eda_gamespot.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:c70e04a7d3ecc99c2b6ef31e9c05cc11a17e204a462161148bbf70da4a3302a7"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Preliminary GameSpot Exploration/Analysis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "load in the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('data/gamespot.json')\n",
      "data = json.load(f)\n",
      "print \"The number of reviews scraped from GameSpot is \", len(data)\n",
      "# print an example review\n",
      "print data[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The number of reviews scraped from GameSpot is  13702\n",
        "{u'score': u'8', u'summary': u' The Unfinished Swan on the PlayStation 4 is a pretty game made prettier, and a gentle charmer in its own right.', u'datetime': u'2014-11-06 18:50:00'}\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "histogram of ratings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scores = np.array([float(r['score']) for r in data])\n",
      "plt.hist(scores, bins=np.arange(0,12)-0.5)\n",
      "plt.xlim((-0.5, 10.5))\n",
      "plt.xticks(range(11))\n",
      "plt.xlabel('rating')\n",
      "plt.ylabel('number of reviews per bin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x106ddefd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pWcA/AJ+x/eFStglo2N5euo/W2n6ppKUAti8vx60GLgMeKsccW8rPB061/bZxsTJIHQMn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\niIi2kiAiIqKtJIiIiGgrCSIiItr6/4xn73oyyUWRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106db6250>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# look at some basic properties of the scores\n",
      "print np.unique(scores)\n",
      "print np.median(scores)\n",
      "print np.mean(scores)\n",
      "is_int = np.array([np.allclose([scores[i]], [np.round(scores[i])]) for i in range(len(scores))])\n",
      "print \"out of \", len(scores), \" scores, \", np.sum(is_int), \" are integer valued\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0.    1.    1.2   1.3   1.4   1.5   1.6   1.7   1.8   1.9   2.    2.1\n",
        "   2.2   2.3   2.4   2.5   2.6   2.7   2.8   2.9   3.    3.1   3.2   3.3\n",
        "   3.4   3.5   3.6   3.7   3.8   3.9   4.    4.1   4.2   4.3   4.4   4.5\n",
        "   4.6   4.7   4.8   4.9   5.    5.1   5.2   5.3   5.4   5.5   5.6   5.7\n",
        "   5.8   5.9   6.    6.1   6.2   6.3   6.4   6.5   6.6   6.7   6.8   6.9\n",
        "   7.    7.1   7.2   7.3   7.4   7.5   7.6   7.7   7.8   7.9   8.    8.1\n",
        "   8.2   8.3   8.4   8.5   8.6   8.7   8.8   8.9   9.    9.1   9.2   9.3\n",
        "   9.4   9.5   9.6   9.7   9.8   9.9  10. ]\n",
        "7.0\n",
        "6.71798277624\n",
        "out of "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13702  scores,  3777  are integer valued\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "histogram of number of words per review summary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nwords = [len((r['summary']).split()) for r in data]\n",
      "plt.hist(nwords)\n",
      "plt.xlabel('number of words in review')\n",
      "plt.ylabel('number of reviews')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x107020f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ul9d7TtKPKP4fn5/KPwFcKWkdxR9FP6GYv3nAPpSIWCNpEcVJHeDvI+KeAeIv\n+x7w+oj4taTvA5NJySYibpT0h8A9aR8fjohHU7PN7n2m9V4PbAB+yp4mnIOBb6m4cUDAB/oLvZ/j\n099y4zZ9da9I8fxAxc1UTwHvBH4eERtUzK63tWGY7L7tv0zRVPgjFRs/yp55sVcBb4qIn0j6GXAI\nQyRh6xweGt3MzLK5ecrMzLI5aZiZWTYnDTMzy+akYWZm2Zw0zMwsm5OGmZllc9IwM7NsThpmZpbt\n/wMnedFQUBr2LgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105e3ce90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "histogram of year in which review written"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = np.array([int((r['datetime'])[0:4]) for r in data])\n",
      "plt.hist(years)\n",
      "plt.xlabel('year')\n",
      "plt.ylabel('number of reviews')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x105e3cc10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1070e61d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "plot illustrating time trend of average rating"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import scoreatpercentile\n",
      "\n",
      "meds = [np.median(scores[years == y]) for y in np.unique(years)]\n",
      "p25 = [scoreatpercentile(scores[years == y], 25) for y in np.unique(years)]\n",
      "p75 = [scoreatpercentile(scores[years == y], 75) for y in np.unique(years)]\n",
      "\n",
      "plt.plot(np.unique(years), np.array(meds),c='r', label='median')\n",
      "plt.plot(np.unique(years), np.array(p25),c='k')\n",
      "plt.plot(np.unique(years), np.array(p75),c='k')\n",
      "plt.legend(loc='lower right', frameon=False)\n",
      "plt.ylim(0,10)\n",
      "plt.xlabel('year')\n",
      "plt.ylabel('rating')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x109012050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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99zB27Ngq59m+fXseeughrrnmGjp37sy0adO44oorql1+JrBMPKwxM8/EukRE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\n8v8BskdHTUFfyJkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1072e56d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "word clouds for best versus worst reviews?"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}