nbconvert1/tests/ipynbref/Gun_Data.orig.ipynb

{
 "metadata": {
  "name": "Gun Data"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Some gun violence analysis with Wikipedia data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As [requested by John Stokes](https://twitter.com/jonst0kes/status/282330530412888064),\n",
      "here are per-capita numbers for gun-related homicides,\n",
      "relating to GDP and total homicides,\n",
      "so the situation in the United States can be put in context relative to other nations."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "main data source is UNODC (via Wikipedia [here](http://en.wikipedia.org/wiki/List_of_countries_by_intentional_homicide_rate)\n",
      "and [here](http://en.wikipedia.org/wiki/List_of_countries_by_firearm-related_death_rate)).\n",
      "\n",
      "GDP data from World Bank, again [via Wikipedia](http://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita).\n",
      "\n",
      "If the numbers on Wikipedia are inaccurate, or their relationship is not sound\n",
      "(e.g. numbers taken from different years, during which significant change occured)\n",
      "then obviously None of this analysis is valid.\n",
      "\n",
      "To summarize the data,\n",
      "every possible way you look at it the US is lousy at preventing gun violence.\n",
      "Even when compared to significantly more violent places,\n",
      "gun violence in the US is a serious problem,\n",
      "and when compared to similarly wealthy places,\n",
      "the US is an outstanding disaster."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**UPDATE:** the relationship of the gun data and totals does not seem to be valid.\n",
      "[FBI data](http://www2.fbi.gov/ucr/cius2009/offenses/violent_crime/index.html) suggests that\n",
      "the relative contribution of guns to homicides in the US is 47%,\n",
      "but relating these two data sources gives 80%.\n",
      "Internal comparisons should still be fine, but 'fraction' analysis has been stricken."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext retina\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display\n",
      "import pandas\n",
      "pandas.set_option('display.notebook_repr_html', True)\n",
      "pandas.set_option('display.precision', 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some utility functions for display"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_percent(df, limit=10):\n",
      "    df['Gun Percent'][:limit].plot()\n",
      "    plt.ylim(0,100)\n",
      "    plt.title(\"% Gun Homicide\")\n",
      "    plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_percapita(df, limit=10):\n",
      "    df = df.ix[:,['Homicides', 'Gun Homicides']][:limit]\n",
      "    df['Total Homicides'] = df['Homicides'] - df['Gun Homicides']\n",
      "    del df['Homicides']\n",
      "    df.plot(kind='bar', stacked=True, sort_columns=True)\n",
      "    plt.ylabel(\"per 100k\")\n",
      "    plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_relevant(df, limit=10):\n",
      "    display(df.ix[:,['Homicides', 'Gun Homicides', 'Gun Data Source']][:limit])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "totals = pandas.read_csv('totals.csv', '\\t', index_col=0)\n",
      "guns = pandas.read_csv('guns.csv', '\\t', index_col=0)\n",
      "gdp = pandas.read_csv('gdp.csv', '\\t', index_col=1)\n",
      "data = totals.join(guns).join(gdp)\n",
      "data['Gun Percent'] = 100 * data['Gun Homicides'] / data['Homicides']\n",
      "del data['Unintentional'],data['Undetermined'],data['Gun Suicides']\n",
      "data = data.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Of all sampled countries (Found data for 68 countries),\n",
      "the US is in the top 15 in Gun Homicides per capita.\n",
      "\n",
      "Numbers are per 100k."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data.sort(\"Gun Homicides\", ascending=False)\n",
      "display_relevant(data, 15)"
     ],
     "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>Homicides</th>\n",
        "      <th>Gun Homicides</th>\n",
        "      <th>Gun Data Source</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>El Salvador</strong></td>\n",
        "      <td> 69.2</td>\n",
        "      <td> 50.4</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Jamaica</strong></td>\n",
        "      <td> 52.2</td>\n",
        "      <td> 47.4</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Honduras</strong></td>\n",
        "      <td> 91.6</td>\n",
        "      <td> 46.7</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Guatemala</strong></td>\n",
        "      <td> 38.5</td>\n",
        "      <td> 38.5</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Colombia</strong></td>\n",
        "      <td> 33.4</td>\n",
        "      <td> 27.1</td>\n",
