{
 "metadata": {
  "name": ""
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**0. Import & parse CSV data**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import numpy as np\n",
      "from matplotlib.pylab import plot, legend\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# read trends.csv with pandas. The first line is the header: \n",
      "## week_start,week_end,textbooks,spring_break,kayak,skiing,global_warming\n",
      "## 2004-01-04,2004-01-10,65,36,17,85,13\n",
      "## 2004-01-11,2004-01-17,79,40,18,84,18\n",
      "\n",
      "\n",
      "# Giving it index_col=0 uses the week_start as the index \n",
      "# and parse_dates=True parses them as a date.\n",
      "df = pd.read_csv('trends.csv',index_col=0,parse_dates=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's take a look at the first few entries of the pandas dataframe to see how it looks:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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>week_end</th>\n",
        "      <th>textbooks</th>\n",
        "      <th>spring_break</th>\n",
        "      <th>kayak</th>\n",
        "      <th>skiing</th>\n",
        "      <th>global_warming</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>week_start</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-04</th>\n",
        "      <td> 2004-01-10</td>\n",
        "      <td> 65</td>\n",
        "      <td> 36</td>\n",
        "      <td> 17</td>\n",
        "      <td> 85</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-11</th>\n",
        "      <td> 2004-01-17</td>\n",
        "      <td> 79</td>\n",
        "      <td> 40</td>\n",
        "      <td> 18</td>\n",
        "      <td> 84</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-18</th>\n",
        "      <td> 2004-01-24</td>\n",
        "      <td> 56</td>\n",
        "      <td> 44</td>\n",
        "      <td> 18</td>\n",
        "      <td> 77</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-25</th>\n",
        "      <td> 2004-01-31</td>\n",
        "      <td> 39</td>\n",
        "      <td> 43</td>\n",
        "      <td> 18</td>\n",
        "      <td> 75</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-01</th>\n",
        "      <td> 2004-02-07</td>\n",
        "      <td> 28</td>\n",
        "      <td> 41</td>\n",
        "      <td> 16</td>\n",
        "      <td> 63</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "              week_end  textbooks  spring_break  kayak  skiing  global_warming\n",
        "week_start                                                                    \n",
        "2004-01-04  2004-01-10         65            36     17      85              13\n",
        "2004-01-11  2004-01-17         79            40     18      84              18\n",
        "2004-01-18  2004-01-24         56            44     18      77              14\n",
        "2004-01-25  2004-01-31         39            43     18      75              15\n",
        "2004-02-01  2004-02-07         28            41     16      63              16"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NB: In the table above it looks like the first row is \"week_start\" followed by a bunch of blank entries, but in fact this is just showing that the label for the indices is called \"week_start\". We can grab the first row of the data frame to verify this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.iloc[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "week_end          2004-01-10\n",
        "textbooks                 65\n",
        "spring_break              36\n",
        "kayak                     17\n",
        "skiing                    85\n",
        "global_warming            13\n",
        "Name: 2004-01-04 00:00:00, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I. Plot trends in single frame, with legend"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.spring_break.plot()\n",
      "df.textbooks.plot()\n",
      "df.skiing.plot()\n",
      "df.kayak.plot()\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.legend.Legend at 0x32f1cf0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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Ynk0s/DCUIs/JET5rbg5aPdmKXK/VCw9EOZBYK744cikGWvihx9PnFaeSApHI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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17ddb0f0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let\u2019s look at some summary statistics:\n",
      "df.describe()"
     ],
     "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>textbooks</th>\n",
        "      <th>spring_break</th>\n",
        "      <th>kayak</th>\n",
        "      <th>skiing</th>\n",
        "      <th>global_warming</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 346.000000</td>\n",
        "      <td> 346.00000</td>\n",
        "      <td> 346.000000</td>\n",
        "      <td> 346.000000</td>\n",
        "      <td> 346.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  22.263006</td>\n",
        "      <td>  20.34104</td>\n",
        "      <td>  40.343931</td>\n",
        "      <td>  25.158960</td>\n",
        "      <td>  26.057803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  15.208032</td>\n",
        "      <td>  16.50224</td>\n",
        "      <td>  14.900255</td>\n",
