test_pyspark.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Example IPython Notebook running with PySpark"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After completing the setup [here](http://example.com), this is what an example IPython Notebook session could look like.\n",
      "\n",
      "You will first notice that when a notebook first connects to the IPython kernel, the notebook server will spit out all the usual PySpark output about initializing Spark.\n",
      "\n",
      "First start by seeing that there does exist a `SparkContext` object in the `sc` variable:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<pyspark.context.SparkContext object at 0x26e7990>\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's load an RDD with some interesting data.  We have the GDELT event data set on our cluster as a collection of tab-delimited text files:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw_events = sc.textFile('/user/laserson/gdelt/events').map(lambda x: x.split('\\t'))\n",
      "# raw_events.cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note how we've cached the event data in distributed memory.\n",
      "\n",
      "Let's see what an object in the RDD looks like"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print raw_events.first()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'0', u'19790101', u'197901', u'1979', u'1979.0027', u'', u'', u'', u'', u'', u'', u'', u'', u'', u'', u'AFR', u'AFRICA', u'AFR', u'', u'', u'', u'', u'', u'', u'', u'1', u'040', u'040', u'04', u'1', u'1', u'9', u'1', u'9', u'5.52631578947368', u'', u'', u'', u'', u'0', u'0', u'0', u'', u'', u'', u'', u'0', u'0', u'0', u'', u'', u'', u'', u'0', u'0', u'', u'20130203']\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's count the number of events we have.  You can follow the progress of the computation using the Spark application web server."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print raw_events.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "61260109\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see that we have about 60 million events at our disposal.\n",
      "\n",
      "The GDELT event data set collects geopolitical events that occur around the world.  Each event is tagged with a Goldstein scale value that measures the potential for the event to destabilize the country.  Let's compute and plot a histogram of the Goldstein scale values across all the events in the database.  The Goldstein scale value is present in the 31st field.\n",
      "\n",
      "First, let's make sure that plotting images are set to be displayed inline (see the IPython docs):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we'll just confirm that all the Goldstein values are indeed between -10 and 10."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = raw_events.map(lambda x: float(x[30])).min()\n",
      "M = raw_events.map(lambda x: float(x[30])).max()\n",
      "print \"Min is %f\\nMax is %f\" % (m, M)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Min is -10.000000\n",
        "Max is 10.000000\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we compute the histogram.  This is slightly hacky, but _c'est la vie_:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = np.arange(-10, 11)\n",
      "one_hot = raw_events.map(lambda x: float(x[30])).map(lambda x: np.histogram([x], bins=bins)[0])\n",
      "hist = one_hot.reduce(lambda x, y: x + y)\n",
      "print hist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 4315780   562319   516529   938546   100780  3890452  1816766        0\n",
        "  4678160  1290723  6450944 10235827  4893120  6673389  4912603  1487392\n",
        "  1056329  5624014  1400922   415514]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally we plot the histogram"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "t = ax.bar(bins[:-1], hist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEGCAYAAACaSwWnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAGAQQh0ADPJfG9OMJ+RbQgkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2556bd0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also plot the number of events each day for the 5 countries that have the most events in the year 2001.\n",
      "\n",
