gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Were taking a look at data concerning printers in hospitals. Where are these printers, what types of printers are they, how are they being used. From a sales perspective, we can use this to become more informed on the printing needs of such a hospital. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import collections\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, lets count up our printers by department..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Laboratory          70\n",
      "NICU                36\n",
      "Patient Accounts    35\n",
      "Accounting          34\n",
      "IT                  29\n",
      "Name: Area (Department), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel('auxillio_data.xlsx',sheetname=0,header=4)\n",
    "departments = df['Area (Department)'].value_counts()\n",
    "print departments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well thats great that those departments have so many printers. But are they using them?\n",
    "Lets find the average for meter count for each department..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Department  Average Meter Total  # of Printers\n",
      "27                Copy Center         1.629779e+06              4\n",
      "100                Accounting         8.755020e+05             34\n",
      "37      Facilities and Safety         8.558115e+05              3\n",
      "59         Business Solutions         6.399690e+05              1\n",
      "6                  Admissions         4.977080e+05              7\n",
      "42                 Foundation         4.797487e+05             16\n",
      "0             Medical Records         4.317000e+05             10\n",
      "57                  Admission         3.468930e+05              5\n",
      "35           Progressive Care         3.252120e+05              6\n",
      "69       Emergency Department         3.182830e+05             17\n",
      "28         Ambulatory/Surgery         3.127352e+05              8\n",
      "101                 Cafeteria         2.673500e+05              3\n",
      "70   Executive Administration         2.180900e+05              5\n",
      "13              Dental Clinic         2.061820e+05              5\n",
      "44        Ambulatory/Surgical         1.976930e+05              5\n",
      "99            Medical Surgery         1.923200e+05              7\n",
      "36         Clinical Nutrition         1.852930e+05              3\n",
      "74        Quality Improvement         1.775970e+05              4\n",
      "80           Accounts Payable         1.732870e+05              3\n",
      "43                         ED         1.609085e+05              6\n"
     ]
    }
   ],
   "source": [
    "#dropping entries with no entry for Meter Total\n",
    "df = df.replace(0,np.nan)\n",
    "df = df.dropna(subset = ['Meter Total'])\n",
    "\n",
    "#calculate average meter total for each category\n",
    "department_meter_counts = collections.defaultdict(list)\n",
    "for index, row in df.iterrows():\n",
    "    department = row['Area (Department)']\n",
    "    meter = row['Meter Total']\n",
    "    department_meter_counts[department].append(meter)\n",
    "\n",
    "printer_use = []\n",
    "\n",
    "department_averages = {}\n",
    "for department,meter_counts in department_meter_counts.iteritems():\n",
