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@@ -0,0 +1,436 @@
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+{
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "# US Gun Deaths Guided Project Solutions"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 30,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [],
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+ "source": [
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+ "import csv\n",
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+ "\n",
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+ "with open(\"guns.csv\", \"r\") as f:\n",
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+ " reader = csv.reader(f)\n",
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+ " data = list(reader)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 31,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "[['', 'year', 'month', 'intent', 'police', 'sex', 'age', 'race', 'hispanic', 'place', 'education'], ['1', '2012', '01', 'Suicide', '0', 'M', '34', 'Asian/Pacific Islander', '100', 'Home', '4'], ['2', '2012', '01', 'Suicide', '0', 'F', '21', 'White', '100', 'Street', '3'], ['3', '2012', '01', 'Suicide', '0', 'M', '60', 'White', '100', 'Other specified', '4'], ['4', '2012', '02', 'Suicide', '0', 'M', '64', 'White', '100', 'Home', '4']]\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "print(data[:5])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 32,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "[['', 'year', 'month', 'intent', 'police', 'sex', 'age', 'race', 'hispanic', 'place', 'education']]\n",
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+ "[['1', '2012', '01', 'Suicide', '0', 'M', '34', 'Asian/Pacific Islander', '100', 'Home', '4'], ['2', '2012', '01', 'Suicide', '0', 'F', '21', 'White', '100', 'Street', '3'], ['3', '2012', '01', 'Suicide', '0', 'M', '60', 'White', '100', 'Other specified', '4'], ['4', '2012', '02', 'Suicide', '0', 'M', '64', 'White', '100', 'Home', '4'], ['5', '2012', '02', 'Suicide', '0', 'M', '31', 'White', '100', 'Other specified', '2']]\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "headers = data[:1]\n",
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+ "data = data[1:]\n",
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+ "print(headers)\n",
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+ "print(data[:5])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 33,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "{'2012': 33563, '2013': 33636, '2014': 33599}"
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+ ]
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+ },
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+ "execution_count": 33,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "years = [row[1] for row in data]\n",
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+ "\n",
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+ "year_counts = {}\n",
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+ "for year in years:\n",
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+ " if year not in year_counts:\n",
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+ " year_counts[year] = 0\n",
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+ " year_counts[year] += 1\n",
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+ "\n",
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+ "year_counts "
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 34,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "[datetime.datetime(2012, 1, 1, 0, 0),\n",
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+ " datetime.datetime(2012, 1, 1, 0, 0),\n",
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+ " datetime.datetime(2012, 1, 1, 0, 0),\n",
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+ " datetime.datetime(2012, 2, 1, 0, 0),\n",
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+ " datetime.datetime(2012, 2, 1, 0, 0)]"
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+ ]
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+ },
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+ "execution_count": 34,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "import datetime\n",
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+ "\n",
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+ "dates = [datetime.datetime(year=int(row[1]), month=int(row[2]), day=1) for row in data]\n",
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+ "dates[:5]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 35,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "{datetime.datetime(2012, 1, 1, 0, 0): 2758,\n",
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+ " datetime.datetime(2012, 2, 1, 0, 0): 2357,\n",
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+ " datetime.datetime(2012, 3, 1, 0, 0): 2743,\n",
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+ " datetime.datetime(2012, 4, 1, 0, 0): 2795,\n",
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+ " datetime.datetime(2012, 5, 1, 0, 0): 2999,\n",
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+ " datetime.datetime(2012, 6, 1, 0, 0): 2826,\n",
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+ " datetime.datetime(2012, 7, 1, 0, 0): 3026,\n",
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+ " datetime.datetime(2012, 8, 1, 0, 0): 2954,\n",
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+ " datetime.datetime(2012, 9, 1, 0, 0): 2852,\n",
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+ " datetime.datetime(2012, 10, 1, 0, 0): 2733,\n",
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+ " datetime.datetime(2012, 11, 1, 0, 0): 2729,\n",
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+ " datetime.datetime(2012, 12, 1, 0, 0): 2791,\n",
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+ " datetime.datetime(2013, 1, 1, 0, 0): 2864,\n",
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+ " datetime.datetime(2013, 2, 1, 0, 0): 2375,\n",