        "      <td> UNODC 2011 [2]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Brazil</strong></td>\n",
        "      <td> 21.0</td>\n",
        "      <td> 18.1</td>\n",
        "      <td>  UNODC 2011[3]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Panama</strong></td>\n",
        "      <td> 21.6</td>\n",
        "      <td> 12.9</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Mexico</strong></td>\n",
        "      <td> 16.9</td>\n",
        "      <td> 10.0</td>\n",
        "      <td>  UNODC 2011[4]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Paraguay</strong></td>\n",
        "      <td> 11.5</td>\n",
        "      <td>  7.3</td>\n",
        "      <td> UNODC 2000[11]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Nicaragua</strong></td>\n",
        "      <td> 13.6</td>\n",
        "      <td>  7.1</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>United States</strong></td>\n",
        "      <td>  4.2</td>\n",
        "      <td>  3.7</td>\n",
        "      <td> OAS 2012[5][6]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Costa Rica</strong></td>\n",
        "      <td> 10.0</td>\n",
        "      <td>  3.3</td>\n",
        "      <td>  UNODC 2002[7]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Uruguay</strong></td>\n",
        "      <td>  5.9</td>\n",
        "      <td>  3.2</td>\n",
        "      <td>  UNODC 2002[7]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Argentina</strong></td>\n",
        "      <td>  3.4</td>\n",
        "      <td>  3.0</td>\n",
        "      <td> UNODC 2011[12]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Barbados</strong></td>\n",
        "      <td> 11.3</td>\n",
        "      <td>  3.0</td>\n",
        "      <td> UNODC 2000[11]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "display_data",
       "text": [
        "               Homicides  Gun Homicides Gun Data Source\n",
        "Country                                                \n",
        "El Salvador         69.2           50.4     OAS 2011[1]\n",
        "Jamaica             52.2           47.4     OAS 2011[1]\n",
        "Honduras            91.6           46.7     OAS 2011[1]\n",
        "Guatemala           38.5           38.5     OAS 2011[1]\n",
        "Colombia            33.4           27.1  UNODC 2011 [2]\n",
        "Brazil              21.0           18.1   UNODC 2011[3]\n",
        "Panama              21.6           12.9     OAS 2011[1]\n",
        "Mexico              16.9           10.0   UNODC 2011[4]\n",
        "Paraguay            11.5            7.3  UNODC 2000[11]\n",
        "Nicaragua           13.6            7.1     OAS 2011[1]\n",
        "United States        4.2            3.7  OAS 2012[5][6]\n",
        "Costa Rica          10.0            3.3   UNODC 2002[7]\n",
        "Uruguay              5.9            3.2   UNODC 2002[7]\n",
        "Argentina            3.4            3.0  UNODC 2011[12]\n",
        "Barbados            11.3            3.0  UNODC 2000[11]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take top 30 Countries by GDP"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top = data.sort('GDP')[-30:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and rank them by Gun Homicides per capita:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_by_guns = top.sort(\"Gun Homicides\", ascending=False)\n",
      "display_relevant(top_by_guns, 5)\n",
      "plot_percapita(top_by_guns, 10)"
     ],
     "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>Homicides</th>\n",
        "      <th>Gun Homicides</th>\n",
        "      <th>Gun Data Source</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>United States</strong></td>\n",
        "      <td> 4.2</td>\n",
        "      <td> 3.7</td>\n",
        "      <td> OAS 2012[5][6]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Israel</strong></td>\n",
        "      <td> 2.1</td>\n",
        "      <td> 0.9</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Canada</strong></td>\n",
        "      <td> 1.6</td>\n",
        "      <td> 0.8</td>\n",
        "      <td>  Krug 1998[13]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Luxembourg</strong></td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 0.6</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Greece</strong></td>\n",
        "      <td> 1.5</td>\n",
        "      <td> 0.6</td>\n",
        "      <td>  Krug 1998[13]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "display_data",
       "text": [
        "               Homicides  Gun Homicides Gun Data Source\n",
        "Country                                                \n",
        "United States        4.2            3.7  OAS 2012[5][6]\n",
        "Israel               2.1            0.9    WHO 2012[10]\n",
        "Canada               1.6            0.8   Krug 1998[13]\n",
