",
       "8.0      160\n",
       "6.0      156\n",
       "5.0      142\n",
       "10.0     136\n",
       "0.0        2\n",
       "Name: num_responses, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.num_responses.value_counts()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}