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## Some gun violence analysis with Wikipedia data
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# As [requested by John Stokes](https://twitter.com/jonst0kes/status/282330530412888064),
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# here are per-capita numbers for gun-related homicides,
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# relating to GDP and total homicides,
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# so the situation in the United States can be put in context relative to other nations.
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# main data source is UNODC (via Wikipedia [here](http://en.wikipedia.org/wiki/List_of_countries_by_intentional_homicide_rate)
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# and [here](http://en.wikipedia.org/wiki/List_of_countries_by_firearm-related_death_rate)).
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#
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# GDP data from World Bank, again [via Wikipedia](http://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita).
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#
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# If the numbers on Wikipedia are inaccurate, or their relationship is not sound
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# (e.g. numbers taken from different years, during which significant change occured)
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# then obviously None of this analysis is valid.
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#
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# To summarize the data,
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# every possible way you look at it the US is lousy at preventing gun violence.
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# Even when compared to significantly more violent places,
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# gun violence in the US is a serious problem,
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# and when compared to similarly wealthy places,
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# the US is an outstanding disaster.
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# **UPDATE:** the relationship of the gun data and totals does not seem to be valid.
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# [FBI data](http://www2.fbi.gov/ucr/cius2009/offenses/violent_crime/index.html) suggests that
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# the relative contribution of guns to homicides in the US is 47%,
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# but relating these two data sources gives 80%.
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# Internal comparisons should still be fine, but 'fraction' analysis has been stricken.
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# In[1]:
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%load_ext retina
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%pylab inline
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# Out[1]:
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#
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# Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
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# For more information, type 'help(pylab)'.
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#
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# In[2]:
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from IPython.display import display
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import pandas
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pandas.set_option('display.notebook_repr_html', True)
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pandas.set_option('display.precision', 2)
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# Some utility functions for display
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# In[3]:
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def plot_percent(df, limit=10):
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df['Gun Percent'][:limit].plot()
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plt.ylim(0,100)
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plt.title("% Gun Homicide")
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plt.show()
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# In[4]:
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def plot_percapita(df, limit=10):
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df = df.ix[:,['Homicides', 'Gun Homicides']][:limit]
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df['Total Homicides'] = df['Homicides'] - df['Gun Homicides']
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del df['Homicides']
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df.plot(kind='bar', stacked=True, sort_columns=True)
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plt.ylabel("per 100k")
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plt.show()
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# In[8]:
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def display_relevant(df, limit=10):
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display(df.ix[:,['Homicides', 'Gun Homicides', 'Gun Data Source']][:limit])
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# Load the data
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# In[9]:
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totals = pandas.read_csv('totals.csv', '\t', index_col=0)
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guns = pandas.read_csv('guns.csv', '\t', index_col=0)
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gdp = pandas.read_csv('gdp.csv', '\t', index_col=1)
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data = totals.join(guns).join(gdp)
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data['Gun Percent'] = 100 * data['Gun Homicides'] / data['Homicides']
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del data['Unintentional'],data['Undetermined'],data['Gun Suicides']
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data = data.dropna()
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# Of all sampled countries (Found data for 68 countries),
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# the US is in the top 15 in Gun Homicides per capita.
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#
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# Numbers are per 100k.
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# In[10]:
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data = data.sort("Gun Homicides", ascending=False)
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display_relevant(data, 15)
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# Out[10]:
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# Homicides Gun Homicides Gun Data Source
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# Country
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# El Salvador 69.2 50.4 OAS 2011[1]
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# Jamaica 52.2 47.4 OAS 2011[1]
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# Honduras 91.6 46.7 OAS 2011[1]
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# Guatemala 38.5 38.5 OAS 2011[1]
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# Colombia 33.4 27.1 UNODC 2011 [2]
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# Brazil 21.0 18.1 UNODC 2011[3]
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# Panama 21.6 12.9 OAS 2011[1]
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# Mexico 16.9 10.0 UNODC 2011[4]
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# Paraguay 11.5 7.3 UNODC 2000[11]
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# Nicaragua 13.6 7.1 OAS 2011[1]
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# United States 4.2 3.7 OAS 2012[5][6]
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# Costa Rica 10.0 3.3 UNODC 2002[7]
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# Uruguay 5.9 3.2 UNODC 2002[7]
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# Argentina 3.4 3.0 UNODC 2011[12]
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# Barbados 11.3 3.0 UNODC 2000[11]
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# Take top 30 Countries by GDP
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# In[11]:
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top = data.sort('GDP')[-30:]
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# and rank them by Gun Homicides per capita:
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# In[12]:
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top_by_guns = top.sort("Gun Homicides", ascending=False)
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display_relevant(top_by_guns, 5)
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plot_percapita(top_by_guns, 10)
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# Out[12]:
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# Homicides Gun Homicides Gun Data Source
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# Country
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# United States 4.2 3.7 OAS 2012[5][6]
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# Israel 2.1 0.9 WHO 2012[10]
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# Canada 1.6 0.8 Krug 1998[13]
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# Luxembourg 2.5 0.6 WHO 2012[10]
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# Greece 1.5 0.6 Krug 1998[13]
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# image file: tests/ipynbref/Gun_Data_orig_files/Gun_Data_orig_fig_00.png
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# **NOTE:** these bar graphs should not be interpreted as fractions of a total,
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# as the two data sources do not appear to be comparable.
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# But the red and blue bar graphs should still be internally comparable.