        "Luxembourg           2.5            0.6    WHO 2012[10]\n",
        "Greece               1.5            0.6   Krug 1998[13]"
       ]
      },
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SJUv0wgsvKCwsTJLUrl07zZo1S02aNLnqr127dq327NmjrKwsrVy5UpLUqVMn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\nFRkZaTgZrsZTnh15KwztfPLJJ+rbt6+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       "text": [
        "<matplotlib.figure.Figure at 0x10d745d90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**NOTE:** these bar graphs should not be interpreted as fractions of a total,\n",
      "as the two data sources do not appear to be comparable.\n",
      "But the red and blue bar graphs should still be internally comparable."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The US is easily #1 of 30 wealthiest countries in Gun Homicides per capita,\n",
      "by a factor of 4:1"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Adding USA, Canada, and Mexico to all of Europe,\n",
      "USA is a strong #2 behind Mexico in total gun homicides per-capita"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index = (data['Region'] == 'Europe') + \\\n",
      "        (data.index == 'United States') + \\\n",
      "        (data.index == 'Canada') + \\\n",
      "        (data.index == 'Mexico')\n",
      "selected = data[index]\n",
      "\n",
      "print \"By Total Gun Homicides\"\n",
      "sys.stdout.flush()\n",
      "\n",
      "by_guns = selected.sort(\"Gun Homicides\", ascending=False)\n",
      "#by_guns['Gun Homicides'].plot(kind='bar')\n",
      "plot_percapita(by_guns, limit=25)\n",
      "display_relevant(selected, limit=None)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "By Total Gun Homicides\n"
       ]
      },
      {
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R8+bN04033uh0UwAAOC93eLjSYmMdeRwzPAHIx8dHMTExGjJkiLd3JiwsTDNmzJCfX+5H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\nbdq0sexYqssvv1yrVq3yDjV6FmPt3r3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       "text": [
        "<matplotlib.figure.Figure at 0x10d76f790>"
       ]
      },
      {
       "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>Homicides</th>\n",
        "      <th>Gun Homicides</th>\n",
        "      <th>Gun Data Source</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>Mexico</strong></td>\n",
        "      <td> 16.9</td>\n",
        "      <td> 10.0</td>\n",
        "      <td>  UNODC 2011[4]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>United States</strong></td>\n",
        "      <td>  4.2</td>\n",
        "      <td>  3.7</td>\n",
        "      <td> OAS 2012[5][6]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Montenegro</strong></td>\n",
        "      <td>  3.5</td>\n",
        "      <td>  2.1</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Moldova</strong></td>\n",
        "      <td>  7.5</td>\n",
        "      <td>  1.0</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Canada</strong></td>\n",
        "      <td>  1.6</td>\n",
        "      <td>  0.8</td>\n",
        "      <td>  Krug 1998[13]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Serbia</strong></td>\n",
        "      <td>  1.2</td>\n",
        "      <td>  0.6</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Luxembourg</strong></td>\n",
        "      <td>  2.5</td>\n",
        "      <td>  0.6</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Greece</strong></td>\n",
        "      <td>  1.5</td>\n",
        "      <td>  0.6</td>\n",
        "      <td>  Krug 1998[13]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Croatia</strong></td>\n",
        "      <td>  1.4</td>\n",
        "      <td>  0.6</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Switzerland</strong></td>\n",
        "      <td>  0.7</td>\n",
        "      <td>  0.5</td>\n",
        "      <td>    OAS 2011[1]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Malta</strong></td>\n",
        "      <td>  1.0</td>\n",
        "      <td>  0.5</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Portugal</strong></td>\n",
        "      <td>  1.2</td>\n",
        "      <td>  0.5</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Belarus</strong></td>\n",
        "      <td>  4.9</td>\n",
        "      <td>  0.4</td>\n",
        "      <td>  UNODC 2002[7]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Ireland</strong></td>\n",
        "      <td>  1.2</td>\n",
        "      <td>  0.4</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Italy</strong></td>\n",
        "      <td>  0.9</td>\n",
        "      <td>  0.4</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Ukraine</strong></td>\n",
        "      <td>  5.2</td>\n",
        "      <td>  0.3</td>\n",
        "      <td> UNODC 2000[11]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Estonia</strong></td>\n",
