CoCalc Public Filestmp / Gun Data.ipynbOpen in with one click!
Author: William A. Stein

Some gun violence analysis with Wikipedia data

As requested by John Stokes, 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 and here).

GDP data from World Bank, again via Wikipedia.

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 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.

In [3]:
%load_ext retina %pylab inline
--------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-3-247cca258f7f> in <module>() ----> 1 get_ipython().magic(u'load_ext retina') 2 get_ipython().magic(u'pylab inline') /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in magic(self, arg_s) 2203 magic_name, _, magic_arg_s = arg_s.partition(' ') 2204 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC) -> 2205 return self.run_line_magic(magic_name, magic_arg_s) 2206 2207 #------------------------------------------------------------------------- /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_line_magic(self, magic_name, line) 2124 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals 2125 with self.builtin_trap: -> 2126 result = fn(*args,**kwargs) 2127 return result 2128 /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/IPython/core/magics/extension.pyc in load_ext(self, module_str) /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k) 191 # but it's overkill for just that one bit of state. 192 def magic_deco(arg): --> 193 call = lambda f, *a, **k: f(*a, **k) 194 195 if callable(arg): /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/IPython/core/magics/extension.pyc in load_ext(self, module_str) 61 if not module_str: 62 raise UsageError('Missing module name.') ---> 63 res = self.shell.extension_manager.load_extension(module_str) 64 65 if res == 'already loaded': /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/IPython/core/extensions.pyc in load_extension(self, module_str) 96 if module_str not in sys.modules: 97 with prepended_to_syspath(self.ipython_extension_dir): ---> 98 __import__(module_str) 99 mod = sys.modules[module_str] 100 if self._call_load_ipython_extension(mod): ImportError: No module named retina
In [ ]:
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

In [ ]:
def plot_percent(df, limit=10): df['Gun Percent'][:limit].plot() plt.ylim(0,100) plt.title("% Gun Homicide") plt.show()
In [4]:
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()
In [8]:
def display_relevant(df, limit=10): display(df.ix[:,['Homicides', 'Gun Homicides', 'Gun Data Source']][:limit])

Load the data

In [9]:
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.

In [7]:
data = data.sort("Gun Homicides", ascending=False) display_relevant(data, 15)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-7-d79cb638699f> in <module>() ----> 1 data = data.sort("Gun Homicides", ascending=False) 2 display_relevant(data, 15) NameError: name 'data' is not defined

Take top 30 Countries by GDP

In [8]:
top = data.sort('GDP')[-30:]
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-8-85c400f287e7> in <module>() ----> 1 top = data.sort('GDP')[-30:] NameError: name 'data' is not defined

and rank them by Gun Homicides per capita:

In [12]:
top_by_guns = top.sort("Gun Homicides", ascending=False) display_relevant(top_by_guns, 5) plot_percapita(top_by_guns, 10)
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TF65ZVXdP78eUnSsWPHNGvWrCJXv65Hr1699P777+vYsWOSpNOnT2vt\n2rVF3tOkSRP5+/sX+b3YtGmTPvroI8fr+++/Xy+88IJyc3MlSRs3btSUKVMkSV988YVOnTqle++9\nVxMnTlRycvINZb6Y04cjt27dqoMHD2rq1KnKzMzU6dOnFRISol7/u95KUFBQkdKVkZFRpJQBAGCa\nrUYNZSYmuuQ416ugDFksFs2bN08vvfSS5syZI0mqXLmyPvroI3l753/bv+eeezRkyBCtXbtWkyZN\n0sSJE9W3b1+FhYWpQYMG6ty5c5GSdiPLTQwZMkS5ubnq3bu3Y2L+6NGjFRkZedVzKe7cCj5u2bKl\nXnrpJT300EOSpPPnz+tvf/vbJZ/rww8/1EsvvaT33ntPeXl5Cg8P18CBAx37X3jhBU2dOlXdu3eX\nr6+vAgMDHfPFgoKC9Mgjj8jX11d5eXl69dVXr/v3odjztF/LPa03aMeOHdqyZUuRiflHjx7V7Nmz\nNXbsWFmtVi1evFjly5e/7JywNWvWqFWrVtd0XO/1612yyJ6Uv9Be7h13uORYAIDSd+TIEVWtWtV0\nDLiRK/2dSU5OvuwdlS5foqKgwSYkJCg6OloVK1ZUp06dNHbsWHl5eal27drq16+fq2MBAAC4lEtL\nWOGVbgtfCoyKirrkjgYAAExw4QARPMT1/p1hxXwAAArx8vLS2bNnTceAG7Db7Tp+/Lj8/Pyu69ez\nYj4AAIVUqlRJ6enpOnXqlFOefwjPUHD1Kzg4+JLFakuKEgYAQCEWi0WVK1c2HQO3AIYjAQAADKCE\nAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAZQwgAAAAyghAEAABhACQMA\nADCAEgYAAGAAJQwAAMAAShgAAIAB3qYDuMLpkJo6PW+5aw4WUkNlXXMkAADgxm6JEvbj6dvUK7aF\nS46VmJipO5TrkmMBAAD3xXAkAACAAZQwAAAAAyhhAAAABlDCAAAADKCEAQAAGEAJAwAAMIASBgAA\nYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAa45NmRc+fO1e7du+Xj46PIyEj17NmzyP5x48bJbrfL\nas3vhLGxsQoPD3dFNAAAACOcXsLOnTunJk2a6MEHH5QkjR8/XnfeeaeCg4Md77FYLBo9erT8/Pyc\nHQcAAOCm4PQS5u/vrzZt2kiSzp4969hWmJeXl6ZMmaKsrCy1bNlSAwYMcHYsAAAAo1wyHClJH3zw\ngZKSkjRw4ED5+voW2Tdq1Cj5+PjIZrNp9uzZ2rx5s6O4AQAAeCKXTcwfMmSI3nrrLW3ZskX79+8v\nss/Hxyc/jNWqDh06XLIfAADA0zi9hO3du1ebNm2SJPn6+iokJEQZGRmO/adOndKiRYskSTabTRs3\nblSDBg2cHQsAAMAopw9HVqlSRYmJiVqxYoUkqWHDhoqIiFBCQoKio6MVGhqqvLw8jRkzRt7e3oqM\njFRERISzYwEAABjl9BJWtmxZPfvss5dsHzhwoOPjmJgYxcTEODsKAADATYPFWgEAAAyghAEAABhA\nCQMAADCAEgYAAGAAJQwAAMAAShgAAIABlDAAAAADKGEAAAAGUMIAAAAMoIQBAAAYQAkDAAAwgBIG\nAABgACUMAADAAEoYAACAAZQwAAAAAyhhAAAABlDCAAAADKCEAQAAGEAJAwAAMIASBgAAYAAlDAAA\nwABKGAAAgAGUMAAAAAMoYQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIAB\nlDAAAAADvF1xkLlz52r37t3y8fFRZGSkevbsWWR/UlKSVq1aJavVqrp162ro0KGuiAUAAGCM00vY\nuXPn1KRJEz344IOSpPHjx+vOO+9UcHCwJCk9PV1r1qxRfHy8LBaLFi9erLVr16pz587OjgYAAGCM\n04cj/f391aZNG0nS2bNnHdsKbN26VVFRUbJYLJKkrl27asuWLc6OBQAAYJRLhiMl6YMPPlBSUpIG\nDhwoX19fx/asrCzVqlXL8To4OFgZGRmuigUAAGCEyybmDxkyRG+99Za2bNmi/fv3O7YHBQUVKV0Z\nGRmOoUoAAABP5fQStnfvXm3atEmS5Ovrq5CQkCKlq0WLFkpKSpLNZpMkrV69Wq1bt3Z2LAAAAKOc\nPhxZpUoVJSYmasWKFZKkhg0bKiIiQgkJCYqOjlbFihXVqVMnjR07Vl5eXqpdu7b69evn7FgAAABG\nOb2ElS1bVs8+++wl2wcOHOj4OCoqSlFRUc6OAgAAcNNgsVYAAAADKGEAAAAGUMIAAAAMoIQBAAAY\nQAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAAAyhhAAAABlDCAAAADKCEAQAAGEAJAwAAMIAS\nBgAAYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwA\nAMAAShgAAIABlDAAAAADKGEAAAAGUMIAAAAMoIQBAAAYUKIStmvXrku2JSQklHoYAACAW0WJSti/\n/vUvpaamOl4vXrxYe/bscVooAAAAT+ddkjc99dRTmj59ul544QV999132r59u8aMGVPig3zxxRda\nv369vL29VbVqVT366KPy9v6/Q48bN052u11Wa34njI2NVXh4+DWeCgAAgPsoUQkLDw/XQw89pHHj\nxikkJEQvvPCC/Pz8SnSArKwsHTx4UJMmTZLFYtH8+fO1ceNG/elPf3K8x2KxaPTo0SX+nAAAAO7u\nsiXsk08+uWRbWFiY6tatq+XLl8tisahfv35XPUBgYKCGDRvmeJ2Tk6OKFSsWeY+Xl5emTJmirKws\ntWzZUgMGDLiWcwDcinX/flkLDe87k61GDdnq1HHJsQAA1+ayJezioiRJd9111w0dbOnSpQoICFD9\n+vWLbB81apR8fHxks9k0e/Zsbd68WW3atLmhYwE3K2tqqoJ69XLJsTITEylhAHCTumwJu/vuux0f\nnz9/XmfOnFHZsmXl6+t7zQex2Wz697//rSpVqqhv376X7Pfx8ZEkWa1WdejQQfv27aOEAQAAj3bZ\nEmaz2bRs2TIlJSXp3Llz8vf317lz5+Tn56eoqCj16dPHMZH+Ss6dO6dZs2bpzjvvVGRk5CX7T506\npS+//FL9+/eXzWbTxo0b1bFjxxs7KwAAgJvcZUvYvHnzdPbsWcXFxSk0NNSx/dSpU1qwYIE+/PBD\nDRky5KoHWLt2rfbs2aOsrCytXLlSktSpUyelpKQoOjpaoaGhysvL05gxY+Tt7a3IyEhFRETc+JkB\nAADcxC5bwn799VdNmjTpku2hoaF6/