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# The US is easily #1 of 30 wealthiest countries in Gun Homicides per capita,
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# by a factor of 4:1
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# Adding USA, Canada, and Mexico to all of Europe,
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# USA is a strong #2 behind Mexico in total gun homicides per-capita
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# In[13]:
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index = (data['Region'] == 'Europe') + \
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(data.index == 'United States') + \
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(data.index == 'Canada') + \
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(data.index == 'Mexico')
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selected = data[index]
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print "By Total Gun Homicides"
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sys.stdout.flush()
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by_guns = selected.sort("Gun Homicides", ascending=False)
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#by_guns['Gun Homicides'].plot(kind='bar')
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plot_percapita(by_guns, limit=25)
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display_relevant(selected, limit=None)
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# Out[13]:
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# By Total Gun Homicides
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#
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# image file: tests/ipynbref/Gun_Data_orig_files/Gun_Data_orig_fig_01.png
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# Homicides Gun Homicides Gun Data Source
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# Country
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# Mexico 16.9 10.0 UNODC 2011[4]
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# United States 4.2 3.7 OAS 2012[5][6]
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# Montenegro 3.5 2.1 WHO 2012[10]
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# Moldova 7.5 1.0 WHO 2012[10]
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# Canada 1.6 0.8 Krug 1998[13]
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# Serbia 1.2 0.6 WHO 2012[10]
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# Luxembourg 2.5 0.6 WHO 2012[10]
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# Greece 1.5 0.6 Krug 1998[13]
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# Croatia 1.4 0.6 WHO 2012[10]
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# Switzerland 0.7 0.5 OAS 2011[1]
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# Malta 1.0 0.5 WHO 2012[10]
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# Portugal 1.2 0.5 WHO 2012[10]
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# Belarus 4.9 0.4 UNODC 2002[7]
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# Ireland 1.2 0.4 WHO 2012[10]
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# Italy 0.9 0.4 WHO 2012[10]
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# Ukraine 5.2 0.3 UNODC 2000[11]
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# Estonia 5.2 0.3 WHO 2012[10]
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# Belgium 1.7 0.3 WHO 2012[10]
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# Finland 2.2 0.3 WHO 2012[10]
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# Lithuania 6.6 0.2 WHO 2012[10]
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# Bulgaria 2.0 0.2 WHO 2012[10]
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# Georgia 4.3 0.2 WHO 2012[10]
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# Denmark 0.9 0.2 WHO 2012[10]
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# France 1.1 0.2 WHO 2012[10]
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# Netherlands 1.1 0.2 WHO 2012[10]
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# Sweden 1.0 0.2 WHO 2012[10]
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# Slovakia 1.5 0.2 WHO 2012[10]
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# Austria 0.6 0.2 WHO 2012[10]
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# Latvia 3.1 0.2 WHO 2012[10]
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# Spain 0.8 0.1 WHO 2012[10]
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# Hungary 1.3 0.1 WHO 2012[10]
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# Czech Republic 1.7 0.1 WHO 2012[10]
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# Germany 0.8 0.1 WHO 2012[10]
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# Slovenia 0.7 0.1 WHO 2012[10]
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# Romania 2.0 0.0 WHO 2012[10]
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# United Kingdom 1.2 0.0 WHO2012 [10]
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# Norway 0.6 0.0 WHO 2012[10]
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# Poland 1.1 0.0 WHO 2012[10]
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# Let's just compare US, Canada, and UK:
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# In[15]:
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select = data.ix[['United States', 'Canada', 'United Kingdom']]
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plot_percapita(select)
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# Out[15]:
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# image file: tests/ipynbref/Gun_Data_orig_files/Gun_Data_orig_fig_02.png
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# Normalize to the US numbers (inverse)
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# In[16]:
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select['Homicides'] = select['Homicides']['United States'] / select['Homicides']
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select['Gun Homicides'] = select['Gun Homicides']['United States'] / select['Gun Homicides']
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display_relevant(select)
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# Out[16]:
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# Homicides Gun Homicides Gun Data Source
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# United States 1.0 1.0 OAS 2012[5][6]
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# Canada 2.6 4.9 Krug 1998[13]
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# United Kingdom 3.5 92.5 WHO2012 [10]
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# So, you are 2.6 times more likely to be killed in the US than Canada,
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# and 3.5 times more likely than in the UK.
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# That's bad, but not extreme.
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#
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# However, you are 4.9 times more likely to be killed *with a gun* in the US than Canada,
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# and almost 100 times more likely than in the UK. That is pretty extreme.
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#
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# Countries represented:
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# In[14]:
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for country in data.index:
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print country
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# Out[14]:
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# El Salvador
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# Jamaica
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# Honduras
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# Guatemala
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# Colombia
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# Brazil
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# Panama
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# Mexico
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# Paraguay
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# Nicaragua
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# United States
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# Costa Rica
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# Uruguay
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# Argentina
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# Barbados
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# Montenegro
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# Peru
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# Moldova
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# Israel
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# India
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# Canada
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# Serbia
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# Luxembourg
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# Greece
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# Uzbekistan
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# Croatia
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# Kyrgyzstan
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# Switzerland
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# Malta
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# Portugal
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# Belarus
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# Ireland
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# Italy
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# Kuwait
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# Ukraine
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# Estonia
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# Belgium
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# Finland
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# Lithuania
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# Cyprus
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# Bulgaria
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# Georgia
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# Denmark
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# France
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# Netherlands
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# Sweden
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# Slovakia
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# Qatar
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# Austria
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# Latvia
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# New Zealand
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# Spain
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# Hungary
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# Czech Republic
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# Hong Kong
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# Australia
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# Singapore
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# Chile
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# Germany
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# Slovenia
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# Romania
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# Azerbaijan
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# South Korea
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# United Kingdom
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# Norway
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# Japan
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# Poland
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# Mauritius
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#
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