Gun data for Jon Stokes

gdp.csv

Rank	Country	GDP	GDP Year
1	Luxembourg	89992	2011
2	Qatar	88919	2011
2	Macau	77607	2011
3	Norway	61882	2011
4	Singapore	61103	2011
5	Kuwait	54654	2011
6	Brunei	50506	2010
6	Hong Kong	49990	2011
7	Switzerland	49151	2011
8	United States	48442	2011
9	United Arab Emirates	48222	2011
10	Netherlands	42834	2011
11	Austria	42225	2011
12	Ireland	41543	2011
13	Sweden	41300	2011
14	Denmark	40983	2011
15	Canada	40440	2011
16	Australia	39438	2011
17	Germany	39211	2011
18	Belgium	38605	2011
19	Finland	38083	2011
20	Equatorial Guinea	36515	2011
21	Iceland	36085	2011
22	United Kingdom	35494	2011
23	France	34993	2011
24	Japan	34294	2011
25	Italy	32928	2011
25	European Union	32642	2011
26	Spain	32424	2011
27	Bahamas	32198	2011
28	Cyprus	32087	2011
29	South Korea	30254	2011
30	New Zealand	29935	2010
31	Oman	28880	2011
32	Israel	27835	2011
33	Slovenia	27412	2011
34	Malta	27294	2011
35	Greece	26948	2011
36	Seychelles	26420	2011
37	Czech Republic	26046	2011
38	Trinidad and Tobago	25951	2011
39	Portugal	25385	2011
40	Saudi Arabia	24434	2011
41	Slovakia	24022	2011
42	Bahrain	23690	2010
43	Estonia	21942	2011
44	Hungary	21610	2011
45	Poland	21310	2011
46	Russia	21248	2011
47	Lithuania	20374	2011
48	Croatia	20031	2011
49	Barbados	19272	2009
50	Chile	18553	2011
51	Latvia	17802	2011
52	Argentina	17742	2011
53	Turkey	17499	2011
54	Libya	16855	2009
55	Antigua and Barbuda	16121	2011
56	Gabon	15960	2011
57	Panama	15695	2011
58	Malaysia	15589	2011
59	Mexico	15270	2011
60	Uruguay	15181	2011
61	Romania	15163	2011
62	Saint Kitts and Nevis	15154	2011
63	Belarus	15040	2011
64	Palau	15023	2011
65	Botswana	14753	2011
66	Lebanon	14709	2011
67	Bulgaria	14603	2011
68	Mauritius	14523	2011
69	Montenegro	13612	2011
70	Kazakhstan	13189	2011
71	Venezuela	12836	2011
72	Dominica	12678	2011
73	Costa Rica	12236	2011
74	Serbia	11919	2011
75	Brazil	11719	2011
76	Macedonia	11666	2011
76	World	11640	2011
77	Iran	11479	2009
78	Grenada	11180	2011
79	South Africa	11035	2011
80	Saint Vincent and the Grenadines	10812	2011
81	Peru	10318	2011
82	Azerbaijan	10136	2011
83	Colombia	10103	2011
84	Dominican Republic	9863	2011
85	Tunisia	9415	2011
86	Saint Lucia	9385	2011
87	Turkmenistan	9184	2011
88	Bosnia and Herzegovina	9089	2011
89	Albania	8944	2011
90	Maldives	8932	2011
91	Algeria	8715	2011
92	Thailand	8703	2011
93	Ecuador	8486	2011
94	China	8466	2011
95	Jamaica	8065	2011
96	Suriname	7891	2010
97	Ukraine	7251	2011
98	El Salvador	6877	2011
99	Namibia	6826	2011
100	Belize	6722	2011
101	Egypt	6324	2011
102	Swaziland	6099	2011
103	Jordan	6007	2011
104	Angola	5930	2011
105	Armenia	5829	2011
106	Bhutan	5810	2011
107	Sri Lanka	5620	2011
108	Georgia	5503	2011
109	Paraguay	5419	2011
110	Syria	5262	2010
111	Bolivia	5130	2011
112	Morocco	4986	2011
113	Guatemala	4961	2011
114	Fiji	4787	2011
115	Mongolia	4764	2011
116	Indonesia	4668	2011
117	Tonga	4666	2011
118	Vanuatu	4631	2011
119	Samoa	4570	2011
120	Congo	4429	2011
121	Philippines	4140	2011
122	Cape Verde	4123	2011
123	Honduras	4066	2011
124	Iraq	3890	2011
125	India	3652	2011
126	Guyana	3445	2010
127	Micronesia	3440	2011
128	Vietnam	3435	2011
129	Moldova	3392	2011
130	Uzbekistan	3310	2011
131	Solomon Islands	2943	2011
132	Nicaragua	2941	2011
133	Laos	2809	2011
134	Pakistan	2763	2011
135	Papua New Guinea	2695	2011
136	Mauritania	2571	2011
137	Nigeria	2532	2011
138	Kiribati	2531	2011