    "    avg = reduce(lambda x, y: x + y, meter_counts) / len(meter_counts)\n",
    "    no_printers = departments[department]\n",
    "    printer_use.append([department,avg,no_printers])\n",
    "    #department_averages[department]=avg\n",
    "\n",
    "printer_use_df = pd.DataFrame(averages_df,columns=('Department','Average Meter Total','# of Printers'))\n",
    "print printer_use_df.sort_values('Average Meter Total',ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So I think we are seeing some real results with how certain departments use the printers more commonly than other departments. Accounting for instance has an average meter total of 875,000 over a total of 34 printers. This high value doesn't seem like an outlier to me. \n",
    "\n",
    "What we really want to know is, Are those departments with all those printers actually using them? For any 1 department that has a lot of printers, are they using them on average less than a department with less printers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NE1YSwMzqgCXAp9z94ShtHnAbcCSwEvisuz8YO+ddhCn8+wGPAxe4+2ux/M8AlwNTgXuA\ni929M3a/mwkLRHYA17v7DbFzx3VvERERKWyZjhGa8PFBUVDyU+DAIVn3AeuAIwi73t9rZnOjc/YC\n7gW+DywAWqPjU9c8A7gKuAA4kbAx7LWxa18HHA4cD1wEXG1m8VWzs763iIiIFL6CmDVmZvOBxcC+\nQ9JPJLS2XOjB1wktL6lxSBcAT7r7t919OXAuMC9a9wjg08C33P237v4UcCFwvpnVm1kjcD7waXdf\n6u73E4Kki3N0bxERESlwmQZC5wJbJ7Ac7wR+R+iCigddC4GnU11ZkUej41L5D6cy3D0BPA0caWaV\nwDuAR2LnLgZqgUOir2pCcBO/9sLx3jujGouIiEjeZTRGyN1/OJGFcPdbUq/NLJ7VROiaimsB5maQ\nPx2oj+e7e5+ZbYryk0BrarXs2Ln1ZjZznPcWERGRIpDNpquTqRHoGpLWBdRlkN8Ye58uv3KEPGLn\nZ3vvjHV1ddHR0TGWUwpSIpEY9G+xK6X6lFJdQPUpZKVUF1B9CllX19A/v9kr9ECok+Fbe9QRZnil\n8ocGHnXAliiPEfI7CHVPl0eUP557Z6y5uZnm5uaxnFLQVq5cme8i5FQp1aeU6gKqTyErpbqA6lPq\nxhwImdkUd2+fiMKksZbhs8jmAM2x/Dlp8p8BNhGClTnACgAzqwJmRudXArPMrNLd+2PnJty9zczG\nc++MNTU1MX369LGcUpASiQQrV65k3rx5NDQ05Ls441ZK9SmluoDqU8hKqS6g+hSytra2nDUiZNMi\n9KyZneXuT+ekBKNbDFxhZnXunmoHO4YdA6AXR+8BiGaCHQZc5e5JM3syyk8Naj6KsCjkUsKg7B7C\nlPrHovxjgSfHee+rx1LBuro6Ghsbd35gkWhoaFB9ClQp1QVUn0JWSnUB1acQ5bJ7L5tA6E3AGzkr\nwegeAlYDd5jZl4FTCDPBPh7l/wC43Mw+B/yaEIS8mlqMkbBY4i1m9gJhYPPNwK2xBRXvjPLPIwxy\nvgz42Dju/Yq7P5Trb4KIiIhMjLFsupryb4SFBT9lZieb2XHxrxyUaWDxxqjL6v2ELqclwIeAU919\nTZT/OmFV6POAPxFmip0aO/8u4GvAd4EHCFPlr4jd61LgKeD3wE3AF6P1hLK992k5qL+IiIhMkmxa\nhP41+vemNHlJoCr74oC7Vw15/ypwwijHPwC8dZT8axm8mnQ8L0FYI+ncEfLHdW8REREpbNkEQvvu\n/BARERGRwjfmQCjqEsLMaglB0StAhbv35LhsIiIiIhMqm+nzFYRxN58mbFdxAPBVM3sD+KQCIhER\nESkW2QyWvgT4KGG39tS08vsIA4W/lJtiiYiIiEy8bAKhC4GL3f0OoB8GZmd9Avhw7oomIiIiMrGy\nCYT2Jf3qyUsZvtKyiIiISMHKJhBaSVhYcKj3Aq+OqzQiIiIikyib6fPfBG42syZCIHWSmf0dYfD0\npbksnIjIROvu6WPJ8hbWbmxnz92msGD+bGprxrUcmogUkWymz99uZjXAlUADYdXmjcCV7n5Ljssn\nIjJhunv6uOnuZ1mzYftA2uPPN3PJWYcqGBIpE2PuGjOzvYHvufvewO7AHHefDdxoZum6zERG1N3T\nx2PPreOe363gsefW0d3Tl+8iSRlZsrxlUBAEsGbDdpYsb8lTiURksmXTNfYaYVD0RndvjaXvS9io\ntLi3tJVJo0/jkm9rN7aPKV1ESk9GgZCZXQRcHr2tAJaY2dCP7rsCr+ewbFLiRvs0ftTBe+SpVFJO\n9txtypjSRaT0ZNoidAcwi9CVdhVwNxD/yJSM3v88l4WT0qZP45JvC+bP5vHnmwcF5HN3n8qC+bPz\nWCoRmUwZBULu3gFcA2BmSeCbUZpI1vRpXPKttqaKS846VLPGRMpYNrPG/sXMGszsHGA+YTr9QcAy\nd9+U6wJK6dKncSkEtTVV6ooVKWPZbLo6G3gcmA3UAbcRxg8tMLMT3P2l3BZRSpU+jYuISL5lM2vs\nemAZ8HYgNcf0HMK4oWuBU3JTNCkH+jQuIiL5lM0WGycCV8fHCLn7FkKr0DG5KpiIiIjIRMsmEJrK\n4BljKUmya2ESERERyYtsAqGHgU/G3idjW278MSelEhEREZkE2bTgXA48bGbHEwZLf4cwe2wX4J25\nK5qIiIjIxBpzi5C7LwcOAX4D/A/QD9wFHOruS3NbPBEREZGJk9WYHndfB3wxx2URERERmVSZ7jX2\ng0wv6O7nZV8cERERkcmTaYvQxwldYE8B