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+ " datetime.datetime(2013, 3, 1, 0, 0): 2862,\n",
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+ " datetime.datetime(2013, 4, 1, 0, 0): 2798,\n",
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+ " datetime.datetime(2013, 5, 1, 0, 0): 2806,\n",
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+ " datetime.datetime(2013, 6, 1, 0, 0): 2920,\n",
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+ " datetime.datetime(2013, 7, 1, 0, 0): 3079,\n",
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+ " datetime.datetime(2013, 8, 1, 0, 0): 2859,\n",
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+ " datetime.datetime(2013, 9, 1, 0, 0): 2742,\n",
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+ " datetime.datetime(2013, 10, 1, 0, 0): 2808,\n",
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+ " datetime.datetime(2013, 11, 1, 0, 0): 2758,\n",
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+ " datetime.datetime(2013, 12, 1, 0, 0): 2765,\n",
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+ " datetime.datetime(2014, 1, 1, 0, 0): 2651,\n",
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+ " datetime.datetime(2014, 2, 1, 0, 0): 2361,\n",
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+ " datetime.datetime(2014, 3, 1, 0, 0): 2684,\n",
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+ " datetime.datetime(2014, 4, 1, 0, 0): 2862,\n",
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+ " datetime.datetime(2014, 5, 1, 0, 0): 2864,\n",
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+ " datetime.datetime(2014, 6, 1, 0, 0): 2931,\n",
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+ " datetime.datetime(2014, 7, 1, 0, 0): 2884,\n",
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+ " datetime.datetime(2014, 8, 1, 0, 0): 2970,\n",
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+ " datetime.datetime(2014, 9, 1, 0, 0): 2914,\n",
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+ " datetime.datetime(2014, 10, 1, 0, 0): 2865,\n",
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+ " datetime.datetime(2014, 11, 1, 0, 0): 2756,\n",
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+ " datetime.datetime(2014, 12, 1, 0, 0): 2857}"
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+ ]
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+ },
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+ "execution_count": 35,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "date_counts = {}\n",
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+ "\n",
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+ "for date in dates:\n",
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+ " if date not in date_counts:\n",
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+ " date_counts[date] = 0\n",
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+ " date_counts[date] += 1\n",
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+ "\n",
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+ "date_counts"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 54,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "{'F': 14449, 'M': 86349}"
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+ ]
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+ },
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+ "execution_count": 54,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "sexes = [row[5] for row in data]\n",
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+ "sex_counts = {}\n",
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+ "for sex in sexes:\n",
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+ " if sex not in sex_counts:\n",
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+ " sex_counts[sex] = 0\n",
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+ " sex_counts[sex] += 1\n",
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+ "sex_counts"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 36,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "{'Asian/Pacific Islander': 1326,\n",
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+ " 'Black': 23296,\n",
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+ " 'Hispanic': 9022,\n",
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+ " 'Native American/Native Alaskan': 917,\n",
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+ " 'White': 66237}"
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+ ]
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+ },
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+ "execution_count": 36,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "races = [row[7] for row in data]\n",
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+ "race_counts = {}\n",
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+ "for race in races:\n",
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+ " if race not in race_counts:\n",
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+ " race_counts[race] = 0\n",
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+ " race_counts[race] += 1\n",
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+ "race_counts"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "## Findings so far\n",
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+ "\n",
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+ "Gun deaths in the US seem to disproportionately affect men vs women. They also seem to disproportionately affect minorities, although having some data on the percentage of each race in the overall US population would help.\n",
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+ "\n",
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+ "There appears to be a minor seasonal correlation, with gun deaths peaking in the summer and declining in the winter. It might be useful to filter by intent, to see if different categories of intent have different correlations with season, race, or gender."