        "      <td>  5.2</td>\n",
        "      <td>  0.3</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Belgium</strong></td>\n",
        "      <td>  1.7</td>\n",
        "      <td>  0.3</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Finland</strong></td>\n",
        "      <td>  2.2</td>\n",
        "      <td>  0.3</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Lithuania</strong></td>\n",
        "      <td>  6.6</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Bulgaria</strong></td>\n",
        "      <td>  2.0</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Georgia</strong></td>\n",
        "      <td>  4.3</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Denmark</strong></td>\n",
        "      <td>  0.9</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>France</strong></td>\n",
        "      <td>  1.1</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Netherlands</strong></td>\n",
        "      <td>  1.1</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Sweden</strong></td>\n",
        "      <td>  1.0</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Slovakia</strong></td>\n",
        "      <td>  1.5</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Austria</strong></td>\n",
        "      <td>  0.6</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Latvia</strong></td>\n",
        "      <td>  3.1</td>\n",
        "      <td>  0.2</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Spain</strong></td>\n",
        "      <td>  0.8</td>\n",
        "      <td>  0.1</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Hungary</strong></td>\n",
        "      <td>  1.3</td>\n",
        "      <td>  0.1</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Czech Republic</strong></td>\n",
        "      <td>  1.7</td>\n",
        "      <td>  0.1</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Germany</strong></td>\n",
        "      <td>  0.8</td>\n",
        "      <td>  0.1</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Slovenia</strong></td>\n",
        "      <td>  0.7</td>\n",
        "      <td>  0.1</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Romania</strong></td>\n",
        "      <td>  2.0</td>\n",
        "      <td>  0.0</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>United Kingdom</strong></td>\n",
        "      <td>  1.2</td>\n",
        "      <td>  0.0</td>\n",
        "      <td>   WHO2012 [10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Norway</strong></td>\n",
        "      <td>  0.6</td>\n",
        "      <td>  0.0</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Poland</strong></td>\n",
        "      <td>  1.1</td>\n",
        "      <td>  0.0</td>\n",
        "      <td>   WHO 2012[10]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "display_data",
       "text": [
        "                Homicides  Gun Homicides Gun Data Source\n",
        "Country                                                 \n",
        "Mexico               16.9           10.0   UNODC 2011[4]\n",
        "United States         4.2            3.7  OAS 2012[5][6]\n",
        "Montenegro            3.5            2.1    WHO 2012[10]\n",
        "Moldova               7.5            1.0    WHO 2012[10]\n",
        "Canada                1.6            0.8   Krug 1998[13]\n",
        "Serbia                1.2            0.6    WHO 2012[10]\n",
        "Luxembourg            2.5            0.6    WHO 2012[10]\n",
        "Greece                1.5            0.6   Krug 1998[13]\n",
        "Croatia               1.4            0.6    WHO 2012[10]\n",
        "Switzerland           0.7            0.5     OAS 2011[1]\n",
        "Malta                 1.0            0.5    WHO 2012[10]\n",
        "Portugal              1.2            0.5    WHO 2012[10]\n",
        "Belarus               4.9            0.4   UNODC 2002[7]\n",
        "Ireland               1.2            0.4    WHO 2012[10]\n",
        "Italy                 0.9            0.4    WHO 2012[10]\n",
        "Ukraine               5.2            0.3  UNODC 2000[11]\n",
        "Estonia               5.2            0.3    WHO 2012[10]\n",
        "Belgium               1.7            0.3    WHO 2012[10]\n",
        "Finland               2.2            0.3    WHO 2012[10]\n",
        "Lithuania             6.6            0.2    WHO 2012[10]\n",
        "Bulgaria              2.0            0.2    WHO 2012[10]\n",
        "Georgia               4.3            0.2    WHO 2012[10]\n",
        "Denmark               0.9            0.2    WHO 2012[10]\n",
        "France                1.1            0.2    WHO 2012[10]\n",
        "Netherlands           1.1            0.2    WHO 2012[10]\n",
        "Sweden                1.0            0.2    WHO 2012[10]\n",
        "Slovakia              1.5            0.2    WHO 2012[10]\n",