PHHS7xERY8ePdSjR48rvicmJkYxMTEl+nwAAACe4LLjiVcb\navTy8ir1MAAAALeKyzatypUra9myZbLb7UW222w2LV26VJUqVXJ6OAAAAE912eHIRx55RHPnztWw\nYcNUuXJllSlTRufOnVNaWppatmypRx991JU5AQAAPMplS5i/v78ee+wxnT9/XmlpacrOzlaZMmVU\npUqV61qmAgAAAP/nio8tSk9P13fffad9+/bp7NmzCggIUN26ddWhQwdVrlzZVRkBAAA8zmXnhCUn\nJ2v8+PGSpK5du6p///7q0qWLJGnChAnavHmzaxICAAB4oMteCVuyZIkmT56skJCQItsjIiJ09913\na+rUqaxqDwAAcJ0ueyXM29v7kgJWIDQ0VN7eVxzJBAAAwBVctoTZ7XalpaUVu+/IkSOy2WxOCwUA\nAODpLns5a+DAgYqPj1fr1q1Vp04dx7Mj9+3bpy1btujpp592ZU4AAACPctkS1qBBA02dOlWbN2/W\n/v37HUtU1K9fXzExMQoICHBlTgAAAI9yxYldAQEBioqKUlRUlKvyAAAA3BKu/IBIAAAAOMVlr4Rl\nZWVd9RcHBgaWahgAAIBbxWVL2N/+9jcFBwfLy8ur2P0Wi0Vvvvmm04IBAAB4ssuWsMGDB2vXrl16\n5plnXJkHAADglnDZOWH33HOPvLy8tGnTJlfmAQAAuCVc8e7IJ5544rLDkQAAwLWs+/fLmprqkmPZ\natSQrU4dlxzrVnXFEkYBAwDg5mFNTVVQr14uOVZmYiIlzMlYogIAAMAAShgAAIABlDAAAAADSlTC\n4uPjnZ0DAADgllKiEmaz2XTmzBlnZwEAALhlXPHuyALh4eF66aWX1LFjR/n4+EjKXzH/3nvvdWo4\nAAAAT1WiEubv76/27dsrLy9PeXl5zs6Ea8CaMQAAuKcSlbD777/f2TlwnVgzBgAA91SiOWEXLlzQ\nggULNHXqVElScnKyzp0759RgAAAAnqxEJSwhIUGVK1dWZmamJOmPP/7Qhx9+6NRgAAAAnqxEJSw1\nNVWdO3d2PMaoe/fuOnDggFODAQAAeLISzQmz2+2XbLNaS77O6xdffKH169fL29tbVatW1aOPPipv\n7/87dFJSklatWiWr1aq6detq6NChJf7cAAAA7qhETSokJESHDx92vP7qq69UsWLFEh0gKytLBw8e\n1KRJkzRhwgQFBQVp48aNjv3p6elas2aN4uPjHfvXrl17jacBAADgXkpUwgYNGqS5c+fq4MGDeu65\n5/Tf//5XDz74YIkOEBgYqGHDhslisUiScnJyihS4rVu3KioqyrG/a9eu2rJly7WeBwAAgFsp0XBk\n+fLl9fzzz+vIkSOSpGrVql3XwZYuXaqAgADVr1/fsS0rK0u1atVyvA4ODlZGRsZ1fX4AAAB3UeKJ\nXSkpKfrqq6+0Zs0apV7j4qA2m03vvfeefHx8FBMTU2RfUFBQkdKVkZGh4ODga/r8AAAA7qZEJezn\nn3/WjBkzVLlyZYWFhWnatGn65ZdfSnSAc+fOafr06YqIiCj2MUctWrRQUlKSbDabJGn16tVq3br1\nNZwCAACA+ynRcOSSJUv0wgsvKCwsTJLUrl07zZo1S02aNLnqr127dq327NmjrKwsrVy5UpLUqVMn\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+uOPPxyFzBNER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at 0x10d745d90>"]}