138	West Bank and Gaza	2465	2005
139	Kyrgyzstan	2424	2011
140	Cameroon	2383	2011
141	Cambodia	2372	2011
142	Yemen	2349	2011
143	Tajikistan	2340	2011
144	Djibouti	2290	2009
145	Sudan &  South Sudan	2141	2011
146	Gambia	2135	2011
147	São Tomé and Príncipe	2058	2011
148	Senegal	1981	2011
149	Ghana	1884	2011
150	Côte d'Ivoire	1803	2011
151	Bangladesh	1788	2011
152	Kenya	1718	2011
153	Lesotho	1715	2011
154	Benin	1628	2011
155	Zambia	1623	2011
156	East Timor	1588	2011
157	Chad	1531	2011
158	Tanzania	1521	2011
159	Uganda	1354	2011
160	Burkina Faso	1310	2011
161	Nepal	1256	2011
162	Guinea-Bissau	1251	2011
163	Rwanda	1251	2011
164	Afghanistan	1202	2010
165	Haiti	1179	2011
166	Guinea	1128	2011
167	Comoros	1117	2011
168	Ethiopia	1116	2011
169	Mali	1099	2011
170	Togo	1042	2011
171	Mozambique	982	2011
172	Madagascar	972	2011
173	Malawi	918	2011
174	Sierra Leone	877	2011
175	Central African Republic	816	2011
176	Niger	732	2011
177	Burundi	608	2011
178	Eritrea	589	2011
179	Liberia	577	2011
180	Democratic Republic of the Congo	375	2011

Gun Data.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_%28PPP%29_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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       "output_type": "display_data",
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SJUv0wgsvKCwsTJLUrl07zZo1S02aNLnqr127dq327NmjrKwsrVy5UpLUqVMn\npaSkKDo6WhUrVlSnTp00duxYeXl5qXbt2urXr98NnBIAAMDNr0QlzGazOQqYJIWFhTmuXF1Njx49\n1KNHjyu+JyoqSlFRUSX6fAAAAJ6gRMOReXl5RdYKs9lsPMgbAADgBpSohEVFRenjjz+WlL9w68cf\nf6y77rrLqcEAAAA8WYmGI5ctW6bjx49r1apVkqTs7GyFhYVp6dKlkiSLxaI333zTeSkBAAA8TIlK\n2KxZs5ydAwAA4JZS8gdAAgAAoNRQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIAB\nlDAAAAADKGEAAAAGUMIAAAAMoIQBAAAYQAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAAAyhh\nAAAABlDCAAAADKCEAQAAGEAJAwAAMIASBgAAYIC36QBAcaz798uamuqSY9lq1JCtTh2XHAsAgAKU\nMNyUrKmpCurVyyXHykxMpIQBAFyO4UgAAAADKGEAAAAGUMIAAAAMoIQBAAAY4JKJ+bm5ufroo4+0\nc+dOvfzyy5fsHzdunOx2u6zW/E4YGxur8PBwV0QDAAAwwiUlbMGCBWratKl27txZ7H6LxaLRo0fL\nz8/PFXEAAACMc0kJi42NlSR98sknxe738vLSlClTlJWVpZYtW2rAgAGuiAWgFLG2GwBcm5tinbBR\no0bJx8dHNptNs2fP1ubNm9WmTRvTsQBcA9Z2A4Brc1NMzPfx8ZEkWa1WdejQQfv37zcbCAAAwMmM\nl7BTp05p0aJFkiSbzaaNGzeqQYMGhlMBAAA4l7HhyISEBEVHRys0NFR5eXkaM2aMvL29FRkZqYiI\nCFOxAAAAXMKlJazw8hQDBw50fBwTE6OYmBhXRgEAADDK+HAkAADArYgSBgAAYAAlDAAAwABKGAAA\ngAGUMAAAAAMoYQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIABlDAAAAAD\nKGEAAAAGUMIAAAAMoIQBAAAYQAkDAAAwwNt0AAAAcGuz7t8va2qqS45lq1FDtjp1XHKsq6GEAQAA\no6ypqQrq1cslx8pMTLxpShjDkQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgA\nAIABlDAAAAADKGEAAAAGUMIAAAAMoIQBAAAY4PRnR+bm5uqjjz7Szp079fLLL1+yPykpSatWrZLV\nalXdunUafQahAAAgAElEQVQ1dOhQZ0cCAAAwzulXwhYsWKCmTZsWuy89PV1r1qxRfHy8JkyYoKCg\nIK1du9bZkQAAAIxz+pWw2NhYSdInn3xyyb6tW7cqKipKFotFktS1a1e9++676ty5s7NjAcAtz7p/\nv6ypqS45lq1GDdnq1HHJsQB34fQSdiVZWVmqVauW43VwcLAyMjIMJgKAW4c1NVVBvXq55FiZiYmU\nMOAiRifmBwUFFSldGRkZCg4ONpgIAADANYyWsBYtWigpKUk2m02StHr1arVu3dpkJAAAAJcwMhyZ\nkJCg6OhoVaxYUZ06ddLYsWPl5eWl2rVrq1+/fiYiAQAAuJTLSljh5SkGDhzo+DgqKkpRUVGuiuFx\nTofU1Ol5y11zsJAaKuuaIwEA4PGMTszHjfvx9G3qFdvCJcdKTMzUHcp1ybEAAPB0rJgPAABgAFfC\nAAAexZXrn0msgYbrRwkDAHgUV65/JrEGGq4fw5EAAAAGUMIAAAAMoIQBAAAYQAkDAAAwgBIGAABg\nACUMAADAAEoYAACAAZQwAAAAAyhhAAAABrBiPm5Kp0Nq6vS85a45WEgNlXXNkQAAcKCE4ab04+nb\n1Cu2hUuOlZiYqTuU65JjAQBQgOFIAAAAAyhhAAAABjAcCQBXYN2/X9bUVJcdz1ajhmx16rjseADM\noYQBwBVYU