cse-143-au14-grades-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSE 143 grades analysis\n",
    "\n",
    "You can grab the data by going to http://courses.cs.washington.edu/courses/cse143/14au/scores.html and copying everything in the table to a text file apart from the last three rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header:\n",
      "['code', 'assign1', 'assign2', 'assign3', 'assign4', 'bonus4', 'assign5', 'assign6', 'assign7', 'assign8', 'bonus8', 'Late_Days', 'explore', 'weekly %', 'midterm', 'final', 'total', 'grade']\n",
      "\n",
      "Random row from data:\n",
      "{'assign3': 19.0, 'code': 8025.0, 'Late_Days': 4.0, 'weekly %': 90.0, 'bonus8': 2.0, 'assign6': 15.0, 'total': 86.6, 'grade': 3.3, 'assign7': 15.0, 'bonus4': 0.0, 'explore': 0.0, 'assign2': 17.0, 'assign1': 18.0, 'assign4': 19.0, 'midterm': 71.0, 'final': 91.0, 'assign8': 29.0, 'assign5': 19.0}\n",
      "\n",
      "A more readable version of that data:\n",
      "{'Late_Days': 4.0,\n",
      " 'assign1': 18.0,\n",
      " 'assign2': 17.0,\n",
      " 'assign3': 19.0,\n",
      " 'assign4': 19.0,\n",
      " 'assign5': 19.0,\n",
      " 'assign6': 15.0,\n",
      " 'assign7': 15.0,\n",
      " 'assign8': 29.0,\n",
      " 'bonus4': 0.0,\n",
      " 'bonus8': 2.0,\n",
      " 'code': 8025.0,\n",
      " 'explore': 0.0,\n",
      " 'final': 91.0,\n",
      " 'grade': 3.3,\n",
      " 'midterm': 71.0,\n",
      " 'total': 86.6,\n",
      " 'weekly %': 90.0}\n",
      "\n",
      "Midterm average:\n",
      "79.8935064935065\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "raw_data = open(\"cse143-au14.txt\", \"r\")\n",
    "\n",
    "lines = raw_data.readlines()\n",
    "\n",
    "def parse_line(line):\n",
    "    cells = line.strip().split(' \\t')\n",
    "    return cells\n",
    "\n",
    "def clean_cell(cell):\n",
    "    if cell == ' ':\n",
    "        return 0.0\n",
    "    elif cell.endswith('%'):\n",
    "        return float(cell[:-1])\n",
    "    else:\n",
    "        return float(cell)\n",
    "    \n",
    "def clean_line(line):\n",
    "    output = []\n",
    "    for cell in line:\n",
    "        output.append(clean_cell(cell))\n",
    "    return output\n",
    "\n",
    "def label_line(header, line):\n",
    "    output = {}\n",
    "    for head, cell in zip(header, line):\n",
    "        output[head] = cell\n",
    "    return output\n",
    "\n",
    "def get_all(category, data):\n",
    "    output = []\n",
    "    for datum in data:\n",
    "        output.append(datum[category])\n",
    "    return output\n",
    "\n",
    "header = parse_line(lines[0])\n",
    "\n",
    "data = []\n",
    "for line in lines[1:]:\n",
    "    bleh = clean_line(parse_line(line))\n",
    "    data.append(label_line(header, bleh))\n",
    "    \n",
    "midterm_scores = get_all('midterm', data)\n",
    "\n",
    "print(\"Header:\")\n",
    "print(header)\n",
    "print()\n",
    "\n",
    "print(\"Random row from data:\")\n",
    "print(data[26])\n",
    "print()\n",
    "\n",
    "print(\"A more readable version of that data:\")\n",
    "pprint.pprint(data[26])\n",
    "print()\n",
    "\n",
    "print(\"Midterm average:\")\n",
    "print(sum(midterm_scores) / len(midterm_scores))\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x68eaef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: this below line isn't Python. It's a special \n",
    "# directive to the software this document is written\n",
    "# in telling it to embed all graphs directly in the\n",
    "# document instead of opening it in a separate window\n",
    "%matplotlib inline\n",
    "\n",
    "import pylab\n",
    "\n",
    "pylab.hist(midterm_scores, bins=100)\n",
    "pylab.title(\"Histogram of midterm scores\")\n",
    "pylab.xlabel(\"Score\")\n",
    "pylab.ylabel(\"Num people\")\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:   76.30389610389611\n",
      "median: 80.0\n",
      "stdev:  19.1102505867709\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "\n",
    "final_scores = get_all('final', data)\n",
    "\n",
    "print(\"mean:   \" + str(statistics.mean(final_scores)))\n",
    "print(\"median: \" + str(statistics.median(final_scores)))\n",
    "print(\"stdev:  \" + str(statistics.stdev(final_scores)))"
   ]
  }
 ],
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