      "First we can see the number of unique countries that are available.  Note that we filter out events that don't list a country code."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw_events.filter(lambda x: x[7] != '').map(lambda x: x[7]).distinct().collect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[u'',\n",
        " u'SAS',\n",
        " u'NMR',\n",
        " u'IDN',\n",
        " u'ALB',\n",
        " u'KHM',\n",
        " u'TUN',\n",
        " u'COK',\n",
        " u'SHN',\n",
        " u'THA',\n",
        " u'ESP',\n",
        " u'BLZ',\n",
        " u'IND',\n",
        " u'CAS',\n",
        " u'TON',\n",
        " u'COM',\n",
        " u'NAF',\n",
        " u'UKR',\n",
        " u'TGO',\n",
        " u'AZE',\n",
        " u'COL',\n",
        " u'MEX',\n",
        " u'NOR',\n",
        " u'BGR',\n",
        " u'LTU',\n",
        " u'AFR',\n",
        " u'SAU',\n",
        " u'CRI',\n",
        " u'TKM',\n",
        " u'EUR',\n",
        " u'DJI',\n",
        " u'NER',\n",
        " u'LAM',\n",
        " u'WLF',\n",
        " u'JOR',\n",
        " u'CAU',\n",
        " u'FJI',\n",
        " u'PRK',\n",
        " u'MMR',\n",
        " u'LAO',\n",
        " u'EAF',\n",
        " u'BLR',\n",
        " u'LBN',\n",
        " u'BEN',\n",
        " u'TTO',\n",
        " u'YEM',\n",
        " u'GEO',\n",
        " u'STP',\n",
        " u'PRT',\n",
        " u'HKG',\n",
        " u'LSO',\n",
        " u'KAZ',\n",
        " u'FRA',\n",
        " u'UGA',\n",
        " u'BEL',\n",
        " u'BTN',\n",
        " u'NAM',\n",
        " u'AIA',\n",
        " u'GBR',\n",
        " u'DMA',\n",
        " u'SVK',\n",
        " u'KIR',\n",
        " u'BWA',\n",
        " u'CAN',\n",
        " u'IRQ',\n",
        " u'GHA',\n",
        " u'BFA',\n",
        " u'CYM',\n",
        " u'DNK',\n",
        " u'AUS',\n",
        " u'VAT',\n",
        " u'BLK',\n",
        " u'PNG',\n",
        " u'MDA',\n",
        " u'MAR',\n",
        " u'MLI',\n",
        " u'ABW',\n",
        " u'DZA',\n",
        " u'MDG',\n",
        " u'HND',\n",
        " u'AUT',\n",
        " u'PGS',\n",
        " u'VUT',\n",
        " u'MYS',\n",
        " u'VNM',\n",
        " u'PER',\n",
        " u'SLV',\n",
        " u'JPN',\n",
        " u'BMU',\n",
        " u'ZAF',\n",
        " u'TMP',\n",
        " u'PRY',\n",
        " u'CAF',\n",
        " u'NIC',\n",
        " u'SUR',\n",
        " u'BRN',\n",
        " u'NGA',\n",
        " u'EGY',\n",
        " u'HTI',\n",
        " u'JAM',\n",
        " u'CZE',\n",
        " u'LKA',\n",
        " u'PLW',\n",
        " u'SOM',\n",
        " u'BOL',\n",
        " u'AND',\n",
        " u'IRL',\n",
        " u'KEN',\n",
        " u'ISR',\n",
        " u'ARE',\n",
        " u'CHE',\n",
        " u'NLD',\n",
        " u'SDN',\n",
        " u'IRN',\n",
        " u'MLT',\n",
        " u'HRV',\n",
        " u'ARG',\n",
        " u'SRB',\n",
        " u'ATG',\n",
        " u'GTM',\n",
        " u'ZWE',\n",
        " u'BHS',\n",
        " u'MAC',\n",
        " u'ETH',\n",
        " u'TJK',\n",
        " u'UZB',\n",
        " u'BHR',\n",
        " u'KNA',\n",
        " u'MDV',\n",
        " u'WSM',\n",
        " u'CHL',\n",
        " u'SWE',\n",
        " u'QAT',\n",
        " u'MOZ',\n",
        " u'WAF',\n",
        " u'SMR',\n",
        " u'ARM',\n",
        " u'SLE',\n",
        " u'RUS',\n",
        " u'BRB',\n",
        " u'USA',\n",
        " u'POL',\n",
        " u'CHN',\n",
        " u'GRD',\n",
        " u'SEA',\n",
        " u'ASA',\n",
        " u'BRA',\n",
        " u'GNQ',\n",
        " u'MNG',\n",
        " u'SYC',\n",
        " u'LVA',\n",
        " u'MEA',\n",
        " u'LUX',\n",
        " u'NPL',\n",
        " u'GRC',\n",
        " u'SEN',\n",
        " u'TCD',\n",
        " u'SAM',\n",
        " u'SLB',\n",
        " u'ISL',\n",
        " u'LBR',\n",
        " u'MKD',\n",
        " u'SWZ',\n",
        " u'DOM',\n",
        " u'FIN',\n",
        " u'SGP',\n",
        " u'KWT',\n",
        " u'CMR',\n",
        " u'WST',\n",
        " u'MWI',\n",
        " u'MUS',\n",
        " u'AFG',\n",
        " u'LBY',\n",
        " u'TZA',\n",
        " u'BDI',\n",
        " u'VCT',\n",
        " u'AGO',\n",
        " u'GIN',\n",
        " u'SAF',\n",
        " u'URY',\n",
        " u'CUB',\n",
        " u'DEU',\n",
        " u'MHL',\n",
