# Some gun violence analysis with Wikipedia data As [requested by John Stokes](https://twitter.com/jonst0kes/status/282330530412888064), here are per-capita numbers for gun-related homicides, relating to GDP and total homicides, so the situation in the United States can be put in context relative to other nations. main data source is UNODC (via Wikipedia [here](http://en.wikipedia.org/wiki/List_of_countries_by_intentional_homicide_rate) and [here](http://en.wikipedia.org/wiki/List_of_countries_by_firearm-related_death_rate)). GDP data from World Bank, again [via Wikipedia](http://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita). If the numbers on Wikipedia are inaccurate, or their relationship is not sound (e.g. numbers taken from different years, during which significant change occured) then obviously None of this analysis is valid. To summarize the data, every possible way you look at it the US is lousy at preventing gun violence. Even when compared to significantly more violent places, gun violence in the US is a serious problem, and when compared to similarly wealthy places, the US is an outstanding disaster. **UPDATE:** the relationship of the gun data and totals does not seem to be valid. [FBI data](http://www2.fbi.gov/ucr/cius2009/offenses/violent_crime/index.html) suggests that the relative contribution of guns to homicides in the US is 47%, but relating these two data sources gives 80%. Internal comparisons should still be fine, but 'fraction' analysis has been stricken. 
%load_ext retina
%pylab inline
Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline]. For more information, type 'help(pylab)'. 
from IPython.display import display
import pandas
pandas.set_option('display.notebook_repr_html', True)
pandas.set_option('display.precision', 2)
Some utility functions for display 
def plot_percent(df, limit=10):
    df['Gun Percent'][:limit].plot()
    plt.ylim(0,100)
    plt.title("% Gun Homicide")
    plt.show()

def plot_percapita(df, limit=10):
    df = df.ix[:,['Homicides', 'Gun Homicides']][:limit]
    df['Total Homicides'] = df['Homicides'] - df['Gun Homicides']
    del df['Homicides']
    df.plot(kind='bar', stacked=True, sort_columns=True)
    plt.ylabel("per 100k")
    plt.show()

def display_relevant(df, limit=10):
    display(df.ix[:,['Homicides', 'Gun Homicides', 'Gun Data Source']][:limit])
Load the data 
totals = pandas.read_csv('totals.csv', '\t', index_col=0)
guns = pandas.read_csv('guns.csv', '\t', index_col=0)
gdp = pandas.read_csv('gdp.csv', '\t', index_col=1)
data = totals.join(guns).join(gdp)
data['Gun Percent'] = 100 * data['Gun Homicides'] / data['Homicides']
del data['Unintentional'],data['Undetermined'],data['Gun Suicides']
data = data.dropna()
Of all sampled countries (Found data for 68 countries), the US is in the top 15 in Gun Homicides per capita. Numbers are per 100k. 
data = data.sort("Gun Homicides", ascending=False)
display_relevant(data, 15)
Homicides Gun Homicides Gun Data Source Country El Salvador 69.2 50.4 OAS 2011[1] Jamaica 52.2 47.4 OAS 2011[1] Honduras 91.6 46.7 OAS 2011[1] Guatemala 38.5 38.5 OAS 2011[1] Colombia 33.4 27.1 UNODC 2011 [2] Brazil 21.0 18.1 UNODC 2011[3] Panama 21.6 12.9 OAS 2011[1] Mexico 16.9 10.0 UNODC 2011[4] Paraguay 11.5 7.3 UNODC 2000[11] Nicaragua 13.6 7.1 OAS 2011[1] United States 4.2 3.7 OAS 2012[5][6] Costa Rica 10.0 3.3 UNODC 2002[7] Uruguay 5.9 3.2 UNODC 2002[7] Argentina 3.4 3.0 UNODC 2011[12] Barbados 11.3 3.0 UNODC 2000[11] Take top 30 Countries by GDP 
top = data.sort('GDP')[-30:]
and rank them by Gun Homicides per capita: 
top_by_guns = top.sort("Gun Homicides", ascending=False)
display_relevant(top_by_guns, 5)
plot_percapita(top_by_guns, 10)
Homicides Gun Homicides Gun Data Source Country United States 4.2 3.7 OAS 2012[5][6] Israel 2.1 0.9 WHO 2012[10] Canada 1.6 0.8 Krug 1998[13] Luxembourg 2.5 0.6 WHO 2012[10] Greece 1.5 0.6 Krug 1998[13] ![](tests/ipynbref/Gun_Data_orig_files/Gun_Data_orig_fig_00.png) **NOTE:** these bar graphs should not be interpreted as fractions of a total, as the two data sources do not appear to be comparable. But the red and blue bar graphs should still be internally comparable. The US is easily #1 of 30 wealthiest countries in Gun Homicides per capita, by a factor of 4:1 Adding USA, Canada, and Mexico to all of Europe, USA is a strong #2 behind Mexico in total gun homicides per-capita 
index = (data['Region'] == 'Europe') + \
        (data.index == 'United States') + \
        (data.index == 'Canada') + \
        (data.index == 'Mexico')
selected = data[index]