2lpDRERESkKmgdA/A2cBbwN+BfwM+G9375mogomIiIhM\ntIwGS7v71939cOAw4AXgK8AGM/uBmb3bzLKZhi8iIiKSV2MKYNz9ZXf/irsfBBwLrAH+HWg2s5vN\n7LiJKKSIiIjIRMh6JWh3X0bYc+wqM/s7wj5jFwLaMVNERESKQtaBkJktAj4QfTUB/0dYT0hERESk\nKIwpEDKzI4Ez2RH8PAR8FfiFu2/KffFEREREJk6m6wh9GzidEPw8wo7gZ+MElk1ERERkQmXaIvRp\noBt4EFgPLAQWmtmwA7WgooiIiBSLTAOhh4Ek0ADsO3HFEREREZk8GQVC7n78BJdDREREZNJpIUQR\nEREpW1lPnxcZTXdPH0uWt7B2Yzt77jaFBfNnU1ujJaZERKSwKBCSnOvu6eOmu59lzYbtA2mPP9/M\nJWcdqmBIREQKirrGJOeWLG8ZFAQBrNmwnSXLW/JUIhERkfTGs7J0LWEG2StAhXail5S1G9vHlC4i\nIpIvYw6EzKwC+BphbaFa4ADgq2b2BvBJBUSy525TxpQuIiKSL9l0jV0CfBS4COiK0u4DTgO+lJti\nSTFbMH82c3efOiht7u5TWTB/dp5KJCIikl42XWMXAhe7+71mdhOAu99lZt3At4Av5LKAUnxqa6q4\n5KxDNWtMREQKXjaB0L7AM2nSlwJzxlccKRW1NVUcdfAe+S6GiIjIqLLpGlsJvCNN+nuBV8dVGhER\nEZFJlE2L0DeBm82siRBInWRmf0cYPH1pLgsnIiIiMpHGHAi5++1mVgNcSdiE9bvARuBKd78lx+UT\nERERmTBZrSPk7rcCt5rZLKDS3TfktlgiIiIiEy+bdYTOSZMGkAS6gTXAYnfvG3fpRERERCZQNi1C\nXyTMHKsEtkZpuxACoYrovZvZu919zfiLKCIiIjIxsgmEbgbOBT7s7s8DmNl84D+B7xMWV/wecC3w\noVwU0sxOBX7BjmArCfzc3c8ys3nAbcCRhBltn3X3B2PnvouwvtF+wOPABe7+Wiz/M8DlwFTgHsIa\nSZ1RXl1U39OBDuB6d78hdu6o9xYREZHCls30+UsJW2k8n0pw9+XAxcA/u3szYSD1u3NTRAAOBH5J\nWKdoDtAEfCLKux9YBxwB/Ai418zmApjZXsC9hABtAdBKCNSI8s8ArgIuAE4EFhECuJTrgMOB4wkr\naV9tZqfH8u8b6d4iIiJS+LJpEZrOji6xuA5gRvR6C2FGWa7MB5a5+8Z4opmdSOimWxi14nzdzE4C\nzgOuIQQ4T7r7t6PjzwXWm9lx7v4wYcr/t9z9t1H+hcD/mNnnCEHi+cDJ7r4UWGpm1xICvl9E994P\nWDTCvUVERKTAZdMi9AhwrZntkkows+nA14HHoqQzAB9/8QYcCKxIk74QeDrVlRV5lNBVlcp/OJXh\n7gngaeBIM6skLAz5SOzcxYSNZA+JvqoJ3Wnxay/M8N4iIiJS4LJpEboY+D2wxsycEEy9hdDt9B4z\nezchKPrbnJUSLLr2F4AqwlieqwhdZOuGHNsCpLqnRsufDtTH8929z8w2RflJoNXde4ecW29mMzO4\nt+RJd0+f9jkTEZGMZLOg4qvR4OizgcOAXuDfgJ+6e7eZdQIHuftLuSigme1N6GZLAGcSusJujNIa\nga4hp3QBddHr0fIbY+/T5VeOkEfs/NHunZGuri46OjrGckpBSiQSg/7Nl+6ePr5734usa31jIO2R\nZ1Zz4akHjikYKpT65EIp1QVUn0JWSnUB1aeQdXUN/fObvWwXVEwAt0dfA8ys3t1fz0XBYvdaZWYz\n3b0tSnrOzKoIg5NvB3YdckodYbwSQCfDA5M6whimztj7dOdXj5BHlN/JjjFR6e6dkebmZpqbm8dy\nSkFbuXJlXu//4uoEr6zZNijtlTWd/OoPSzlwr7EPW5vs+vT0JXl5XSebtvUwc1oNb9mjnpqqip2f\nmIF8P5tcU30KVynVBVSfUpfNgoozgS8ABxG6qSBMaa8jjOWZnrPSRWJBUMpyQrfWesJA6rg5QCqy\nWBu9H5r/DLCJEMzMIRp/FAVYM6PzK4FZZlbp7v2xcxPu3mZmawn1HeneGWlqamL69Jx/yyZdIpFg\n5cqVzJs3j4aGXI6TH5uXNrxGQ333sPSq+l2ZP3/fjK+Tj/rsaM0K5X9tYzfrttaMuTVrqEJ5Nrmi\n+hSuUqoLqD6FrK2tLWeNCNmuI3QS8CChq+qnhGDkcODzOSlVjJn9JfATYG5sYPJhhDFJjwCXm1md\nu6fayY5hxwDoxdH71LUao3OvcvekmT0Z5acGVB9FWB17KSG46yFMqU8NAj8WeDJ27StGuXdG6urq\naGxs3PmBRaKhoSGv9dl3z11Z8tLGYenz9tg1q3JNZn2efW4d6zcnqKzcMYdh/eYEL76+naMO3mPc\n18/3s8k11adwlVJdQPUpRLns3ssmEHoXcI67/8bMDga+6e7PmdmtwNtyVrIdHiN0N33PzK4B3kxY\n6+cbhABmNXCHmX0ZOIUwE+zj0bk/IARKnwN+DVwNvBpNnYcQ1N1iZi8QBj7fDNwaW1Dxzij/PMIg\n6MuAj0XnPrSTe0seLJg/m8efb2bNhu0DaXN3n8qC+bPzWKrMrN3YPqZ0EREZv2ymz08BnotevwQc\nGr2+CTghF4WKc/d24GRgN0JrzG3ALe5+fdRldQqhS2oJYSXrU1Nbe0TjlU4nrO3zJ0K33amxa98F\nfA34LvAAYar8FbHbXwo8RZgldxPwRXe/Pzq3H3j/SPeW/KitqeKSsw7lzJMO4KiD9+DMkw7gkrMO\nLYpZY3vuNmVM6SIiMn7ZtAitBfYhtIasAA6O0uMLKuZUtHL1ySPkvcooAZi7PwC8dZT8axm8mnQ8\nL0HYTuTcbO4t+VFbU5WTrqTJVsytWSIixSqbQOjnhO6gjwH/C/zMzBYTWlpezmXhRPKhu6ePJ15o\nYenybWzrb+HoQ/eelBalVGuW1kASEZk82QRCXwBqgH3c/Sdm9nPgbqCNMHhapGh19/Rx093Psmr9\nVhKdnby28TWeXrF50rrXirU1S0SkWGUzRugM4F/c/ScA7v73wCxgd3f/XS4LJzLZlixvGdQ1BbBm\nw3aWLG/JU4lERGQiZdMi9B+EaeJbUgnuvjlnJRLJI83cEhEpL9m0CK0gLKYoUnI0c0tEpLxk0yK0\nFPixmf0jYXD0oFWN3P28XBRMJB9SM7dWrd86kKaZWyIipSubQOgAdqyePHT7CpGilpq59cdnV7F0\n+UoOmT9v0maNiYjI5Mtm93mtmyMlrbamioVvm820ys3M1/R1EZGSltXu82bWQJgq/1bgOsKYoWXu\nvimHZRMRERGZUGMeLG1ms4EXgO8AnyNsW3E5sMzMRlzBWURERKTQZDNr7HpgGWHvr9RA6XOitLRb\nVYjkQndPH489t457freCx55bR3dPX76LJCIiRS6brrETgfe5e4eZAeDuW8zscuAPuSycSEpqxef4\nYoePP99cNBuqiohIYcqmRWgqkG51uSRZjjkS2Rmt+CwiIhMhm8DlYeCTwKXR+6SZ1QBXAn/MVcFE\n4kZa2XlVy3Z4bp02KRURkaxkEwhdDjxsZscDdYRB0/OBXYB35q5oIjukW9k5mYRnfSNPLGseSCvF\n7rLunj7tSC8iMkGyWUdouZkdQmgVWkfoXrsLuNndV+a2eCJBasXnePdYfV0Via4eKisqBtJS3WVD\nd3Av1mBCY6NERCbWmAMhMzsPuMvdvzgB5RFJK7XiczyYWbV+G0+8sH7YsUO70Yo5mBhtbNTQYE9E\nRMYum8HS3wbWm9kPzUyrTMukqa2p4qiD9+DMkw7gqIP3YO8509IeN7QbrZgHWo80NmqkdBERGZts\nAqHZwIVAurqnAAAgAElEQVTATOABM1tpZteY2X65LZrI6BbMn83c3acOSku3QWoxBxMj7Xo/UrqI\niIxNNmOEEsBPgJ+Y2W7AWcDZwOfN7DF314BpmRTpusvSjf0p5mAi3diodMGeiIhkZ7zr/mwFmoHV\nwKFoN3oZo/EOYk51l42mmIOJTIM9ERHJTrabrp4AfBg4g9C9djfwXnd/NIdlkxI3WYOYiz2YyCTY\nExGR7GQza2wtYZzQw8CngZ+7e0eU1xB1nYns1GTOiFIwISIi6WTTInQr8MP4mkFmdiDw98BHgBm5\nKZqUumIexCwiIqUhm8HS/wJgZrXAmYQA6CjCXmP35bR0UtKKeRCziIiUhmy6xvYnBD8fI0yhTwK3\nA//q7q/mtnhSyop5ELOIiJSGjAIhM6sCTiesH3QC0As8APwMuAO4QUGQjNVYBzEX6zYZIiJSuDJt\nEVpD2FT198AFwL3uvgXAzH44QWWTMpDpIOZi3iZDREQKV6YrS+8CtACvA5uBjgkrkUgaS5a3sLpl\nO+2JHrZs66I90cPqluLYJkNERApXpi1CswmrR59H2HV+u5ndT9h1PjlBZRMZsGr9dlo2d9DZ3Ucy\nmaSiooL22h5WtWznqCHHqgtNREQylVEg5O7bgduA28xsPiEg+kj0lQQ+a2bfcPc/T1hJpSRlGrS0\nJ7rp6OolmYzi7mSSjq4k7R3dw66nLjQREclUNtPnlwP/aGb/BLwP+DhwDnCumT3o7u/NbRGlVI0W\ntACDAqTWtsTwtsckbN7WOShpMhdpFBGR4pf1XmPu3gf8EvhltPnqRwlBkUhGlixvYVXLNjo6e+nu\n6aO2popVLdtYvKyZJ18cHNBse6ObqkpIUjnQNVZZAVWVg4e5aZFGEREZi/FuugqAu28Eboi+RDKy\nav02NmxJ0NPTD8AbiV7aEz0sXtZMc+sbg46tqKigqqqS0DNWAUBNTSWHHbAbjz23bqDlaPaMxrT3\n0iKNIiKSTk4CIZGhMhn7k+jqpau7l76+JP1JqKyA/mQ/m7Z2DrvelIYaGuqq6ezupaenn5qaSt68\n53SWvLSB5tYdrT1Ns6awx6wprIulaZFGEREZiQIhyblMBywnk0l6encM/OlPQn9vkqmNNWza2jfo\nmhUVcOZJb6G6qnIguOrt6+fe/xs8Pr+5tZ3Tjt9/0HGpICjecqSZZCIiAgqEZAJkOmB52aub0p7f\n2pZgr9nThm29sejtTYOCl3t+tyLt+es2trP3nGkD77t7+vjuvc9rJpmIiAyjQEhybqSByatatkOs\nVWbbGz1pj3ujszejrTfSjfvpTyZ59uWNPPHC+oG0Xz7yComuXiorKgbSNJNMRERAgZBMgD13m0JP\nXz8btyTo7umntqaSWdMbeNY38sSy5oHjenv7qWDwrPgKYJ85UzPaeiPdpq0NtTV0dvUSi3lY39pB\nTU0lUxpqBp2vmWQiIqJASHJuvz2nsbqlnf7+EOL09vWzumU7e82eQm31jladN72pmm0dlfT29Q+k\n1dZUcdEZh6S9broB2ENbjlat387iZc20J3oGBlVXV1fS09MHQwKh8c4kS1ceYFJWtdbq2SIiuaFA\nSHLu1vuWDQRBKckktLZ1ssesNw2k1VZVceqx+7JqQzurWtrZe/YULjrjEGbs0jDsmqMNwI63HPX2\nraFlS8fAlHwSUFNdydzdp9DW3jUQHB2w164cvP+srAdQpyvPH5euIwmDZrFNxFgkrZ6dHwo+RUqT\nAiHJuT+vbkub3tkV1glKLZ7YWF/N/nvP4Jy/fvtOrzneFaOTA3FZeNHXn+Q7P39u0DT7sQQT6cqz\nYvUWgEFdcBMxFkmrZ08+BZ8ipUuBkORcQ101W7Z3pc3bsi2kv5Hopb8fDt5/VkbXHGk8z2vrtuKv\nb+a1ddvYd49p1FZXsdv0BrZs6xwYn1RbU0VrWwe7Tqsf6B57ZW0I1rINWtKVJ7RCJYd1waU7djyt\nC1o9e/Ip+BQpXQqEJOdmz2hg3ZCVoSGM/6mvraarp4+6mire1FDN075h2Jo/6QKCdON5+vqT/Pax\nlXR2hzWHXnxtM3W1VfT29tHdmySZTNLd209FZw8zptUPOre7p48KKmBIL1ymwUS68tTUVKY5cvix\n421dGGlsk1bPzp2hgeqq9dvTHqfgU6T4KRCSnOvp7R8xPZnsBcKq0r1b+rn7f1fQ1dM30F326NJ1\nfObsw4YFBAvmz+bhZ9bywqutA4FUY301ie7eENBEOjp76IuPT4r2JUsmB49Zqq2pIplMDuuqyzSY\nSDdj7YC9dh02RijdqtbjbV1Id2+tnp076QLVuppqkkkGzUYEBZ9SPLp7+njihRaWLt/Gtv4Wjj50\nb3XrRhQISc4luvvSpvf1J0nSP7Bpam9fH+ta+0gmGUhr79jA4mXNHHfY3EHndvf08fLqLbQnegdW\npH6js3fYH6bUIO2qytjmrJVhjFA86Nm3aRdeWdtG2/bhXXWZ/MKoralKu9YR7HzW2GhdW5l0mY10\nb/1Sy410gWqiu4eGumq6Yj/bCj6lWKSC+1Xrt5Lo7OS1ja/x9IrNGuMWUSAkOdfTmz4Qgh2BSqqF\npq8/3nqU5I3OfhYva2bR25sG/aF/8bVNbG3vpqqygtSmqz19YR2i6qpYNFQR2ofixyVJDrxOtR5t\nbe/iTQ01VFZWDMwke1N9DU/7Bp5Yth5/fTMdnV0sW/UyS17axD+kaaVKJ5P1j0ZqRZg9ozHjLrN0\n9ym1WU35qk+6QLWyooJD37Ibe8+ZVjLfXykfGuM2OgVCknNTGmqzPjeZhA2bE3z7Z8/w8uotAy04