        " u'NRU',\n",
        " u'CPV',\n",
        " u'TUR',\n",
        " u'CYP',\n",
        " u'LCA',\n",
        " u'EEU',\n",
        " u'ZMB',\n",
        " u'GNB',\n",
        " u'BGD',\n",
        " u'VEN',\n",
        " u'LIE',\n",
        " u'ROM',\n",
        " u'GAB',\n",
        " u'GMB',\n",
        " u'RWA',\n",
        " u'ECU',\n",
        " u'HUN',\n",
        " u'TUV',\n",
        " u'PAK',\n",
        " u'ITA',\n",
        " u'PHL',\n",
        " u'MRT',\n",
        " u'CRB',\n",
        " u'MCO',\n",
        " u'OMN',\n",
        " u'FSM',\n",
        " u'SYR',\n",
        " u'CIV',\n",
        " u'ERI',\n",
        " u'COD',\n",
        " u'GUY',\n",
        " u'COG',\n",
        " u'PAN',\n",
        " u'SCN',\n",
        " u'KGZ',\n",
        " u'PSE',\n",
        " u'EST',\n",
        " u'KOR',\n",
        " u'NZL']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we convert each event into counts.  We will aggregate by country and day, for all events in the year 2001."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events_2001 = raw_events.filter(lambda x: x[7] != '').filter(lambda x: x[1] >= '20010101').filter(lambda x: x[1] <= '20011231')\n",
      "events_2001.cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "PythonRDD[12] at RDD at PythonRDD.scala:40"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events_2001.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "3162516"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_day_counts = events_2001.filter(lambda x: x[7] != '').map(lambda x: ((x[7], x[1]), 1)).reduceByKey(lambda x, y: x + y)\n",
      "country_day_counts.cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "PythonRDD[22] at RDD at PythonRDD.scala:40"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_day_counts.take(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "[((u'VEN', u'20010602'), 12),\n",
        " ((u'GHA', u'20011224'), 15),\n",
        " ((u'BWA', u'20011224'), 2),\n",
        " ((u'PHL', u'20010223'), 63),\n",
        " ((u'IRN', u'20010428'), 186)]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from dateutil.parser import parse as parse_date\n",
      "epoch = parse_date('19700101')\n",
      "def td2s(td):\n",
      "    return (td.microseconds + (td.seconds + td.days * 24 * 3600) * 1000000) / 1e6\n",
      "def day2unix(day):\n",
      "    return td2s(parse_date(day) - epoch)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_series = country_day_counts \\\n",
      "    .map(lambda x: (x[0][0], (day2unix(x[0][1]), x[1]))) \\\n",
      "    .groupByKey() \\\n",
      "    .map(lambda x: (x[0], sorted(x[1], key=lambda y: y[0]))) \\\n",
      "    .map(lambda x: (x[0], zip(*x[1]))) \\\n",
      "    .map(lambda x: (x[0], x[1][0], x[1][1]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_series.cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "PythonRDD[77] at RDD at PythonRDD.scala:40"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_series.first()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "(u'NMR',\n",
        " (978307200.0,\n",
        "  978393600.0,\n",
        "  978480000.0,\n",
        "  978652800.0,\n",
        "  978739200.0,\n",
        "  978825600.0,\n",
        "  978912000.0,\n",
        "  978998400.0,\n",
        "  979084800.0,\n",
        "  979689600.0,\n",
        "  980121600.0,\n",
        "  980294400.0,\n",
        "  980467200.0,\n",
        "  980640000.0,\n",
        "  980726400.0,\n",
        "  980812800.0,\n",
        "  980899200.0,\n",
        "  981331200.0,\n",
        "  981417600.0,\n",
        "  981504000.0,\n",
        "  981590400.0,\n",
        "  981676800.0,\n",
        "  981849600.0,\n",
        "  982108800.0,\n",
        "  982195200.0,\n",
        "  982281600.0,\n",
        "  982368000.0,\n",
        "  982540800.0,\n",
        "  982713600.0,\n",
        "  982800000.0,\n",
        "  982886400.0,\n",
        "  982972800.0,\n",
        "  983059200.0,\n",
        "  983232000.0,\n",
        "  983750400.0,\n",
        "  983836800.0,\n",
        "  983923200.0,\n",
        "  984096000.0,\n",
        "  984182400.0,\n",
        "  984355200.0,\n",
        "  984441600.0,\n",
        "  984528000.0,\n",
        "  984873600.0,\n",
        "  985132800.0,\n",