1MV1KuXy46XmZhICQNuEQxHAgAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAA\nAANYogJwMZ6LCQCQKGGAy3nqczEplwBwbShhAEqFp5ZLAHAW5oQBAAAYQAkDAAAwwCXDkYmJidq0\naZMkqVWrVurbt2+R/ePGjZPdbpfVmt8JY2NjFR4e7opoAAAARji9hO3cuVP79u1TfHy8JOmtt97S\n9u3b1axZM8d7LBaLRo8eLT8/P2fHAYBr4tIbDiRuOgBuIU4vYT/++KO6dOnieN2lSxdt2LChSAnz\n8vLSlClTlJWVpZYtW2rAgAHOjgUAJeLKGw4kbjoAbiVOL2GZmZkKCgpyvA4ODtbp06eLvGfUqFHy\n8fGRzWbT7NmztXnzZrVp08bZ0QAAAIxxegkLCgpSRkaG43VGRoaCg4OLvMfHx0eSZLVa1aFDB+3b\nt48SBgBOxtpugFlOL2GtWrXSqlWrHMOPa9euVceOHR37T506pS+//FL9+/eXzWbTxo0bi+wHADgH\na7sBZjm9hDVq1Ei7d+/Wiy++KCm/lDVr1kwJCQmKjo5WaGio8vLyNGbMGHl7eysyMlIRERHOjgUA\nAGCUS5ao6N27t3r37l1k28CBAx0fx8TEKCYmxhVRAAAAbgos1goAAGAAJQwAAMAAShgAAIABlDAA\nAAADKGEAAAAGuOTuSAAAXMWTn/fpqQvseup5XQ0lDADgUTz5eZ+eusCup57X1TAcCQAAYAAlDAAA\nwABKGAAAgAGUMAAAAAMoYQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIAB\nlDAAAAADKGEAAAAGUMIAAAAMoIQBAAAYQAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAAAyhh\nAAAABlDCAAAADKCEAQAAGEAJAwAAMMDbFQdJTEzUpk2bJEmtWrVS3759i+xPSkrSqlWrZLVaVbdu\nXQ0dOtQVsQAAAIxx+pWwnTt3at++fYqPj1d8fLzS0tK0fft2x/709HStWbNG8fHxmjBhgoKCgrR2\n7VpnxwIAADDKYrfb7c48wEcffaSIiAg1bdpUkvTrr79qw4YNevDBByVJX375pby9vdW5c2dJ0qlT\np/Tuu+/queeeK/bzrVmzxplxAQAASlWXLl2K3e704cjMzEwFBQU5XgcHB+v06dOO11lZWapVq1aR\n/RkZGZf9fJc7EQAAAHfi9OHIoKCgIqUqIyNDwcHBJd4PAADgiZxewlq1alVkjtfatWvVunVrx+sW\nLVooKSlJNptNkrR69eoi+wEAADyR0+eESdJnn31W5O7I++67TwkJCYqOjlZwcLDWrVunVatWycvL\nS7Vr19bDDz/s7EgAAABGuaSEAQAAoCgWawUAADDAJYu1ujObzSarla4K1zt69KgsFovsdrvj/35+\nfty4ApSyU6dOKTQ01HSMUpebmytv7//7Np+TkyM/Pz+DiW5McnLyFfe3atXKRUlKDyWsGMuWLVNk\nZKQOHz6shQsXqmXLlho4cKDpWNdt9OjRV9z/8ssvuyiJcx0/flzZ2dmSpAsXLqhu3bqGE92Y9957\nTwcOHFCTJk20Y8cO1ahRQzabTRkZGXr88ccVHh5uOmKp8JQfdC7+d3bs2DGFhITo9OnTqlChgkf8\nO1u/fr0+++wzZWVlycvLS+XLl9eECRNMx7ph06ZNU3BwsLp27aoWLVqYjlNqJk2apLi4OMfrmTNn\n6n/+538MJroxe/fudXx8/Phx/frrr2rbtq02b96s+vXrU8I8xZYtW9SnTx999dVXeu211/Tiiy+a\njnRDPOGL/9XMnTtX+/fv1+HDh1WlShWFhobq2WefNR3rhpQvX17Dhg1TuXLldPLkSc2bN09PPfWU\nUlNT9fbbbys+Pt50xBty8OBBLViwQOnp6Ro2bJjOnDnj1t8AC/87W7hwoRo2bKiWLVtq69at2rFj\nh8FkpWf58uWKj4/X1KlT9eyzz+qTTz4xHalUTJgwQYcPH9a6deu0dOlSNWvWTJ06dVJYWJjpaNfl\np59+0rZt25SWlqZ58+ZJyr8qdvToUcPJbsz999/v+Hjq1KmaMGGCAgMDFR0drdmzZxtMdv3c/8dP\nJ7DZbDp06JB8fX1lt9vl5eVlOlKpSk5O1ooVK5Sdna3z58+bjlMqdu/erbi4ONWqVUvjxo0rcgne\nXR0+fFjlypWTJJUrV86xyHGNGjVMxio1CQkJGj58uIKDg1W3bl19+umnpiOVmp07d6ply5aS8pfh\n+e233wwnKh3BwcHy9/eXt7e3AgMDdeTIEdORSk21atUUFRWlBg0aKCkpSXPmzNG0adP0xx9/mI52\nzapWrapWrVopKChIrVu3VqtWrdS+ffsiV8XcXVZWlgIDAyVJgYGBOnv2rOFE18f9v1M5Qe/evbVw\n4UINHjxYv/32mzp27Gg6UqlZsGCBypQpo02bNqlZs2b64osv9Pjjj5uOdcPKli0rSfL29lZeXp5O\nnDhhOFHpOH36tEJCQpSRkaHc3FxJ0vnz5x0fu7MLFy4oICBAUv6fm4+Pj+FEpefim84L1kF0dxUq\nVNDRo0d12223aeHChY7hf3e3evVqJSUlKSAgQF27dlVMTIysVquOHTumhIQEPf3006YjXpOKFSuq\nYsWKGjx4sBo3bmw6jlNYrVZHETtz5ozbTmmghBUjMjJSbdq00YkTJ9SgQQM1aNDAdKRSs3fvXr34\n4ovatm2bateurUOHDpmOVCqaNWum9PR0tW/fXpMmTXKUMnf217/+VZMnT1a9evW0Z88eDR06VFL+\nUNef//xnw+lunJ+fnw4fPixJ+uOPPxyFzBNEREToww8/1J/+9Cdt2LDB8excdzd06FD5+vqqT58+\n+u677zzmMXInT57UU089dcnwY4UKFdSmTRtDqa7fDz/8oHbt2unHH3/Ujz/+6NhusVg0aNAgg8lK\nT8HXx/DwcP3++++KjY01Hem6sE5YMXbs2KGlS5fq5MmTeuSRR/THH3/orrvuMh2rVLz00ksaP368\nJkyYoLi4OI0fP96jLlFL+XcVli9f3u2HkdeuXasOHTroyJEjqlq1qsqUKSPJcyayHz16VP/+97/1\n+++/q379+hoyZIgqVKhgOlapWb9+vX777TfVq1dPd955p+k4pWLOnDlFFtP+8MMPNXjwYIOJrl9K\nSkqR1xaLRZIcdyO787D/r7/+qgYNGuiXX34pst1isXjUlbFz587p8OHDqlatmvz9/U3HuS5cCSvG\nkiVL9Pzzz2vSpElq2LCh5s2b5zElrH379nrvvfeUmZmpf//732rXrp3pSNftP//5T5HXhZdzkKRu\n3bqZiFVq/vvf/6pz586X3AXpCQVMkvz9/TVy5Eh5e3srNzfXY4a2Ctxxxx264447TMcoFUeOHNGR\nI0e0c+dOxzIBubm52rp1q9uWsMTERMfXiuI88cQTLkxTugpGb5o0aWI4ifPY7XYdOHBAZ86c0alT\npySxRIXHyMvLc0zstlgsbtuwi9OjRw/t2LFDVapUUf369dWoUSPTka5b4T+XlJQU7du3zzH8U7t2\nbYPJSkfNmjU1ZcoURURESMr/u+juxbKwd955R4MHD1bFihV1/PhxJSQkaMSIEaZjlYr169dr6dKl\n8vb2dpRmd75L+eTJk9q7d6+ys7MdywRYLBY9+uijhpNdvyeffNJ0BKfz1CVFpPzlNvLy8lSzZk1H\nmaaEeYjQ0FDt2rVLkvTbb7953CJ+lStXVlBQkCRp3759brue1t133+34eNKkSRo1apT8/PwUFRWl\nqVOnmgtWSurVq2c6glNlZWWpYsWKkvL/TmZmZhpOVHqWLl2quLg4j1lYt3HjxmrcuLHKlCmje++9\n13ScUnfixAl9/fXXys7Olt1uV25urmMOpjvz1CVFpPzpDJMmTTId44ZRworx8MMPa968eTp79qy+\n/vprj/jHWMAT19OS8u+0K1gJ2tfX1yPuHixcMj2R3W7XmTNnVLZsWZ05c8Z0nFJVvnx5jylghVcp\nr1at2iWrlrvj1YeLzZgxQ507d9bKlSvVrVs3t1yWojievKRI+fLldf78efn6+pqOckMoYcXYvXt3\nkWUbkpOTPeILjZR/bpMnT9akSZM0ZswYvfnmm6YjlQpfX18dPXpUFStW1LFjx9z+H6ZU/JMO3HlI\n62L9+vXTxIkTddttt2nv3r1u/VSKizVs2FCLFy9WZGSkLBaLW0/0LrxKeXE84Wujt7e37r77bn37\n7bfq3LmzXn/9ddORSoWnLiki5X/NHz16tCIiImS1Wt32zk9KWDFWrFih1q1bO14vW7bMI77QSJ67\nnlZsbKxmzJihwMBAZWVl6bHHHjMd6YYVFK68vDwlJyd7zHIiBZo2baqxY8cqLS1NAwcOdNz96QnS\n0tJktVq1YsUKxzZ3nehdeJVyT+Xt7a2cnBwFBwfryJEjbr+yfAFPXVJEkjp16uT4AcedsURFIatX\nr9aaNWsct7xK+csBNGrUyGOGJBMTE9W+fXvt3LlT33zzjcqWLat//OMfpmOVmsKrKHua+Ph4jR07\n1nSMUpObm6vVq1crPT1dvXr1ktVq9ZghPE914MABrVy5ssjcKXd+FmGBffv2KSwsTBkZGZozZ47u\nvPNOde7c2XSsG/bWW2+pQ4cOatasmdsv2VOc48ePO1bKv3Dhgls+T5crYYV07dpVXbt21cSJE93+\neZGX07RpU1WqVEmVKlXS7bff7rbPRruYJ06sLVjHyG636/Dhw8rKyjKcqHS9//77aty4sb777jud\nO3dOCQkJGjlypOlYpWL79u1atmyZ4xuEv7+/R6zH98477+iBBx7QwoUL1b9/f495HNPx48dVt25d\nBQcHKy4u7pJ5b+7qz3/+s5KTk7V8+XKVL19ekZGRatu2relYpcJT5jd7xoJDpezFF1+UzWZTTk6O\ncnJyPOqb39y5cx0fV6pUyWN+OpoxY4bCwsK0fft2Va9e3SOWFUlMTNTy5cu1YsUK7d69W8OHDzcd\nqVT98ccf6tixo7y8vFSlShXHWj+eICEhQY899pgCAgL09NNPu/VSMIX5+/urSZMmKlOmjJo1a3bV\n+WLu4vPPPy/yetmyZYaSlK569eqpf//+jtXkFy1aZDhR6fGU5wW7Z2onW758ub7++mudPHlSgYGB\nqly5ssdcGWvUqJGWL1+uFi1aSJJbTxguzBMn1hasY+SpQ6yFn4Fps9nc9otocQIDA1WpUiV5e3ur\nSpUq2r9/v+lIpcLf31+ZmZkKCwvTTz/9pOPHj5uOdEMKT0EpuBGmYAqKJ1i0aJEOHz6sWrVqqW/f\nvo5pNp7AU+Y3e85XvVK0fv16vfbaa5o8ebJGjRql9957z3SkUnPixAmdPHmyyCM73HXCcGGeOLH2\n0KFDmj17tgICAnT27Fk9/vjjHlGYC9xzzz2aPn26Tpw4oRkzZnjUQrRBQUE6ceKEqlWrptWrV3vM\nVb7BgwfLx8dH999/v2NI0p15+hSUM2fOyG63KyAgwKOezSp5zvOCmZhfjMmTJ2vMmDF6+eWX9fzz\nz2vixIkeNSG6wNGjR1WhQgW3v7tEkn7//XdVqFDBoybWvvzyyxo6dKiqVKmi9PR0zZkzp9hlK9xZ\nWlqaDhw4oLp166pSpUqm45SajIwMBQYGKjs7W1988YVat27tlpOG4f7sdrt27NihtWvX6vjx4xo3\nbpzpSKXOnZ8XzJWwYtSsWVNHjx5Vs2bNNGvWLNNxStUHH3ygrl27as+ePfruu+8UEhLiEY/vKPgG\nVzCx1hOcP39eVapUkZQ/f+/ChQuGE5W+w4cPO/6tecLCiwUK7vIsW7asRyzxMG3aNMezWQuzWCxu\nORm6wJV+qPGENfkOHTqk5ORkbd++XT4+Pm7/DOQr3TCRkpLilktJUcKKUTCJ8d5779Uvv/zito/1\nKc7vv/+uGjVqaO3atRozZoxeeukl05FKxcGDB7Vy5UrHyuvu/s1Byp+bUlBMzp8/f8k3QHe3YMEC\nlSlTRps2bVKzZs20cuVKDRs2zHSs63a1q5Tu/E39xIkTKlOmjKKjo1W1alVJ8oi/j+78Z1ISCxcu\nVLt27fTMM894xHCkJy4cTAkrxuuvv+64Vb5Jkyb65z//6TF3ptlsNm3dulVlypTRhQsXPGIoUpJm\nz56tfv36qVatWpLkEed177336uWXX1bz5s21bds2de/e3XSkUrV37169+OKL2rZtm2rXrq3U1FTT\nkW5I4W/oJ06cUPny5Q2mKV0TJ07Uzp07tWLFCnl7e6t3794Mr7qBglEOi8WinJwc2Ww2t14U2ROu\nKl+MElbInj17tGfPHh08eFCrVq1yrDf1+++/m45WagYPHqwffvhBffr0UUpKinr27Gk6UqkICAhQ\nmzZtTMcoVW3btlXt2rW1b98+/elPf/KoOVOSLrm65+PjYzBN6ZoxY4YmTJhgOkapuv3223X77bcr\nNTVVy5cv16lTp9SzZ09FRESYjobLePXVVyXlP3XjwIEDqlWrluLj4w2nKh3