\nnV29w46trqwYljaloYa+vn56+3Z0hVVWVFBZWREGRkdjgtZvfoPamqpBaQCLlzXzzIqNdPf0kexP\n0tXbxdMrhrdSjWecz4L5s3l06bpB9XvLXrsCZP3LarTyFKN81mekQHXvOdP0R0OKkiZYjC796E6R\ncTjpHXsN67Iai/aObp5ZsYEt27p4I9Eb/u3sHbTwIkBFRQV/uWhv3nf0PA7cbwbvO3oeN112PAe/\nZTeqqyroTyaprqpg910baKyvpj3Rw5ZtXbQneqiprtqx1lDM5q2dw9J7evp58sWWQWmjfcLKRMXA\nvxUD79eN45fVeMtTaPJZnwXzZzN396mD0tQNJsVMEyxGpxYhybn62moa6qro7N4xHmjoAouj6e7t\nGxaMVFRAVVUFvX07rjljWj1nnngAL72+hSmNtQP/qf+8uo2unnBcV08/bdu76OtP7mglSkB1dQV7\nz542qBtv7u5Tw0DrNVuHlSnJ4PKPuJ/a+m0D+SN1nyxZ3sK61vZBrVHrWtuZNb1+6OWAzH5ZjfaJ\n79D9p+/0/EKTz/poDJaUmtQEi1Xrd/xuU3C/gwKhcTCzOuBm4HSgA7je3W/Ib6nyr2VzB7vt2sjG\nLYmB8Tfd3b2MMJlsmOrqSpIk6e9n0IBnokHV/UmoJEl/fz+33beM9Zt3TNXv6ulj6xtdg8YIdXT1\nhmvEmqkqKip4z5H7UF9bPeiPXega20B3z44Aqaamkr84cM6gMma64Wu67rKR/sg31Ncwd/epWc0G\nK7VPfPmuTyZjvUSKRSq4/+Ozq1i6fCWHzJ+nWWMxCoTG5zrgcOB4YB5wp5mtdPdf5LNQ+TZzl3rW\nbmgfaIHp6cswAopUREHQwEDqZJK+fgamL1dUhPWhN2/v4vlXW9lt+o5BPpvaOunvh6pYp28yCTVV\nUFdbNTD1fvq0OjZt7eTMkw4YdO9Fb29i8bL1rHh9Ex2d3TTW13LAPjNZ9PamQcdluuFrujE+I45B\nmT2V04/fP6uWiNGm1Pf2pF/cspCVWn1E8q22poqFb5vNtMrNzFcL5yAKhLJkZo3A+cDJ7r4UWGpm\n1wIXA2UdCL28qm3QYOWx6u7pp762is5uYl1r/Qy9YjIZ1iOKq62ppKunD4i3/kBvP/R3hVaeRFcf\nvW2dzJ7ROOzetTVVfObsw3b6ySld98mq9dt54oXmYdcc2gI02h/5bFsiRuvO6e0Z8+XyrtTqIyKF\nS4FQ9g4hfP8ej6U9CvxzfopTOJ5/pXVc59fWVDFtSh0dnb309PRRU1PFlm2ddKUZ3Fw5ZFT2rtPq\nSSYZWG0a4E31NfQOmUm2s/tn8slpeNCyLm0gNLQFaKLGoJRad06p1UdECpMCoew1Aa3uHm+SaAHq\nzWymu2/KU7nyrr1jfB/Zp72pjt6+/mgwcdi3qz+ZpLUtQXyB6KqqCvaZM23QWkT7zJnGlz6xiPsf\nfoXXmrexb1PYf+zJ5S280dkzaM2gls0d4yrnUGNZ8Vl/5EVECoMCoew1AkMHK6Te12V6ka6uLjo6\ncvsHOR8SicTAv431VWzalvm5qZWfKyrCQogzptZSWVkxaL+y/fecxrzZU/BVWwfW3pk/bzoXvH8+\nL7y6heZNHTTNbOSwA2ZRW5Pkb0/ab+DcJ15oIZnsp7GuCupCq0sy2c+saTUjfu/j9RmL8//6AJ5Z\n0TqoPL09XXntzsm2LoVK9SlcpVQXUH0KWVdX7sYKKhDKXifDA57U+4wjm+bmZpqbh3enFKuVK1ey\n54wKVm8YnldVCf3JHUFP9UAAVDEwpKe6soKmXXp469wGXl5Xy6ZtPcycVsNb9gg/qvNmTY2l1bB6\n5StMq4RpuwF08sqfNw+7b0MyyZtq+2jdtiMamTWthobkJpYvH3780PqM1c7Kky/Z1KWQqT6Fq5Tq\nAqpPqVMglL21wCwzq3T3VN/MHCDh7m2ZXqSpqYnp04tvnZehEokEK1euZN68eVwwr4o/3/wEbdu7\nBoKehrpqdp1aR1d3Hz29/dRUV1JfV0V9bRVt27sH0t48dxqnnvQ2amuqOPjtw++TLi0Tb7W+YS01\no43JidenoaFhxOOKQSnVBVSfQlZKdQHVp5C1tbXlrBFBgVD2ngV6gEXAY1HascCTY7lIXV0djY3D\nZy8Vq4aGBhobG7npshO453crBsbpvP+4N3P7r18cNn7mwtMO4rk/t074wnWNwAnvmLrT44ZK1acU\nlFJdQPUpZKVUF1B9ClEuu/cUCGXJ3RNmdidwi5mdB8wFLgM+lt+SFYYpjbWc+zeDm29GmimlQcMi\nIpIvCoTG51LCytK/B7YCX3T3+/NbpMKloEdERAqNAqFxcPcEcG70JSIiIkVGu8+LiIhI2VIgJCIi\nImVLgZCIiIiULQVCIiIiUrYUCImIiEjZUiAkIiIiZUuBkIiIiJQtBUIiIiJSthQIiYiISNlSICQi\nIiJlS4GQiIiIlC0FQiIiIlK2FAiJiIhI2VIgJCIiImVLgZCIiIiULQVCIiIiUrYUCImIiEjZUiAk\nIiIiZUuBkIiIiJQtBUIiIiJSthQIiYiISNlSICQiIiJlS4GQiIiIlC0FQiIiIlK2FAiJiIhI2VIg\nJCIiImVLgZCIiIiULQVCIiIiUrYUCImIiEjZUiAkIiIiZUuBkIiIiJQtBUIiIiJSthQIiYiISNlS\nICQiIiJlS4GQiIiIlC0FQiIiIlK2FAiJiIhI2VIgJCIiImVLgZCIiIiULQVCIiIiUrYUCImIiEjZ\nUiAkIiIiZUuBkIiIiJQtBUIiIiJSthQIiYiISNlSICQiIiJlS4GQiIiIlC0FQiIiIlK2FAiJiIhI\n2VIgJCIiImWrOt8F2BkzOxR4GkgCFVHyEnf/iyh/BnAb8G5gI3CVu/84dv5hwHeAg4BlwCfd/elY\n/geBLwNNwAPABe6+KZb/deA8QtD4fXe/IpY36r1FRESksBVDi9CBwDPAnNjXybH8HwJTgYXAV4Hv\nmdkCADNrBH4DPAQcDjwO/MbMGqL8vwC+B1wdnb8rcEfqwmZ2GXA28H7gDODDZnZpJvcWERGRwlfw\nLULAfGC5u28cmmFm+wHvA/Zx99XAcjM7EriI0IpzNtARa8X5jJn9FXAmcCfwKeCuVCuOmX0UeN3M\n9nH314FPA1e6++NR/hWE1qMbzOzNO7m3iIiIFLhiaRFaMULeQmBVFIikPAocGct/dMg5f4zlLwIe\nTmW4+xpgFbDIzJqAvYBHhlx7HzObDfzFTu4tIiIiBa5YWoQqzew5YBfgt8Dl7t5OGNezbsjxLcDc\n6HUTYVzQ0Py3xfJHOr+JMC5p3ZC8ilj+aPcWERGRApf3QMjM6oE9R8jeCLwZeAX4OGEMz7eB/wRO\nAxqBriHndAF10evx5DcCuHv3kDxi+aNde2fqAdrb2zM8vLB1dYVvRVtbG4lEIs+lGb9Sqk8p1QVU\nn0JWSnUB1aeQxf521o/3WnkPhAjdV38gtL4MdRowE0i4ex+AmX0MeNLM5gCdDA886oCO6PV48juj\n+9XGgqHUsR07OTcT8wBaW1tpbW3N8JTC19zcnO8i5FQp1aeU6gKqTyErpbqA6lPg5gGPjecCeQ+E\n3P0hxjZWaTmhe2pPYC1hFlncHCD1lMeTvza6zxzCuKFUXjKWP9q1d+YB4MPASqKgS0RERDJSTwiC\nHhjvhfIeCI3GzOYDTwAHRbO4AA4DeoA/A5sJg5f3cPfUeJ1jgMXR68XAFQx2NGHmVyr/GMIMMsxs\nL8IYn8fdvdnMVkX5P4mOP5YwQLrFzBbv5N6jOuKIIzbFrisiIiJjM66WoJSCDoSAl4CXgdvM7LOE\nMUK3ALe6+1Zgq5k9APzIzP6BMJPrg8Bx0fn/BXzNzL4F3Ar8PWFszz1R/neAP0RBzRLC+KNfufuq\nWP43zCzVOvQ14JsA7v7aTu4tIiIiBa6gp8+7exI4BdhGmOZ+L/AgEF/U8JwofzHweeBcd38qOn87\n8NeE4GQJIVh5r7snovzFwIWEBRUfBTYxeA2gbwJ3Ab+I/v2hu/9bJvcWERGRwleRTKYboywiIiJS\n+gq6RUhERERkIikQEhERkbKlQEhERETKlgIhERERKVsKhERERKRsFfo6QiUtWofox+5+ZyxtBnAb\n8G7CXmtXufuP81TEjJhZHXAzcDphi5Hr3f2G/JZqbKI6LAE+5e4PR2nzCM/iSMIK4J919wfzVcZM\nmNkewI3ACYRncTfweXfvLtL6vBn4D8JCqJuAf3f366K8eRRZfVLM7DdAi7ufF72fR5HVxcxOJSwt\nkiSss5YEfu7uZxVpfWqBbxHWg+sCfuDuX4jy5lFE9Ym2orqdwc+mAuh392oz25ewtl6x1GcuYV2/\n4wi/B/4ttZRNLp6NWoTywMwqzOwm4F1psn8ITCXswfZV4HtmtmAyy5eF64DDgeOBi4Crzez0vJZo\nDKIg6KfAgUOy7gPWAUcAPwLujf5DFrKfE5aePxo4G/gbdqykfj9FVB8zqwB+A7QAhxIWRL3SzM6O\nDimq+qRE5X/vkORi/Fk7EPglYWuhOUAT8IkorxifzY3ASYQPoR8CLjCzC6K8YqvPz9jxTOYA+xB2\nY/h2lF9sP2/3ANsJf2c+A3zVzN4f5Y372ahFaJJFn9h/BOwLtA3J2w94H7CPu68GlpvZkYTg4ryh\n1yoEZtYInA+c7O5LgaVmdi1wMeHTYkGLtnEZttWJmZ0I7AcscvdO4OtmdhLhOVwzuaXMjJkZYdHQ\n2e7eGqVdBXzTzP4f4WduYbHUB5gNPANc5O5vAK+Y2e+AY8ysheKrD2a2K3At8KdYWtH9rEXmA8vc\nfWM8MapPUT2b6LmcB5yYWhTXzK4DFprZnymy+rh7F7Ah9d7MPh+9/HyxPR8zm05oGDjf3V8h/B74\nf8BJZraNHNRFLUKT73DCJq5HEFaljltI2MtsdSztUUKTX6E6hBBQPx5Le5RQl2LwTuB3hO9xRSx9\nIfB09J8rpdCfxXrgPakgKGYXYBFFVh93X+/uH4yCIMzsaMJ+f/9HEdYnch1hb8PlsbRi/FmD0CK0\nIk16MdbnGKDN3R9NJbj7te7+CYr3Zw0YCPI+B1zh7j0U3/NJAG8A55pZdfSB72jCh6ScPBu1CE0y\nd/818GuA8DwHaSI08cW1EDaCLVRNQKu798bSWoB6M5vp7pvyVK6MuPstqddDnkfRPYto/72BvvGo\na+liQqBXdPWJM7OVwF6E/zu/IDTxF1V9ok/ixwIHEfZMTCnWZ2PAe8zsC0AVofviKoqzPvsBK83s\no8A/A7WEMTZfpTjrE3cRsNbd743eF1V93L3LzC4G/p3QLVYF3O7ut5vZjeSgLgqEcszM6oE9R8hu\ndveOUU5vJAzSi+sC6nJRtgkyUpmhsMu9M8X4LIb6JnAY8A7C/nzFXJ/TCWMdvkMY0FpUzycah3YL\noZuva0jQXVR1ATCzvYEGwqf1MwndEzdGaUVXH2AKcADwd8DHCcHCdwkTDoqxPnHnA1+PvS/G+swn\njEe7jvBB4qaomzwndVEglHsLgT8QRukPdRrhYY6kk+EPsI7wn7FQjVRmKOxy70wnMGNIWqE/iwFm\n9g3g08BZ7v6imRV1fdz9aQAzuxT4MfB9YNchhxVyfb4EPOnu/5smr+iejbuvilp8U+McnzOzKsL4\nx9sprmcD0EuYpPJBd18DYGb7EFpT/geYOeT4Qq8PAGb2DsIH87tiyUX18xaN+TkfmBuNfXomGgx9\nJaG1e9zPRoFQjrn7Q2Q/9mot4VNv3BygeVyFmlhrgVlmVunu/VHaHCAR+yVZjNYyfBZZoT8LAKIZ\niRcCH3b3+6LkoquPme0OHOnu98eSXyR0WzQTPiXGFXJ9/haYbWbbo/d1AGb2AeBfKbJnA5Dm//dy\nwozF9RTXs4FQts5UEBRxQhfLWuBtQ44v9PqknAw8HHWbpxTb74LDgZejICjlGUIXZk6ejQZLF5bF\nwD7RzLKUY6L0QvUs0EMYtJZyLPBkfoqTM4uBw6MujZRCfxaY2dWE5v2/dfd7YlnFWJ99gV+YWVMs\nbQFhNsyjwBFFVJ93Epr0D4m+fkmY9nsI8ARF9mzM7C/NrDUaCpByGNAKPEJxPRsIZas3s/1jaQcS\n1qVZTPHVJ2Uh8MchacX2u2AdsL+ZxRtu5gOvkaNnoxahAuLur0WLLP7IzP6BMBX6g4RFpAqSuyfM\n7E7gFjM7j/AJ6jLgY/kt2bg9BKwG7jCzLwOnEMbafDyfhRpNtBTAlYQWhsfMbHYsu+jqQwimlwA/\niLrE9iVMPf8K8DBFVJ8hM0GJWoaS0f/51ymiukQeI3Q/fM/MrgHeTHg236DIng2Au6+IFrm8w8wu\nIowRuoIwBbvo6hPzduA/h6QV2++CXxF+tr5nZl8F3gp8PvrKybNRi1B+pRtHdA5hWv1iwoM+N7Wu\nRQG7FHgK+D1wE/DFId0ZxWLgeUTdfO8nNLMuISywduqQpvNCcwrh//SVhE9R6whNxOui+pxKEdUn\n9gzeIPzhvRX4trv/e5R3CkVUn5EU48+au7cTul12IwSstwG3uPv1RfxsPkxYdPAR4A7gRnf/jyKu\nD8DuwJZ4QrH9vLn7NsJCl02E9beuB65x9+/l6tlUJJPp/haLiIiIlD61CImIiEjZUiAkIiIiZUuB\nkIiIiJQtBUIiIiJSthQIiYiISNlSICQiIiJlS4GQiIiIlC0FQiIiIlK2FAiJiIhI2dJeYyKSV2Y2\nk7Bj+bRo77ofACvd/Zosr7crcA9wNLDU3RcNyd+HsGFjXB+wGfgD8I9D9wYbcn4/8HF3vzPD8hwI\nzHP3/868FiIyWdQiJCL5tghY5u6J6P1CxrcT9kcIQdDRhP3V0kkCpxH2KJoD7A2cTthB/Vc7uf4c\n4K4xlOfXwIIxHC8ik0gtQiKSb4sImyliZtMAI2zkma0ZwHp3f3qUYyqALe6+IZbWbGZfAn5kZge5\n+/PpThxyTiYqxni8iEwibboqInlhZq8RWmIqCC00qYAh9UvpBHd/OM15bwW+QWjxqQYeBC5z91Vm\ndjvwsdh1zh3ahRXrGjt+6PXN7Czgp8B8oCs67p+BfwDeILQYbSXqGovuB9AKnANMAX4PXODu62N1\nBHjI3U+Mgr3rCK1VtYRds69w96eiMlwNnAA0A39F2An9s8DXgA8SdhR/Dfi2u393xG+wiGREXWMi\nki8LgCagDTiR0OX0NeDe6PVjQ08ws72Bx4EE8E7g3dGxD5vZFODTwPXAasbQhWVmFWZ2KHAl8Ky7\nr4hln0MITM5y9+1pTv8gsCtwLPAe4AjgK1HeO4C1UZlOj9J+C+xDCHL+gtAN+EczOyR2zeOAdcAh\nwI3ARcAZwJnAW4CbgJvN7KhM6iciI1PXmIjkhbtvMrM9gEbgUXfvM7N9gSfdfeMIp30K2A581N17\nAMzsA4QWko+4+y1m1g70jXKNlN9GA58B6qJ/HwIuHHLcf7j7S6Ncpw240N37gBVm9jPgvVEdW82s\nD2h39zYzO4kwBmqWu7dF519pZscQWp3Oi9KSwJdSgZeZvZnQIvW6u68nBEEvAfGATUSyoEBIRPLp\nUGB5FERAaAH54SjHvx1YkgqCANy9xcwcOGiM9z6faGwS8P/buXvXKIMgAOMPilERBAv1DzA6CIIf\nYJfCwiYoxEKwtRRFxEothDSCYGEl2ljFwjS2miKFRbAUJRYD8VAiFoeNIooIarF3cB73kRPOK/b5\nwTXvu7vvdDfMzu5PoJmZP3qMWxuyztuO+KFsnU31GXuUUolfj4jO51Ndc5pd1ad7lK20DxHxkrId\n+DgzPw2JTdIQJkKSJiIiVoF9wOaIaP/p7wCetKoos5m50jWtX+PxJkoyM4qPmdnYwLjvQ973Sp4G\nxfkZONZjTOc6f30zM9ciYho4QdkOPAVci4jzmbkwJD5JA9gjJGlSZin9PvOUStBF4B2l6nOE0kTc\n7TVwPCK2tB9ExF5K38yb8Yb7zzpPpKwCO4Gtmdlo/4AbwFy/BSLiMnA2M5cz83pmHgaWgXPjDFyq\ngRUhSRORmesRcQi4mpmN1omtlczsvuyw033gArAQEbeA7cAdoMlod/v8T1+B/RGxB3gGvAIWI+IK\npan7EuWk26MBa+wGbkbEt9b8g5Rk8e44A5dqYEVI0kRExAFgG6XKA6WJ+MWgOZn5nnJabFdr7FPK\nqV1zFhQAAACISURBVKyZzPwywuc3em9Ir3G/R5gP5dTXaWApM38BJynVrkVKUjMDnMnM5wPWmAce\nttZK4AGlb+j2CHFI6sF7hCRJUrWsCEmSpGqZCEmSpGqZCEmSpGqZCEmSpGqZCEmSpGqZCEmSpGqZ\nCEmSpGqZCEmSpGqZCEmSpGqZCEmSpGqZCEmSpGr9AS31pg2d+JTHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1238c5d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "ax = sns.regplot(x=\"# of Printers\", y=\"Average Meter Total\", data=printer_use_df, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! Ok, lets look at the outliers. Laboratories have a total of 70 printers yet the average use is way below average. On the other hand, Copy centers have a TON of printing going on with only 4 printers - maybe they need some more. Accounting has by far the most activity with 34 printers at an average meter count of near 1 million as well. If only they all could be more like Accounting!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another possibility confounding factor in this is printer preference. Perhaps when people are printing, they are choosing certain types of printers? Perhaps some printers like all those unused ones in the labs are simply just a pain to use?\n",
    "\n",
    "First lets check the distribution of meter totals, categorized by printer model... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XM7155                       132\n",
      "XS798de                       15\n",
      "XM7170                         7\n",
      "XM3150                         4\n",
      "BizHub Press C1070             2\n",
      "ImageRunner Advance 6075       2\n",
      "ImageRunner Advance C5035      1\n",
      "XS748D                         1\n",
      "BizHub Press 1250              1\n",
      "X792                           1\n",
      "XS748de                        1\n",
      "XM 7155                        1\n",
      "Aficio MP C3002                1\n",
      "CS748de                        1\n",