        "  985219200.0,\n",
        "  985392000.0,\n",
        "  985478400.0,\n",
        "  985564800.0,\n",
        "  985651200.0,\n",
        "  985737600.0,\n",
        "  985910400.0,\n",
        "  986169600.0,\n",
        "  986342400.0,\n",
        "  986515200.0,\n",
        "  986774400.0,\n",
        "  986947200.0,\n",
        "  987292800.0,\n",
        "  987379200.0,\n",
        "  987465600.0,\n",
        "  987552000.0,\n",
        "  987638400.0,\n",
        "  987724800.0,\n",
        "  987811200.0,\n",
        "  987897600.0,\n",
        "  987984000.0,\n",
        "  988156800.0,\n",
        "  988329600.0,\n",
        "  988675200.0,\n",
        "  988848000.0,\n",
        "  989020800.0,\n",
        "  989193600.0,\n",
        "  989366400.0,\n",
        "  989625600.0,\n",
        "  989884800.0,\n",
        "  989971200.0,\n",
        "  990057600.0,\n",
        "  990316800.0,\n",
        "  990403200.0,\n",
        "  990489600.0,\n",
        "  990576000.0,\n",
        "  990662400.0,\n",
        "  990748800.0,\n",
        "  990835200.0,\n",
        "  991094400.0,\n",
        "  991180800.0,\n",
        "  991267200.0,\n",
        "  991353600.0,\n",
        "  991699200.0,\n",
        "  991872000.0,\n",
        "  991958400.0,\n",
        "  992217600.0,\n",
        "  992304000.0,\n",
        "  992390400.0,\n",
        "  992476800.0,\n",
        "  992995200.0,\n",
        "  993081600.0,\n",
        "  993340800.0,\n",
        "  993513600.0,\n",
        "  993600000.0,\n",
        "  993686400.0,\n",
        "  994032000.0,\n",
        "  994118400.0,\n",
        "  994204800.0,\n",
        "  994291200.0,\n",
        "  994550400.0,\n",
        "  994636800.0,\n",
        "  994809600.0,\n",
        "  994896000.0,\n",
        "  995414400.0,\n",
        "  995500800.0,\n",
        "  995587200.0,\n",
        "  995846400.0,\n",
        "  996019200.0,\n",
        "  996192000.0,\n",
        "  996364800.0,\n",
        "  996451200.0,\n",
        "  996710400.0,\n",
        "  996796800.0,\n",
        "  997142400.0,\n",
        "  997228800.0,\n",
        "  997401600.0,\n",
        "  997574400.0,\n",
        "  997660800.0,\n",
        "  997747200.0,\n",
        "  997833600.0,\n",
        "  998438400.0,\n",
        "  998524800.0,\n",
        "  998956800.0,\n",
        "  999043200.0,\n",
        "  999216000.0,\n",
        "  999388800.0,\n",
        "  999561600.0,\n",
        "  999648000.0,\n",
        "  999734400.0,\n",
        "  999820800.0,\n",
        "  1000080000.0,\n",
        "  1000252800.0,\n",
        "  1000425600.0,\n",
        "  1000598400.0,\n",
        "  1000684800.0,\n",
        "  1000857600.0,\n",
        "  1001030400.0,\n",
        "  1001116800.0,\n",
        "  1001289600.0,\n",
        "  1001548800.0,\n",
        "  1001635200.0,\n",
        "  1001808000.0,\n",
        "  1001980800.0,\n",
        "  1002067200.0,\n",
        "  1002153600.0,\n",
        "  1002240000.0,\n",
        "  1002412800.0,\n",
        "  1002499200.0,\n",
        "  1002585600.0,\n",
        "  1002672000.0,\n",
        "  1002758400.0,\n",
        "  1002844800.0,\n",
        "  1002931200.0,\n",
        "  1003104000.0,\n",
        "  1003190400.0,\n",
        "  1003363200.0,\n",
        "  1003622400.0,\n",
        "  1003708800.0,\n",
        "  1003795200.0,\n",
        "  1004054400.0,\n",
        "  1004140800.0,\n",
        "  1004227200.0,\n",
        "  1004313600.0,\n",
        "  1004745600.0,\n",
        "  1004918400.0,\n",
        "  1005004800.0,\n",
        "  1005091200.0,\n",
        "  1005436800.0,\n",
        "  1005523200.0,\n",
        "  1005696000.0,\n",
        "  1005782400.0,\n",
        "  1005868800.0,\n",
        "  1006128000.0,\n",
        "  1006214400.0,\n",
        "  1006300800.0,\n",
        "  1006905600.0,\n",
        "  1006992000.0,\n",
        "  1007164800.0,\n",
        "  1007251200.0,\n",
        "  1007424000.0,\n",
        "  1007769600.0,\n",
        "  1008028800.0,\n",
        "  1008115200.0,\n",
        "  1008201600.0,\n",
        "  1008460800.0,\n",
        "  1008547200.0,\n",
        "  1008633600.0,\n",
        "  1008720000.0,\n",
        "  1009238400.0,\n",
        "  1009411200.0,\n",
        "  1009584000.0,\n",