Hjx9XQkKCjh8/rqFD\nhyovL0+33Xab6VjXzGucJ87Su04ZGRnKysrSr7/+qiZNmsjb21v+/v7q1auXgoKCTMcrFWFhYWre\nvLn8/PxUrlw5Va9e3XSkUnHkyBFZrVZVrFjRdJRStXXrVu3atUuSHFf5PEV2drb++9//6vDhw0pN\nTdXtt9+uevXqmY5VKo4eParMzExVqFBBubm5ysvL85glOIKDg+Xr66tt27YpIyPD43748SR33323\n7r77bnXq1ElRUVFKSUnxmD+v2bNnKzY2Vps3b1a3bt301ltv6e677zYd65p5xleFUlKnTh3VqVNH\n3t7euuOOO0zHKVWePgF127ZtWr9+vcqWLSurNX8NYnc/r8WLFysrK0sdO3bUhg0bdOTIEY+6HN+o\nUSPVqVNHVapUUf369T1mbSYpfyHJnTt3atWqVY5t7n7DiM1m07fffqvVq1erQYMGGjFihMqVK2c6\nFkooLCxMhw8fNh2j1GRnZ6tChQqS5NZDrJSwQnbu3KlKlSrpjjvuUG5urt59911lZ2dryJAhbj+/\nw90LydV44vn99NNPjiGtBg0aKC4uzqNK2Ny5czV+/Hg1btzYdJRS52lL2qxYsUIbNmxQZGSkRo0a\n5RGTvG8F//nPfxwfHzp0yGPuPpby57mdPn1aUv6C1u56bpSwQj7++GONGDFCUv5f3nbt2ikkJERz\n5szRc889ZzgdrsQTnx1ZcEXvcq/dnac+vUGSzp49q08//VTHjh1TTEyMJDmWG3FH8+bNU82aNfX9\n99/r+++/L7LPE38A8hSFH98WERHhuPPfEwwaNEjTpk3ToUOHNH36dA0ePNh0pOtCCSvEYrE41vdJ\nS0vTvffeK0mOh/Di5jVjxgx17txZK1euVLdu3fTHH3+YjnTDmjZtqk2bNqlt27bavHmzbr/9dtOR\nStXJkyc98ukNkjRnzhx169ZNCxYskL+/v2bPnq0xY8aYjnXdPv74Y9MRcA327NmjevXqXTJHKikp\nSXfeeaeZUKUsOTlZ48ePV2ZmplvP2aaEFZKXl6ezZ8/q+PHjCg0NlZS/Fk52drbhZLgaT3l25MVz\n97755hstWrRI2dnZCgoKcvvHxEj5P9QEBASoTp06jnXdJCkqKspgqtJ16tQpNWjQQJIUGhqqnJwc\nw4lwK/nggw/09NNPF7lRaceOHfr66689poT9/PPPHnHTHCWskPvuu09xcXEKDg7W008/LUn6/vvv\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": {}
  }
 ]
}

guns.csv

Country	Total Gun Deaths	Gun Homicides	Gun Suicides	Unintentional	Undetermined	Year	Gun Data Source
El Salvador	50.36	50.36	NA	NA	NA	2009	OAS 2011[1]
Jamaica	47.44	47.44	NA	NA	NA	2009	OAS 2011[1]
Honduras	46.70	46.70	NA	NA	NA	2007	OAS 2011[1]
Guatemala	38.5	38.5	NA	NA	NA	2009	OAS 2011[1]
Swaziland	37.16	37.16	NA	NA	NA	2004	UNODC 2006[1]
Colombia	28.11	27.10	0.87	0.14	NA	2009	UNODC 2011 [2]
Brazil	19.01	18.10	0.73	0.18	NA	2008	UNODC 2011[3]
Panama	12.92	12.92	NA	NA	NA	2010	OAS 2011[1]
Mexico	11.14	10.00	0.67	0.47	NA	2010	UNODC 2011[4]
United States	10.2	3.7	6.1	0.2	0.1	2009	OAS 2012[5][6]
Philippines	9.46	9.46	NA	NA	NA	2002	UNODC 2002[7]
South Africa	9.41	NA	NA	NA	NA	2012	UNODC 2012[8] & Stats SA[9]
Montenegro	8.55	2.06	6.49	NA	NA	2009	WHO 2012[10]
Paraguay	7.35	7.35	NA	NA	NA	2000	UNODC 2000[11]
Nicaragua	7.14	7.14	NA	NA	NA	2007	OAS 2011[1]
Argentina	5.65	3.00	2.01	0.64	NA	2001 2008	UNODC 2011[12]
Canada	4.78	0.76	3.72	0.22	NA	1992	Krug 1998[13]
Zimbabwe	4.75	4.75	NA	NA	NA	2000	UNODC 2000[11]
Serbia	3.90	0.62	2.81	0.18	0.29	2010	WHO 2012[10]
Finland	3.64	0.26	3.34	0.02	0.02	2010	WHO 2012[10]
Switzerland	3.5	0.52	3.15	0.10	0.07	2010	OAS 2011[1]
Costa Rica	3.32	3.32	NA	NA	NA	2002	UNODC 2002[7]
Uruguay	3.24	3.24	NA	NA	NA	2002	UNODC 2002[7]
Croatia	3.01	0.57	2.35	0.07	0.02	2010	WHO 2012[10]
Barbados	3	3	NA	NA	NA	2000	UNODC 2000[11]
France	3.00	0.22	2.33	0.05	0.41	2009	WHO 2012[10]
Austria	2.94	0.18	2.68	NA	0.08	2010	WHO 2012[10]
New Zealand	2.66	0.17	2.14	0.09	NA	1993	Krug 1998[13]
Estonia	2.54	0.30	1.57	0.07	0.60	2010	WHO 2012[10]
Slovenia	2.44	0.05	2.34	NA	0.05	2010	WHO 2012[10]
Belgium	2.43	0.29	1.96	0.01	0.16	2006	WHO 2012[10]
Malta	2.16	0.48	1.68	NA	NA	2010	WHO 2012[10]
Peru	1.87	1.87	NA	NA	NA	2009	OAS 2011[1]
Israel	1.86	0.94	0.71	0.03	0.19	2009	WHO 2012[10]
Republic of Macedonia	1.04	0.45	0.42	0.08	0.08	2011	WHO 2012[10]
Luxembourg	1.81	0.60	1.00	NA	0.20	2009	WHO 2012[10]
Norway	1.78	0.04	1.72	0.02	NA	2010	WHO 2012[10]
Portugal	1.77	0.48	1.09	0.02	0.18	2010	WHO 2012[10]