      "BizHub 751                     1\n",
      "Name: Full Model Name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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3ZQDuvpjgqsZTgDcIzrQ6temF3f1h4AbgbuAZglPJr4zkvhSoAF4A7gSuCa+nQ5j7lHbm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\nxm0xM3s8o6NT27zTe6+Nl3c/hCe+H47nZ53PyGO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KzyH4C/7l/5tV0JukLuqct5lNn/P9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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120735e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "models=df['Full Model Name']\n",
    "counts=models.value_counts()\n",
    "\n",
    "print (counts)\n",
    "plt.xticks(rotation=90)\n",
    "ax = sns.stripplot(x=\"Full Model Name\", y=\"Meter Total\", data=df,jitter=True)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that XM7155 is by far the most common printer even with its relatively low meter totals. Additionally, based on the distributions, we can look at BizHub Press C1070 and say that this printer is being used most often. However, to know this for sure, it would be useful to cross reference these findings with the ones above regarding Departments. Here we extract the list of all printers in each department and find the mode - the most common printer model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In  Copy Center  the most popular printer is  BizHub Press C1070\n",
      "In  Laboratory  the most popular printer is  I Class\n",
      "In  Accounting  the most popular printer is  LaserJet 4250n\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel('auxillio_data.xlsx',sheetname=0,header=4) #re-read in our dataframe \n",
    "\n",
    "printers_by_department_dict = collections.defaultdict(list)\n",
    "for index, row in df.iterrows():\n",
    "    department = row['Area (Department)']\n",
    "    printer = row['Full Model Name']\n",
    "    printers_by_department_dict[department].append(printer)\n",
    "\n",
    "for department,printers in printers_by_department_dict.iteritems():\n",
    "    mode = max(set(printers), key=printers.count)\n",
    "    if department == 'Accounting' or department == \"Copy Center\" or department == \"Laboratory\":\n",
    "        print \"In \",department,\" the most popular printer is \",mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Copy Center are using the BizHub Press. Looking above, we know that the Copy Center has very few printers with high output. This confirms what we are seeing in the data and leads to additional questions - why are these higher output? Should we be suggesting these instead of a low output printer such as XM7155? Or are the areas where the XM7155 is deployed simply used by more people who print less freqeuntly? Further analysis is needed to answer these questions specifically. \n",
    "\n",
    "Additionally, in the Laboratories, we have insufficient data to determine why so many printers exist with so little use. It may be worth going back to these printers to check the meter counts and adding them to the data set. More meter total data on printers in Accounting would help as well if we wish to further study the high print output in accounting. "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:meanshift]",
   "language": "python",
   "name": "conda-env-meanshift-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}