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

In [13]:
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
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GHDBtWqVas4mgIAAOCYYhlqbNeunXbt2qVBgwbJx8dHTZo0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{"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>"],"metadata":{},"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]"]}

Let's just compare US, Canada, and UK:

In [15]:
select = data.ix[['United States', 'Canada', 'United Kingdom']] plot_percapita(select)
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//CRwIAAPB/9SphLpfLU8AkKSoqynPl6liuuuoqXXXVVUd9\nTUpKilJSUur1fgAAAE1BvYYja2pqvNYKc7lcPMgbAADgL6hXCUtJSdE777wj6eDCre+8844uu+wy\nnwYDAABoyuo1HLlgwQL9/vvvWrx4sSSpvLxcUVFRmj9/viTJYrHohRde8F1KAACAJqZeJezFF1/0\ndQ4AAICAUv8HQAIAAOCkoYQBAAAYQAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAAAyhhAAAA\nBlDCAAAADKCEAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAZQwgAAAAyg\nhAEAABhACQMAADCAEgYAAGAAJQwAAMAAu+kAAMyzbtsm644dpmM0Oa7oaLnatTMdA4CfooQBkHXH\nDoX37Ws6RpOzNzeXEgagTgxHAgAAGEAJAwAAMIASBgAAYAAlDAAAwIAGmZhfXV2tt956Sxs3btTE\niROPOD5u3Di53W5ZrQc7YUZGhuLi4hoiGgAAgBENUsLmzJmjTp06aePGjbUet1gsGj16tIKDgxsi\nDgAAgHENUsIyMjIkSe+9916tx202m5588kmVlZWpS5cuGjRoUEPEAgAAMMYv1gkbNWqUHA6HXC6X\npk6dqpUrV6pr166mYwEAAPiMX0zMdzgckiSr1apu3bpp27ZtZgMBAAD4mPEStmfPHr377ruSJJfL\npeXLlys+Pt5wKgAAAN8yNhw5e/ZspaamKjIyUjU1NXr44Ydlt9uVnJyshIQEU7EAAAAaRIOWsMOX\np0hPT/d8nZaWprS0tIaMAgAAYJTx4UgAAIBARAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAA\nAyhhAAAABlDCAAAADKCEAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAZQ\nwgAAAAyghAEAABhgNx0AAIDjYd22TdYdO0zHaHJc0dFytWtnOkZAoYQBABoV644dCu/b13SMJmdv\nbi4lrIExHAkAAGAAJQwAAMAAShgAAIABlDAAAAADKGEAAAAGUMIAAAAMoIQBAAAYQAkDAAAwgBIG\nAABgACUMAADAAEoYAACAAT5/dmR1dbXeeustbdy4URMnTjzieEFBgRYvXiyr1arY2FgNHTrU15EA\nAACM8/mVsDlz5qhTp061HisuLlZeXp6ysrL0+OOPKzw8XPn5+b6OBAAAYJzPS1hGRoYSExNrPbZm\nzRqlpKTIYrFIknr37q1Vq1b5OhIAAIBxRueElZWVyel0eradTqdKS0sNJgIAAGgYRktYeHi4V+kq\nLS31KmUikfVbAAAULUlEQVQAAABNldES1rlzZxUUFMjlckmSlixZoqSkJJORAAAAGoTP746szezZ\ns5WamqrmzZurR48eGjt2rGw2m9q2bauBAweaiAQAANCgGqyEHb48RXp6uufrlJQUpaSkNFQMAAAA\nv8BirQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIABlDAAAAADKGEAAAAG\nUMIAAAAMoIQBAAAYQAkDAAAwgBIGAABggN10AADmlUS0UUnOQtMxmp6IaDUznQGA36KEAdC3JWep\nb0Zn0zGanNzcvbpE1aZjAPBTDEcCAAAYQAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAAAyhh\nAAAABlDCAAAADKCEAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAbYG+Ik\nubm5WrFihSQpMTFRAwYM8Do+btw4ud1uWa0HO2FGRobi4uIaIhoAAIARPi9hGzdu1NatW5WVlSVJ\neumll7R27Vqdd955ntdYLBaNHj1awcHBvo4DAADgF3xewr799lv16tXLs92rVy999dVXXiXMZrPp\nySefVFlZmbp06aJBgwb5OhYAAIBRPi9he/fuVXh4uGfb6XSqpKTE6zWjRo2Sw+GQy+XS1KlTtXLl\nSnXt2tXX0QAAAIzx+cT88PBwlZaWerZLS0vldDq9XuNwOA6GsVrVrVs3bdu2zdexAAAAjPJ5CUtM\nTFR+fr5nOz8/X0lJSZ7tPXv26N1335UkuVwuLV++XPHx8b6OBQAAYJTPhyPPPfdcbd68WY888oik\ng6XsvPPO0+zZs5WamqrIyEjV1NTo4Ycflt1uV3JyshISEnwdCwAAwKgGWaKiX79+6tevn9e+9PR0\nz9dpaWlKS0triCgAAAB+gcVaAQAADKCEAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAA\nAAMoYQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIABlDAAAAADKGEAAAAG\nUMIAAAAMoIQBAAAYQAkDAAAwgBIGAABgACUMAADAAEoYAACAAZQwAAAAAyhhAAAABlDCAAAADKCE\nAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAAAgAF20wEAADgeJRFtVJKz0HSMeqmqqpLD4TAdo34i\notXMdIYA0yAlLDc3VytWrJAkJSYmasCAAV7HCwoKtHjxYlmtVsXGxmro0KENEQsA0Ah9W3KW+mZ0\nNh2jycnN3atLVG06RkDx+XDkxo0btXXrVmVlZSkrK0tFRUVau3at53hxcbHy8vKUlZWlxx9/XOHh\n4crPz/d1LAAAAKMsbrfb7csTvPXWW0pISFCnTp0kST/88IO++uor3XTTTZKkTz75RHa7XT179pQk\n7dmzR6+99poefPDBWt8vLy/Pl3EBAABOql69etW63+fDkXv37lV4eLhn2+l0qqSkxLNdVlammJgY\nr+OlpaV1vl9dHwQAAKAx8flwZHh4uFepKi0tldPprPdxAACApsjnJSwxMdFrjld+fr6SkpI82507\nd1ZBQYFcLpckacmSJV7HAQAAmiKfzwmTpA8++MDr7shrr71Ws2fPVmpqqpxOp5YuXarFixfLZrOp\nbdu2+uc//+nrSAAAAEY1SAkDAACAN1bMBwAAMIASBo9D8/IAAIDv8diiALdgwQIlJyfr119/1dtv\nv60uXbooPT3ddCygTr///rvKy8slHXwkTGxsrOFEgLfNmzdr4cKF2rdvn2dfZmamwUTwV5SwALdq\n1Sr1799fn376qZ566ik98sgjpiMBdZoxY4a2bdumX3/9Va1atVJkZKRGjhxpOhbgZfr06br11lvV\nqlUr01Hg5xiODHAul0u//PKLgoKC5Ha7ZbPZTEcC6rR582ZlZmYqJiZG48aNk93OvyPhf0JDQ9Wu\nXTuFhIR4fgG14W+wANevXz+9/fbbuvHGG/Xjjz+qe/fupiMBdWrWrJkkyW63q6amRn/88YfhRMCR\nevTooXnz5ik+Pl5W68FrHR06dDCcCv6IEhbgkpOT1bVrV/3xxx+Kj49XfHy86UhAnc477zwVFxfr\noosuUnZ2tqeUAf5k48aNKikp8ZoTRglDbVgnLMBt2LBB8+fP13//+18NHz5cv/32my677DLTsYBj\n2rVrl0477TSG0OF3xowZo+zsbNMx0AhwJSzAzZs3Tw899JCys7PVvn175eTkUMLgVz7++GOvbYvF\nIrfbLYvFIkm64oorTMQC6hQXF6fff/9dUVFRpqPAz1HCAlxNTY1ncrPFYmECKfzO4T+ThYWF2rp1\nqy6++GJ99dVXatu2rcFkQO02bdqke++9V2eccYZnTtjEiRMNp4I/ooQFuMjISG3atEmS9OOPPyoy\nMtJwIsDb5Zdf7vk6Oztbo0aNUnBwsFJSUjR58mRzwYA6TJo0yVO+gKOhhAW4f/7zn8rJydH+/fv1\n2WefaejQoaYjAXWqqqpScHCwJCkoKEjV1dWGEwFHcrvdWrBggX744Qedc8456tevH6UMteKnIsBt\n3rxZt912myZNmqSbb75ZP/zwg+lIQJ2CgoK0a9cuSdLu3bsVFBRkOBFwpJycHNlsNo0YMUIOh0Mz\nZswwHQl+ihIW4D788EOv7QULFhhKAhxbRkaGnn32WU2cOFFTpkzhEVvwS1u3blVqaqpOPfVUXXPN\nNdq+fbvpSPBTDEcGqCVLligvL0+//vqrRo8eLeng6vnnnnuu4WRA3dq0aaPs7GyVlZUpLCzMdByg\nVofu3AWOhXXCAtz48eN5XiQajT/++EOfffaZysvL5Xa7VV1dzTxG+J3p06erRYsW6t69u7788ksV\nFRVp2LBhpmPBDzEcGeAeeeQRuVwuVVZWqrKyUmVlZaYjAXV69tlnFRUVpbVr1+rMM89kSRX4pYyM\nDFVWVuqVV15RRUWFbrzxRtOR4KcYjgxwCxcu1Geffab//ve/CgsLU8uWLbkyBr9lt9t1+eWX64sv\nvlDPnj319NNPm44EHMFut+vaa681HQONACUswC1btkxPPfWUJkyYoFGjRun11183HQmok91uV2Vl\npZxOp3bu3Om5UxIw7dDc2kN2796tiIgIlZSU6PTTT2exVtSKEhbgIiIiZLVaZbPZZLfbtXv3btOR\ngDoNGjRIlZWVGjBggF599VX9/e9/Nx0JkOS9Iv7bb7+t9u3bq0uXLlqzZo02bNhgMBn8GSUswLVp\n00a7du3SeeedpxdffNF0HOCo4uLiJElOp1OZmZmG0wC127hxowYNGiRJ6ty5sz744APDieCvKGEB\nLiMjQ5J0zTXXaP369YqNjTWcCKjb9u3btWjRIu3bt0/SwaUARo4caTgV4O3Piw64XC5DSeDvuDsy\nwB0+sbljx46aNm2awTTA0U2dOlVJSUkaPHiw5xfgbxISEjRz5kxt2bJFOTk56tSpk+lI8FNcCQtQ\nW7Zs0ZYtW7R9+3YtXrzYs+bSf/7zH9PRgDqFh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at 0x10e768d90>"]}

Normalize to the US numbers (inverse)

In [16]:
select['Homicides'] = select['Homicides']['United States'] / select['Homicides'] select['Gun Homicides'] = select['Gun Homicides']['United States'] / select['Gun Homicides'] display_relevant(select)
{"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>"],"metadata":{},"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]"]}

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:

In [14]:
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