        "      <td>  20.202616</td>\n",
        "      <td>  15.060347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   7.000000</td>\n",
        "      <td>   5.00000</td>\n",
        "      <td>  15.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td>   5.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  13.000000</td>\n",
        "      <td>   8.25000</td>\n",
        "      <td>  29.000000</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  16.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  16.000000</td>\n",
        "      <td>  13.00000</td>\n",
        "      <td>  39.000000</td>\n",
        "      <td>  16.000000</td>\n",
        "      <td>  22.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  23.000000</td>\n",
        "      <td>  30.00000</td>\n",
        "      <td>  51.000000</td>\n",
        "      <td>  36.000000</td>\n",
        "      <td>  31.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  90.000000</td>\n",
        "      <td>  85.00000</td>\n",
        "      <td>  76.000000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  82.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "        textbooks  spring_break       kayak      skiing  global_warming\n",
        "count  346.000000     346.00000  346.000000  346.000000      346.000000\n",
        "mean    22.263006      20.34104   40.343931   25.158960       26.057803\n",
        "std     15.208032      16.50224   14.900255   20.202616       15.060347\n",
        "min      7.000000       5.00000   15.000000    6.000000        5.000000\n",
        "25%     13.000000       8.25000   29.000000   10.000000       16.250000\n",
        "50%     16.000000      13.00000   39.000000   16.000000       22.000000\n",
        "75%     23.000000      30.00000   51.000000   36.000000       31.000000\n",
        "max     90.000000      85.00000   76.000000  100.000000       82.000000"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**II. Determine maxima and minima of each trend term**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Determine in which week of each year (for all five search trends including \"global warming\") that search reached its peak and its minimum."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can query the pandas data frame to return to us all values where a year is a particular value"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend = df.global_warming\n",
      "for yr in range(2004,2011):\n",
      "    sub_df = df[df.index.yr == yr]\n",
      "    print sub_df[trend == max(trend)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AttributeError",
       "evalue": "'DatetimeIndex' object has no attribute 'yr'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-29-5de8abbceb5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_warming\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2004\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2011\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0msub_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myr\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0myr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0msub_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mAttributeError\u001b[0m: 'DatetimeIndex' object has no attribute 'yr'"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates = trends.week_start\n",
      "yrs = zeros_like(dates)\n",
      "wks = zeros_like(dates)\n",
      "for i in range(len(dates)):\n",
      "    yrs[i] = dates[i].year\n",
      "    wks[i] = dates[i].isocalendar()[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For each year, list week numbers corresponding to maximum and minimum search values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trend = trends.global_warming\n",
      "for yr in range(2004,2011):\n",
      "    idx = find(yrs==yr)\n",
      "    print yr, wks[find(trend[idx] == max(trend[idx]))], wks[find(trend[idx] == min(trend[idx]))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**III. Study scatter about median values**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Q: Is it true that $\\sigma(A) > \\sigma(B) \\Rightarrow \\sigma_\\textrm{median}(A) > \\sigma_\\textrm{median}(B)$?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def std_median(datums):\n",
      "    return sqrt( sum( (datums - median(datums))**2 ) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print std_median(trends.spring_break)\n",
      "print std_median(trends.textbooks)\n",
      "print std_median(trends.skiing)\n",
      "print std_median(trends.kayak)\n",
      "print std_median(trends.global_warming)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**IV. Correlation between trends**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = np.correlate(trends.skiing,trends.spring_break, mode='full')\n",
      "gap = arange(result.size) - result.size/2\n",
      "plot(gap,result)\n",
      "print gap[find(result==max(result))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = np.correlate(trends.skiing,trends.global_warming, mode='full')\n",
      "gap = arange(result.size) - result.size/2\n",
      "plot(gap,result)\n",
      "print gap[find(result==max(result))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "&nbsp;"
    }
   ],
   "metadata": {}
  }
 ]
}