print "By Total Gun Homicides"
sys.stdout.flush()

by_guns = selected.sort("Gun Homicides", ascending=False)
#by_guns['Gun Homicides'].plot(kind='bar')
plot_percapita(by_guns, limit=25)
display_relevant(selected, limit=None)
By Total Gun Homicides ![](tests/ipynbref/Gun_Data_orig_files/Gun_Data_orig_fig_01.png) Homicides Gun Homicides Gun Data Source Country Mexico 16.9 10.0 UNODC 2011[4] United States 4.2 3.7 OAS 2012[5][6] Montenegro 3.5 2.1 WHO 2012[10] Moldova 7.5 1.0 WHO 2012[10] Canada 1.6 0.8 Krug 1998[13] Serbia 1.2 0.6 WHO 2012[10] Luxembourg 2.5 0.6 WHO 2012[10] Greece 1.5 0.6 Krug 1998[13] Croatia 1.4 0.6 WHO 2012[10] Switzerland 0.7 0.5 OAS 2011[1] Malta 1.0 0.5 WHO 2012[10] Portugal 1.2 0.5 WHO 2012[10] Belarus 4.9 0.4 UNODC 2002[7] Ireland 1.2 0.4 WHO 2012[10] Italy 0.9 0.4 WHO 2012[10] Ukraine 5.2 0.3 UNODC 2000[11] Estonia 5.2 0.3 WHO 2012[10] Belgium 1.7 0.3 WHO 2012[10] Finland 2.2 0.3 WHO 2012[10] Lithuania 6.6 0.2 WHO 2012[10] Bulgaria 2.0 0.2 WHO 2012[10] Georgia 4.3 0.2 WHO 2012[10] Denmark 0.9 0.2 WHO 2012[10] France 1.1 0.2 WHO 2012[10] Netherlands 1.1 0.2 WHO 2012[10] Sweden 1.0 0.2 WHO 2012[10] Slovakia 1.5 0.2 WHO 2012[10] Austria 0.6 0.2 WHO 2012[10] Latvia 3.1 0.2 WHO 2012[10] Spain 0.8 0.1 WHO 2012[10] Hungary 1.3 0.1 WHO 2012[10] Czech Republic 1.7 0.1 WHO 2012[10] Germany 0.8 0.1 WHO 2012[10] Slovenia 0.7 0.1 WHO 2012[10] Romania 2.0 0.0 WHO 2012[10] United Kingdom 1.2 0.0 WHO2012 [10] Norway 0.6 0.0 WHO 2012[10] Poland 1.1 0.0 WHO 2012[10] Let's just compare US, Canada, and UK: 
select = data.ix[['United States', 'Canada', 'United Kingdom']]
plot_percapita(select)
![](tests/ipynbref/Gun_Data_orig_files/Gun_Data_orig_fig_02.png) Normalize to the US numbers (inverse) 
select['Homicides'] = select['Homicides']['United States'] / select['Homicides']
select['Gun Homicides'] = select['Gun Homicides']['United States'] / select['Gun Homicides']
display_relevant(select)
Homicides Gun Homicides Gun Data Source United States 1.0 1.0 OAS 2012[5][6] Canada 2.6 4.9 Krug 1998[13] United Kingdom 3.5 92.5 WHO2012 [10] So, you are 2.6 times more likely to be killed in the US than Canada, and 3.5 times more likely than in the UK. That's bad, but not extreme. However, you are 4.9 times more likely to be killed *with a gun* in the US than Canada, and almost 100 times more likely than in the UK. That is pretty extreme. Countries represented: 
for country in data.index:
    print country
El Salvador Jamaica Honduras Guatemala Colombia Brazil Panama Mexico Paraguay Nicaragua United States Costa Rica Uruguay Argentina Barbados Montenegro Peru Moldova Israel India Canada Serbia Luxembourg Greece Uzbekistan Croatia Kyrgyzstan Switzerland Malta Portugal Belarus Ireland Italy Kuwait Ukraine Estonia Belgium Finland Lithuania Cyprus Bulgaria Georgia Denmark France Netherlands Sweden Slovakia Qatar Austria Latvia New Zealand Spain Hungary Czech Republic Hong Kong Australia Singapore Chile Germany Slovenia Romania Azerbaijan South Korea United Kingdom Norway Japan Poland Mauritius