{
 "metadata": {
  "name": "compare_goog_data"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data used by researchers in the study:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat PreisMoatStanley_ScientificReports_3_1684_2013.dat | head -n 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"Google Start Date\" \"Google End Date\" \"arts\" \"banking\" \"bonds\" \"bubble\" \"buy\" \"cancer\" \"car\" \"cash\" \"chance\" \"color\" \"conflict\" \"consume\" \"consumption\" \"crash\" \"credit\" \"crisis\" \"culture\" \"debt\" \"default\" \"derivatives\" \"dividend\" \"dow jones\" \"earnings\" \"economics\" \"economy\" \"energy\" \"environment\" \"fed\" \"finance\" \"financial markets\" \"fine\" \"fond\" \"food\" \"forex\" \"freedom\" \"fun\" \"gain\" \"gains\" \"garden\" \"gold\" \"greed\" \"growth\" \"happy\" \"headlines\" \"health\" \"hedge\" \"holiday\" \"home\" \"house\" \"housing\" \"inflation\" \"invest\" \"investment\" \"kitchen\" \"labor\" \"leverage\" \"lifestyle\" \"loss\" \"markets\" \"marriage\" \"metals\" \"money\" \"movie\" \"nasdaq\" \"nyse\" \"office\" \"oil\" \"opportunity\" \"ore\" \"politics\" \"portfolio\" \"present\" \"profit\" \"rare earths\" \"religion\" \"restaurant\" \"return\" \"returns\" \"revenue\" \"rich\" \"ring\" \"risk\" \"sell\" \"short sell\" \"short selling\" \"society\" \"stock market\" \"stocks\" \"success\" \"tourism\" \"trader\" \"train\" \"transaction\" \"travel\" \"unemployment\" \"war\" \"water\" \"world\" \"DJIA Date\" \"DJIA Closing Price\"\r\n",
        "2004-01-04 2004-01-10 0.956667 0.193333 0.203333 0.183333 2.41667 0.913333 4.19333 0.326667 0.0766667 1.12 0.12 0.00333333 0.0866667 0.31 2.19333 0.12 0.383333 0.21 0.23 0.02 0.04 0.0433333 0.05 0.183333 0.19 0.723333 0.206667 0.17 0.413333 0 0.383333 0.03 2.13333 0.02 0.29 0.606667 0.146667 0.04 0.85 0.936667 0.01 0.336667 0.426667 0.04 2.73333 0.0633333 0.886667 5.6 3.2 0.493333 0.0566667 0.0466667 0.363333 0.63 0.486667 0 0.0733333 0.503333 0.11 0.563333 0.08 1 4.85 0.09 0.06 2.74667 0.86 0.1 0.02 0.12 0.11 0.0966667 0.116667 0 0.293333 1.74667 0.6 0.07 0.293333 0.266667 0.93 0.236667 0.3 0 0 1.23667 0.126667 0.26 0.123333 0.343333 0.346667 0.706667 0.05 2.06 0.336667 2.02 1.91333 4.83333 2004-01-12 10485.18\r\n",
        "cat: write error: Broken pipe\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('PreisMoatStanley_ScientificReports_3_1684_2013.dat', delimiter=' ')\n",
      "columns = ['Google Start Date', 'Google End Date', 'debt', 'DJIA Date', 'DJIA Closing Price']\n",
      "df = df[columns]\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Google Start Date</th>\n",
        "      <th>Google End Date</th>\n",
        "      <th>debt</th>\n",
        "      <th>DJIA Date</th>\n",
        "      <th>DJIA Closing Price</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2004-01-04</td>\n",
        "      <td> 2004-01-10</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 2004-01-12</td>\n",
        "      <td> 10485.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2004-01-11</td>\n",
        "      <td> 2004-01-17</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 2004-01-20</td>\n",
        "      <td> 10528.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2004-01-18</td>\n",
        "      <td> 2004-01-24</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 2004-01-26</td>\n",
        "      <td> 10702.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2004-01-25</td>\n",
        "      <td> 2004-01-31</td>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 2004-02-02</td>\n",
        "      <td> 10499.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2004-02-01</td>\n",
        "      <td> 2004-02-07</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 2004-02-09</td>\n",
        "      <td> 10579.03</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "  Google Start Date Google End Date      debt   DJIA Date  DJIA Closing Price\n",
        "0        2004-01-04      2004-01-10  0.210000  2004-01-12            10485.18\n",
        "1        2004-01-11      2004-01-17  0.210000  2004-01-20            10528.66\n",
        "2        2004-01-18      2004-01-24  0.210000  2004-01-26            10702.51\n",
        "3        2004-01-25      2004-01-31  0.213333  2004-02-02            10499.18\n",