        "Austria               0.6            0.2    WHO 2012[10]\n",
        "Latvia                3.1            0.2    WHO 2012[10]\n",
        "Spain                 0.8            0.1    WHO 2012[10]\n",
        "Hungary               1.3            0.1    WHO 2012[10]\n",
        "Czech Republic        1.7            0.1    WHO 2012[10]\n",
        "Germany               0.8            0.1    WHO 2012[10]\n",
        "Slovenia              0.7            0.1    WHO 2012[10]\n",
        "Romania               2.0            0.0    WHO 2012[10]\n",
        "United Kingdom        1.2            0.0    WHO2012 [10]\n",
        "Norway                0.6            0.0    WHO 2012[10]\n",
        "Poland                1.1            0.0    WHO 2012[10]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's just compare US, Canada, and UK:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "select = data.ix[['United States', 'Canada', 'United Kingdom']]\n",
      "plot_percapita(select)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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AAE1BvYYja2pqvNYKc7lcPMgbAADgL6hXCUtJSdE777wj6eDCre+8844uu+wy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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e768d90>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Normalize to the US numbers (inverse)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "select['Homicides'] = select['Homicides']['United States'] / select['Homicides']\n",
      "select['Gun Homicides'] = select['Gun Homicides']['United States'] / select['Gun Homicides']\n",
      "display_relevant(select)"
     ],
     "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>Homicides</th>\n",
        "      <th>Gun Homicides</th>\n",
        "      <th>Gun Data Source</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>United States</strong></td>\n",
        "      <td> 1.0</td>\n",
        "      <td>  1.0</td>\n",
        "      <td> OAS 2012[5][6]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>Canada</strong></td>\n",
        "      <td> 2.6</td>\n",
        "      <td>  4.9</td>\n",
        "      <td>  Krug 1998[13]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>United Kingdom</strong></td>\n",
        "      <td> 3.5</td>\n",
        "      <td> 92.5</td>\n",
        "      <td>   WHO2012 [10]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "display_data",
       "text": [
        "                Homicides  Gun Homicides Gun Data Source\n",
        "United States         1.0            1.0  OAS 2012[5][6]\n",
        "Canada                2.6            4.9   Krug 1998[13]\n",
        "United Kingdom        3.5           92.5    WHO2012 [10]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So, you are 2.6 times more likely to be killed in the US than Canada,\n",
      "and 3.5 times more likely than in the UK.\n",
      "That's bad, but not extreme.\n",
      "\n",
      "However, you are 4.9 times more likely to be killed *with a gun* in the US than Canada,\n",
      "and almost 100 times more likely than in the UK.  That is pretty extreme.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Countries represented:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for country in data.index:\n",
      "    print country"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "El Salvador\n",
        "Jamaica\n",
        "Honduras\n",
        "Guatemala\n",
        "Colombia\n",
        "Brazil\n",
        "Panama\n",
        "Mexico\n",
        "Paraguay\n",
        "Nicaragua\n",
        "United States\n",
        "Costa Rica\n",
        "Uruguay\n",
        "Argentina\n",
        "Barbados\n",
        "Montenegro\n",
        "Peru\n",
        "Moldova\n",
        "Israel\n",
        "India\n",
        "Canada\n",
        "Serbia\n",
        "Luxembourg\n",
        "Greece\n",
        "Uzbekistan\n",
        "Croatia\n",
        "Kyrgyzstan\n",
        "Switzerland\n",
        "Malta\n",
        "Portugal\n",
        "Belarus\n",
        "Ireland\n",
        "Italy\n",
        "Kuwait\n",
        "Ukraine\n",
        "Estonia\n",
        "Belgium\n",
        "Finland\n",
        "Lithuania\n",
        "Cyprus\n",
        "Bulgaria\n",
        "Georgia\n",
        "Denmark\n",
        "France\n",
        "Netherlands\n",
        "Sweden\n",
        "Slovakia\n",
        "Qatar\n",
        "Austria\n",
        "Latvia\n",
        "New Zealand\n",
        "Spain\n",
        "Hungary\n",
        "Czech Republic\n",
        "Hong Kong\n",
        "Australia\n",
        "Singapore\n",
        "Chile\n",
        "Germany\n",
        "Slovenia\n",
        "Romania\n",
        "Azerbaijan\n",
        "South Korea\n",
        "United Kingdom\n",
        "Norway\n",
        "Japan\n",
        "Poland\n",
        "Mauritius\n"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}