Czech Republic	1.76	0.12	1.39	0.10	0.15	2010	WHO 2012[10]
Slovakia	1.75	0.18	0.94	0.39	0.24	2010	WHO 2012[10]
Lithuania	1.61	0.24	1.00	0.03	0.33	2010	WHO 2012[10]
Georgia	1.54	0.23	0.09	1.00	0.23	2009	WHO 2012[10]
Greece	1.5	0.59	0.84	0.04	NA	1994	Krug 1998[13]
Sweden	1.47	0.19	1.20	0.06	0.01	2010	WHO 2012[10]
Denmark	1.45	0.22	1.16	0.04	0.04	2006	WHO 2012[10]
Latvia	1.43	0.18	0.94	0.04	0.27	2010	WHO 2012[10]
Bulgaria	1.35	0.23	0.87	0.14	0.11	2011	WHO 2012[10]
Italy	1.28	0.36	0.81	0.08	0.03	2009	WHO 2012[10]
Kuwait	1.25	0.36	0.06	0	NA	1995	Krug 1998[13]
Iceland	1.25	NA	1.25	NA	NA	2009	WHO 2012[10]
Germany	1.10	0.06	0.94	0.02	0.08	2010	WHO 2012[10]
Moldova	1.04	1.04	NA	NA	NA	2011	WHO 2012[10]
Australia	1.05	0.09	0.79	0.02	0.15	2008	UNODC 2011[14]
Ireland	1.03	0.36	0.56	0.04	0.07	2010	WHO 2012[10]
Kyrgyzstan	1.01	0.53	0.07	0.28	0.13	2010	WHO 2012[10]
India	0.93	0.93	NA	NA	NA	2000	UNODC 2000[11]
Hungary	0.85	0.13	0.72	NA	NA	2009	WHO 2012[10]
Cyprus	0.83	0.24	0.48	NA	0.11	2010	WHO 2012[10]
Uzbekistan	0.68	0.58	0.03	NA	0.08	2005	WHO 2012[10]
Spain	0.63	0.15	0.42	0.05	0.01	2010	WHO 2012[10]
Netherlands	0.46	0.20	0.24	0.01	0.01	2010	WHO 2012[10]
Taiwan	0.42	0.13	0.12	0.11	NA	1994	Krug 1998[13]
Belarus	0.38	0.38	NA	NA	NA	2002	UNODC 2002[7]
Ukraine	0.35	0.35	NA	NA	NA	2000	UNODC 2000[11]
United Kingdom	0.25	0.04	0.17	0.01	0.02	2011	WHO2012 [10]
Poland	0.26	0.02	0.12	0.02	0.09	2010	WHO 2012[10]
Singapore	0.24	0.07	0.17	0	NA	1994	Krug 1998[13]
Romania	0.20	0.04	0.06	0.09	0.01	2010	WHO 2012[10]
Hong Kong	0.19	0.12	0.07	0	NA	1993	Krug 1998[13]
Mauritius	0.19	0	0.09	0.09	NA	1993	Krug 1998[13]
Qatar	0.18	0.18	NA	NA	NA	2000	UNODC 2000[11]
South Korea	0.13	0.04	0.02	0.05	NA	1994	Krug 1998[13]
Japan	0.07	0.02	0.04	0	NA	1994	Krug 1998[13]
Azerbaijan	0.07	0.04	0.01	0.02	NA	2007	WHO 2012[10]
Chile	0.06	0.06	NA	NA	NA	2002	UNODC 2002[7]
 

totals.csv

Country	Homicides	Total Homicides	Region	Subregion
Honduras	91.6	7,104	Americas	Central America
El Salvador	69.2	4,308	Americas	Central America
Côte d'Ivoire	56.9	10,801	Africa	Western Africa
Jamaica	52.2	1,430	Americas	Caribbean
Venezuela	45.1	13,080	Americas	South America
Belize	41.4	129	Americas	Central America
U.S. Virgin Islands	39.2	43	Americas	Caribbean
Guatemala	38.5	5,681	Americas	Central America
Saint Kitts and Nevis	38.2	20	Americas	Caribbean
Zambia	38.0	4,710	Africa	Eastern Africa
Uganda	36.3	11,373	Africa	Eastern Africa
Malawi	36.0	5,039	Africa	Eastern Africa
Lesotho	35.2	764	Africa	Southern Africa
Trinidad and Tobago	35.2	472	Americas	Caribbean
Colombia	33.4	15,459+	Americas	South America
South Africa	31.8	15,940	Africa	Southern Africa
Congo	30.8	1,180	Africa	Middle Africa
Central African Republic	29.3	1,240	Africa	Middle Africa
Bahamas	27.4	94	Americas	Caribbean
Puerto Rico	26.2	983	Americas	Caribbean
Saint Lucia	25.2	44	Americas	Caribbean
Dominican Republic	25.0	2,513	Americas	Caribbean
Tanzania	24.5	10,357	Africa	Eastern Africa
Sudan	24.2	10,028++	Africa	Northern Africa
Saint Vincent and the Grenadines	22.9	25	Americas	Caribbean
Ethiopia	22.5	20,239	Africa	Eastern Africa
Guinea	22.5	2,152	Africa	Western Africa
Dominica	22.1	15	Americas	Caribbean
Burundi	21.7	1,726	Africa	Eastern Africa
Democratic Republic of the Congo	21.7	13,558	Africa	Middle Africa
Panama	21.6	759	Americas	Central America
Brazil	21.0	40,974	Americas	South America
Equatorial Guinea	20.7	137	Africa	Middle Africa