        "4        2004-02-01      2004-02-07  0.200000  2004-02-09            10579.03"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "debt_from_study = df[['debt']]\n",
      "debt_from_study.index = df['Google End Date']\n",
      "debt_from_study.index = debt_from_study.index.to_datetime()\n",
      "debt_from_study.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>debt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-10</th>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-17</th>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-24</th>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-31</th>\n",
        "      <td> 0.213333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-07</th>\n",
        "      <td> 0.200000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                debt\n",
        "2004-01-10  0.210000\n",
        "2004-01-17  0.210000\n",
        "2004-01-24  0.210000\n",
        "2004-01-31  0.213333\n",
        "2004-02-07  0.200000"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "debt_us_as_of_20130528 = pd.read_csv('debt_US_google_trend.csv', index_col='date')\n",
      "debt_us_as_of_20130528.index = debt_us_as_of_20130528.index.to_datetime()\n",
      "debt_us_as_of_20130528.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>debt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-10</th>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-17</th>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-24</th>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-31</th>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-07</th>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "            debt\n",
        "2004-01-10    24\n",
        "2004-01-17    25\n",
        "2004-01-24    24\n",
        "2004-01-31    25\n",
        "2004-02-07    23"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "debt_compare = pd.merge(debt_from_study, debt_us_as_of_20130528, left_index=True, right_index=True, how='outer')\n",
      "debt_compare.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>debt_x</th>\n",
        "      <th>debt_y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-10</th>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-17</th>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-24</th>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-31</th>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-07</th>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "              debt_x  debt_y\n",
        "2004-01-10  0.210000      24\n",
        "2004-01-17  0.210000      25\n",
        "2004-01-24  0.210000      24\n",
        "2004-01-31  0.213333      25\n",
        "2004-02-07  0.200000      23"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "ax2 = ax.twinx()\n",
      "\n",
      "debt_compare.debt_x.plot(ax=ax, style='b-')\n",
      "debt_compare.debt_y.plot(ax=ax2, style='r-', secondary_y=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x344db10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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bhIerK4vYWMq2rq0F3nkHePhh+k1uWbS0kHXCvTCPPQasWQO8+ab1Oai5ofhI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\niygUndsVNzdujzIyuJ6GhnIZ797NZW5rUIS3N5fPwIH8PFy/Lk5+m5K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qCBsTYo8BdIg9\nNl0bUlZWhoCAAADA8OHDAQBKpRJLlizB8ePHXaZn8ODB8Pb2xoIFCwAACxcuxLfffusyPQJnzpxB\nS0sLYmJiHNLiLD35+fkYNWoURo0aBQC45557cOTIEZdoAYC77roLR48exZEjR6DVahESEmK3Fnv1\nWCIgIABlZWVmdbpCjzNxpp7f/OY3CAkJwapVq1yuBQC8vLyQnJyMEydOOKTHGdzUxoKIsHz5coSF\nhWHNmjWGzy3FHiclJSErKwt6vR6XLl3ChQsXcNttt6G1tRWXL18GwJVgz549HTI79rYehUKBuXPn\n4tChQwCAzz//HOEO7HPsLD0CO3bswJIlS+zW4Ww9o0ePRkFBgeGeHThwAGFhYS7RAsDgIrxy5Qpe\nfvllrFixosfLxvg8Y4YNGwaVSoVjx46BiLB9+3aHYu+dpcdZOFPPxo0bUV9fj7/+9a8u1dLU1ASd\nTgeARzqffvpptzpi3cYVEyW9RU5ODikUCoqOjjaEde7du9dq7PGWLVtIo9FQSEgIZWdnExFRY2Mj\njR8/3hD+uGbNGkNUkiv0EBGVlJRQXFwcRUVF0axZs6i0tNSleoiIRo8eTYWFhXbr6Ak9mZmZhtDZ\npKQkqq2tdZmWxYsXU1hYGIWFhdHOnTt7rWxGjhxJvr6+pFQqKTAwkPLz84lIDJ3VaDT0+9//3uV6\n1q1bR4GBgeTu7k6BgYG0efNml+kpLS0lhUJBYWFhhuu88cYbLtFSVVVFsbGxFBUVRZGRkbR27VqH\n2h1n4fJtVWVkZGRkpM9N7YaSkZGRkXEOsrGQkZGRkekS2VjIyMjIyHSJbCxkZGRkZLpENhYyMjIy\nMl0iGwsZGRkZmS75P5H7Fh27vg0oAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}