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 57,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "[['Id',\n",
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+ " 'Year',\n",
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+ " 'Id',\n",
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+ " 'Sex',\n",
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+ " 'Id',\n",
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+ " 'Hispanic Origin',\n",
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+ " 'Id',\n",
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+ " 'Id2',\n",
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+ " 'Geography',\n",
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+ " 'Total',\n",
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+ " 'Race Alone - White',\n",
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+ " 'Race Alone - Hispanic',\n",
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+ " 'Race Alone - Black or African American',\n",
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+ " 'Race Alone - American Indian and Alaska Native',\n",
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+ " 'Race Alone - Asian',\n",
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+ " 'Race Alone - Native Hawaiian and Other Pacific Islander',\n",
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+ " 'Two or More Races'],\n",
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+ " ['cen42010',\n",
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+ " 'April 1, 2010 Census',\n",
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+ " 'totsex',\n",
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+ " 'Both Sexes',\n",
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+ " 'tothisp',\n",
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+ " 'Total',\n",
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+ " '0100000US',\n",
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+ " '',\n",
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+ " 'United States',\n",
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+ " '308745538',\n",
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+ " '197318956',\n",
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+ " '44618105',\n",
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+ " '40250635',\n",
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+ " '3739506',\n",
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+ " '15159516',\n",
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+ " '674625',\n",
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+ " '6984195']]"
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+ ]
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+ },
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+ "execution_count": 57,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "import csv\n",
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+ "\n",
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+ "with open(\"census.csv\", \"r\") as f:\n",
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+ " reader = csv.reader(f)\n",
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+ " census = list(reader)\n",
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+ " \n",
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+ "census"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 40,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "{'Asian/Pacific Islander': 8.374309664161762,\n",
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+ " 'Black': 57.8773477735196,\n",
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+ " 'Hispanic': 20.220491210910907,\n",
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+ " 'Native American/Native Alaskan': 24.521955573811088,\n",
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+ " 'White': 33.56849303419181}"
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+ ]
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+ },
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+ "execution_count": 40,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "mapping = {\n",
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+ " \"Asian/Pacific Islander\": 15159516 + 674625,\n",
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+ " \"Native American/Native Alaskan\": 3739506,\n",
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+ " \"Black\": 40250635,\n",
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+ " \"Hispanic\": 44618105,\n",
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+ " \"White\": 197318956\n",
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+ "}\n",
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+ "\n",
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+ "race_per_hundredk = {}\n",
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+ "for k,v in race_counts.items():\n",
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+ " race_per_hundredk[k] = (v / mapping[k]) * 100000\n",
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+ "\n",
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+ "race_per_hundredk"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 41,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "{'Asian/Pacific Islander': 3.530346230970155,\n",
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+ " 'Black': 48.471284987180944,\n",
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+ " 'Hispanic': 12.627161104219914,\n",
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+ " 'Native American/Native Alaskan': 8.717729026240365,\n",
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+ " 'White': 4.6356417981453335}"
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+ ]
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+ },
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+ "execution_count": 41,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "intents = [row[3] for row in data]\n",
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+ "homicide_race_counts = {}\n",
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+ "for i,race in enumerate(races):\n",
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+ " if race not in homicide_race_counts:\n",
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+ " homicide_race_counts[race] = 0\n",
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+ " if intents[i] == \"Homicide\":\n",
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+ " homicide_race_counts[race] += 1\n",
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+ "\n",
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+ "race_per_hundredk = {}\n",
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+ "for k,v in homicide_race_counts.items():\n",
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+ " race_per_hundredk[k] = (v / mapping[k]) * 100000\n",
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+ "\n",
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+ "race_per_hundredk "
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "## Findings\n",
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+ "\n",
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+ "It appears that gun related homicides in the US disproportionately affect people in the `Black` and `Hispanic` racial categories.\n",
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+ "\n",
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+ "Some areas to investigate further:\n",
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+ "\n",
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+ "* The link between month and homicide rate.\n",
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+ "* Homicide rate by gender.\n",
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+ "* The rates of other intents by gender and race.\n",
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+ "* Gun death rates by location and education."
|
|
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+ ]
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+ }
|
|
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+ ],
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+ "metadata": {
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|
+ "kernelspec": {
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+ "display_name": "Python 3",
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|
+ "language": "python",
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+ "name": "python3"
|
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+ },
|
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+ "language_info": {
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+ "codemirror_mode": {
|
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|
+ "name": "ipython",
|
|
|
+ "version": 3
|
|
|
+ },
|
|
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+ "file_extension": ".py",
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|
+ "mimetype": "text/x-python",
|
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|
+ "name": "python",
|
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|
+ "nbconvert_exporter": "python",
|
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|
+ "pygments_lexer": "ipython3",
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+ "version": "3.4.2"
|
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 0
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+}
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Vik Paruchuri