Guinea-Bissau	20.2	294	Africa	Western Africa
Kenya	20.1	7,733	Africa	Eastern Africa
Kyrgyzstan	20.1	1,072	Asia	Central Asia
Cameroon	19.7	3,700	Africa	Middle Africa
Montserrat	19.7	1	Americas	Caribbean
Greenland	19.2	11	Europe	Northern Europe
Angola	19.0	3,426	Africa	Middle Africa
Guyana	18.6	140	Americas	South America
Burkina Faso	18.0	2,876	Africa	Western Africa
Eritrea	17.8	879	Africa	Eastern Africa
Namibia	17.2	352	Africa	Southern Africa
Rwanda	17.1	1,708	Africa	Eastern Africa
Mexico	16.9	25,757+	Americas	Central America
Chad	15.8	1,686	Africa	Middle Africa
Ghana	15.7	3,646	Africa	Western Africa
Ecuador	15.2	2,638	Americas	South America
North Korea	15.2	3,658	Asia	Eastern Asia
Benin	15.1	1,262	Africa	Western Africa
Sierra Leone	14.9	837	Africa	Western Africa
Mauritania	14.7	485	Africa	Western Africa
Botswana	14.5	287	Africa	Southern Africa
Zimbabwe	14.3	1,775	Africa	Eastern Africa
Gabon	13.8	200	Africa	Middle Africa
Nicaragua	13.6	785	Americas	Central America
French Guiana	13.3	30	Americas	South America
Papua New Guinea	13.0	854	Oceania	Melanesia
# Swaziland	12.9	141	Africa	Southern Africa
Bermuda	12.3	8	Americas	Northern America
Comoros	12.2	85	Africa	Eastern Africa
Nigeria	12.2	18,422	Africa	Western Africa
Cape Verde	11.6	56	Africa	Western Africa
Grenada	11.5	12	Americas	Caribbean
Paraguay	11.5	741	Americas	South America
Barbados	11.3	31	Americas	Caribbean
Togo	10.9	627	Africa	Western Africa
Gambia	10.8	106	Africa	Western Africa
Peru	10.3	2,969	Americas	South America
Myanmar	10.2	4,800	Asia	South-Eastern Asia
Russia	10.2	14,574	Europe	Eastern Europe
Liberia	10.1	371	Africa	Western Africa
Costa Rica	10.0	474	Americas	Central America
Nauru	9.8	1	Oceania	Micronesia
Bolivia	8.9	884	Americas	South America
Mozambique	8.8	1,925	Africa	Eastern Africa
Kazakhstan	8.8	1,418	Asia	Central Asia
Senegal	8.7	1,027	Africa	Western Africa
Turks and Caicos Islands	8.7	3	Americas	Caribbean
Mongolia	8.7	239	Asia	Eastern Asia
British Virgin Islands	8.6	2	Americas	Caribbean
Cayman Islands	8.4	5	Americas	Caribbean
Seychelles	8.3	7	Africa	Eastern Africa
Madagascar	8.1	1,588	Africa	Eastern Africa
Indonesia	8.1	18,963	Asia	South-Eastern Asia
Mali	8.0	1,157	Africa	Western Africa
Pakistan	7.8	13,860+	Asia	Southern Asia
Moldova	7.5	267	Europe	Eastern Europe
Kiribati	7.3	7	Oceania	Micronesia
Guadeloupe	7.0	32	Americas	Caribbean
Haiti	6.9	689	Americas	Caribbean
Timor-Leste	6.9	75	Asia	South-Eastern Asia
Anguilla	6.8	1	Americas	Caribbean
Antigua and Barbuda	6.8	6	Americas	Caribbean
Lithuania	6.6	219	Europe	Northern Europe
Uruguay	5.9	199	Americas	South America
# Philippines	5.4	4,947	Asia	South-Eastern Asia
Ukraine	5.2	2,356	Europe	Eastern Europe
Estonia	5.2	70	Europe	Northern Europe
Cuba	5.0	563	Americas	Caribbean
Belarus	4.9	473	Europe	Eastern Europe
Thailand	4.8	3,307	Asia	South-Eastern Asia
Suriname	4.6	24	Americas	South America
Laos	4.6	279	Asia	South-Eastern Asia
Georgia	4.3	187	Europe	Eastern Europe
Martinique	4.2	17	Americas	Caribbean
United States	4.2	12,996	Americas	Northern America
Turkmenistan	4.2	203	Asia	Central Asia
Yemen	4.2	990+	Asia	Western Asia
Albania	4.0	127	Europe	Southern Europe
Niger	3.8	552	Africa	Western Africa
Solomon Islands	3.7	19	Oceania	Melanesia
Sri Lanka	3.6	745	Asia	Southern Asia
Montenegro	3.5	22	Europe	Southern Europe
Djibouti	3.4	29	Africa	Eastern Africa