        "  1009670400.0),\n",
        " (1,\n",
        "  2,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  2,\n",
        "  4,\n",
        "  1,\n",
        "  5,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  3,\n",
        "  2,\n",
        "  1,\n",
        "  4,\n",
        "  2,\n",
        "  1,\n",
        "  3,\n",
        "  3,\n",
        "  5,\n",
        "  1,\n",
        "  3,\n",
        "  8,\n",
        "  7,\n",
        "  12,\n",
        "  2,\n",
        "  2,\n",
        "  11,\n",
        "  7,\n",
        "  6,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  2,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  4,\n",
        "  2,\n",
        "  4,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  5,\n",
        "  2,\n",
        "  5,\n",
        "  3,\n",
        "  3,\n",
        "  5,\n",
        "  1,\n",
        "  4,\n",
        "  2,\n",
        "  1,\n",
        "  4,\n",
        "  2,\n",
        "  9,\n",
        "  19,\n",
        "  2,\n",
        "  10,\n",
        "  1,\n",
        "  4,\n",
        "  10,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  8,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  3,\n",
        "  2,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  3,\n",
        "  6,\n",
        "  1,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  4,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  6,\n",
        "  4,\n",
        "  1,\n",
        "  4,\n",
        "  2,\n",
        "  1,\n",
        "  1,\n",
        "  3,\n",
        "  2,\n",
        "  4,\n",
        "  3,\n",
        "  2,\n",
        "  7,\n",
        "  2,\n",
        "  7,\n",
        "  2,\n",
        "  3,\n",
        "  4,\n",
        "  1,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  16,\n",
        "  1,\n",
        "  1,\n",
        "  3,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  2,\n",
        "  2,\n",
        "  4,\n",
        "  4,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  1,\n",
        "  7,\n",
        "  5,\n",
        "  2,\n",
        "  3,\n",
        "  7,\n",
        "  4,\n",
        "  8,\n",
        "  3,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  6,\n",
        "  2,\n",
        "  5,\n",
        "  2,\n",
        "  4,\n",
        "  4,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  1,\n",
        "  4,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  4,\n",
        "  2,\n",
        "  1,\n",
        "  2,\n",
        "  4,\n",
        "  3,\n",
        "  2,\n",
        "  2,\n",
        "  3,\n",
        "  3,\n",
        "  2,\n",
        "  1,\n",
        "  4,\n",
        "  3,\n",
        "  3,\n",
        "  3,\n",
        "  2,\n",
        "  3,\n",
        "  1,\n",
        "  2,\n",
        "  2,\n",
        "  2,\n",
        "  1,\n",
        "  3,\n",
        "  2,\n",
        "  2,\n",
        "  1,\n",
        "  4,\n",
        "  3,\n",
        "  1,\n",
        "  1,\n",
        "  1,\n",
        "  1,\n",
        "  2,\n",
        "  1,\n",
        "  1,\n",
        "  7,\n",
        "  6,\n",
        "  4))"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we're ready to plot.  Each object in `country_series` has the information we need to plot a single line."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "random_color = lambda: '#%02x%02x%02x' % tuple(np.random.randint(0,256,3))\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "for (country, times, events) in country_series.takeOrdered(10, lambda x: -sum(x[2])):\n",
      "    t = ax.plot(times, events, lw=1, c=random_color())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAENCAYAAAAL98L+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDIOCgoKCQhSKYVBQUFBQiOL/AzHTuGz9GybpAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3903850>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What's the big spike for the blue line above?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_day_counts.reduce(lambda x, y: max(x, y, key=lambda z: z[1]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "((u'USA', u'20010912'), 2387)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like it was the day after September 11th."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}