Argentina	3.4	1,360	Americas	South America
Cambodia	3.4	448	Asia	South-Eastern Asia
India	3.4	40,752+	Asia	Southern Asia
Turkey	3.3	2,320	Asia	Western Asia
Chile	3.2	541	Americas	South America
Taiwan	3.2	743	Asia	Eastern Asia
Uzbekistan	3.1	831	Asia	Central Asia
Latvia	3.1	70	Europe	Northern Europe
Iran	3.0	2,215	Asia	Southern Asia
Libya	2.9	176+	Africa	Northern Africa
Nepal	2.8	818	Asia	Southern Asia
Liechtenstein	2.8	1	Europe	Western Europe
Fiji	2.8	23	Oceania	Melanesia
Bangladesh	2.7	3,988	Asia	Southern Asia
South Korea	2.6	1,251	Asia	Eastern Asia
Mauritius	2.5	33	Africa	Eastern Africa
Luxembourg	2.5	12	Europe	Western Europe
Afghanistan	2.4	712+	Asia	Southern Asia
Malaysia	2.3	604	Asia	South-Eastern Asia
Syria	2.3	463+	Asia	Western Asia
Azerbaijan	2.2	206	Asia	Western Asia
Kuwait	2.2	59	Asia	Western Asia
Lebanon	2.2	95	Asia	Western Asia
Finland	2.2	118	Europe	Northern Europe
Tajikistan	2.1	143	Asia	Central Asia
Israel	2.1	159+	Asia	Western Asia
Iraq	2.0	608+	Asia	Western Asia
Bulgaria	2.0	147	Europe	Eastern Europe
Romania	2.0	421	Europe	Eastern Europe
São Tomé and Príncipe	1.9	3	Africa	Middle Africa
Macedonia	1.9	40	Europe	Southern Europe
Jordan	1.8	100	Asia	Western Asia
Cyprus	1.7	19	Asia	Western Asia
Czech Republic	1.7	181	Europe	Eastern Europe
Belgium	1.7	180	Europe	Western Europe
Canada	1.6	554	Americas	Northern America
Vietnam	1.6	1,346	Asia	South-Eastern Asia
Maldives	1.6	5	Asia	Southern Asia
Somalia	1.5	138+	Africa	Eastern Africa
Algeria	1.5	516	Africa	Northern Africa
Slovakia	1.5	84	Europe	Eastern Europe
Bosnia and Herzegovina	1.5	56	Europe	Southern Europe
Greece	1.5	176	Europe	Southern Europe
Morocco	1.4	447	Africa	Northern Africa
Armenia	1.4	44	Asia	Western Asia
Croatia	1.4	62	Europe	Southern Europe
Hungary	1.3	133	Europe	Eastern Europe
Andorra	1.3	1	Europe	Southern Europe
Egypt	1.2	992	Africa	Northern Africa
Ireland	1.2	54	Europe	Northern Europe
United Kingdom	1.2	722	Europe	Northern Europe
Portugal	1.2	124	Europe	Southern Europe
Serbia	1.2	123	Europe	Southern Europe
Tunisia	1.1	117	Africa	Northern Africa
Poland	1.1	436	Europe	Eastern Europe
France	1.1	682	Europe	Western Europe
Netherlands	1.1	179	Europe	Western Europe
Samoa	1.1	2	Oceania	Polynesia
China	1.0	13,410	Asia	Eastern Asia
Bhutan	1.0	7	Asia	Southern Asia
Saudi Arabia	1.0	265+	Asia	Western Asia
Sweden	1.0	91	Europe	Northern Europe
Malta	1.0	4	Europe	Southern Europe
Australia	1.0	229	Oceania	Australasia
Tonga	1.0	1	Oceania	Polynesia
Qatar	0.9	13	Asia	Western Asia
Denmark	0.9	47	Europe	Northern Europe
Italy	0.9	529	Europe	Southern Europe
New Zealand	0.9	39	Oceania	Australasia
Vanuatu	0.9	2	Oceania	Melanesia
Federated States of Micronesia	0.9	1	Oceania	Micronesia
United Arab Emirates	0.8	39	Asia	Western Asia
Spain	0.8	390	Europe	Southern Europe
Germany	0.8	690	Europe	Western Europe
Macau	0.7	4	Asia	Eastern Asia
Oman	0.7	18	Asia	Western Asia
Slovenia	0.7	15	Europe	Southern Europe
Switzerland	0.7	52	Europe	Western Europe
Bahrain	0.6	6	Asia	Western Asia
Norway	0.6	29	Europe	Northern Europe
Austria	0.6	56	Europe	Western Europe
Guam	0.6	1	Oceania	Micronesia
Brunei	0.5	2	Asia	South-Eastern Asia
French Polynesia	0.4	1	Oceania	Polynesia
Japan	0.3	442 [8]	Asia	Eastern Asia
Singapore	0.3	16	Asia	South-Eastern Asia
Iceland	0.3	1	Europe	Northern Europe
Hong Kong	0.2	17	Asia	Eastern Asia
Monaco	0.0	0	Europe	Western Europe
Palau	0.0	0	Oceania	Micronesia