12523-218.ipynb

{
	"cells": [{
			"cell_type": "code",
			"execution_count": 2,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"[['', '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"
				]
			}],
			"source": [
				"import csv\n",
				"f = open(\"guns.csv\", \"r\")\n",
				"csv_file = csv.reader(f)\n",
				"data = list(csv_file)\n",
				"print(data[:5])"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 3,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"['', 'year', 'month', 'intent', 'police', 'sex', 'age', 'race', 'hispanic', 'place', 'education']\n",
					"[['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"
				]
			}],
			"source": [
				"headers=data[0]\n",
				"print(headers)\n",
				"data=data[1:len(data)]\n",
				"print(data[:5])"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 4,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"['2012', '2012', '2012', '2012']\n",
					"{'2013', '2012', '2014'}\n",
					"{'2013': 33636, '2012': 33563, '2014': 33599}\n"
				]
			}],
			"source": [
				"years=[i[1] for i in data]\n",
				"print(years[:4])\n",
				"years_set = set(years)\n",
				"print(years_set)\n",
				"year_counts={}\n",
				"for i in years:\n",
				"    if i in year_counts.keys():\n",
				"        year_counts[i] += 1\n",
				"    else:\n",
				"        year_counts[i] = 1\n",
				"print(year_counts)\n",
				" "
			]
		},
		{
			"cell_type": "code",
			"execution_count": 47,
			"metadata": {
				"collapsed": false,
				"scrolled": true
			},
			"outputs": [{
				"data": {
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BpKWl4eXlxa5du1i2bBlPPvkkS5curY/hiog0Gw1qZgPg5uYGXJzlVFZWmr8MWN2XU69c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\n",
					"text/plain": [
						"<matplotlib.figure.Figure at 0x7fa326db7940>"
					]
				},
				"metadata": {},
				"output_type": "display_data"
			}],
			"source": [
				"        \n",
				"from matplotlib import pyplot as plt\n",
				"%matplotlib inline\n",
				"\n",
				"year_counts_name = year_counts.keys()\n",
				"year_counts_values = year_counts.values()\n",
				"year_counts_range = range(len(year_counts))\n",
				"\n",
				"plt.bar(year_counts_range, year_counts_values, \n",
				"        tick_label=year_counts_name, align='center',\n",
				"       color=('seagreen', 'palevioletred'))\n",
				"plt.xticks(rotation=90)\n",
				"plt.title('Gun deaths grouped by year in % \\n')\n",
				"plt.xlabel('Years')\n",
				"plt.ylabel('Count of years \\n')\n",
				"plt.show()"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"The gun deaths per year are nearly constant."
			]
		},
		{
			"cell_type": "code",
			"execution_count": 51,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"['01', '01', '01', '02', '02']\n",
					"{'08', '10', '06', '09', '04', '05', '01', '03', '12', '11', '02', '07'}\n",
					"{'08': 8783, '10': 8406, '06': 8677, '09': 8508, '04': 8455, '05': 8669, '01': 8273, '03': 8289, '12': 8413, '11': 8243, '02': 7093, '07': 8989}\n"
				]
			}],
			"source": [
				"# exploring variable month\n",
				"months = [i[2] for i in data]\n",
				"print(months[:5])\n",
				"# what are the unique values of the variable months\n",
				"months_set= set(months)\n",
				"print(months_set)\n",
				"# all months are included: okay\n",
				"# count gun deaths per months, meaning count row per month\n",
				"gundeaths_month = {}\n",
				"for i in data:\n",
				"    if i[2] in gundeaths_month.keys():\n",
				"        gundeaths_month[i[2]] += 1\n",
				"    else:\n",
				"        gundeaths_month[i[2]] = 1\n",
				"\n",
				"print(gundeaths_month)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 87,
			"metadata": {
				"collapsed": false,
				"scrolled": true
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"[('08', 8783), ('10', 8406), ('06', 8677), ('09', 8508), ('04', 8455), ('05', 8669), ('01', 8273), ('03', 8289), ('12', 8413), ('11', 8243), ('02', 7093), ('07', 8989)]\n",
					"[('01', 8273), ('02', 7093), ('03', 8289), ('04', 8455), ('05', 8669), ('06', 8677), ('07', 8989), ('08', 8783), ('09', 8508), ('10', 8406), ('11', 8243), ('12', 8413)]\n"
				]
			}],
			"source": [
				"gundeaths_month_list = [(k, v) for k,v in gundeaths_month.items()]\n",
				"print(gundeaths_month_list)\n",
				"gundeaths_month_list_sorted = sorted(gundeaths_month_list)\n",
				"print(gundeaths_month_list_sorted)\n",
				"\n",
				"    "
			]
		},
		{
			"cell_type": "code",
			"execution_count": 104,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
					"name": "stdout",
					"output_type": "stream",
					"text": [
						"['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']\n",
						"[8273, 7093, 8289, 8455, 8669, 8677, 8989, 8783, 8508, 8406, 8243, 8413]\n"
					]
				},
				{
					"data": {
						"image/png": 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kZOCDD2TuiKi0Ll4EFi0C3n8f+PnnordXrSpzlE8+CTRv7vr2uSHdiqtWquT9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\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x7fa316252278>"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				}
			],
			"source": [
				"x_gundeaths_month = []\n",
				"y_gundeaths_month = []\n",
				"for row in gundeaths_month_list_sorted:\n",
				"    x_gundeaths_month.append(row[0])\n",
				"    y_gundeaths_month.append(row[1])\n",
				"\n",
				"print(x_gundeaths_month)\n",
				"print(y_gundeaths_month)\n",
				"\n",
				"plt.plot(x_gundeaths_month, y_gundeaths_month, linestyle='solid', linewidth=3.0, color=\"red\")\n",
				"plt.title(\"Gundeaths by months \\n\", fontsize=14, color='black', weight = 'bold')\n",
				"plt.xlabel(\"Months \\n\",  fontsize=14, color='blue')\n",
				"plt.ylabel(\"Absolute number of gun deaths \\n\", fontsize=14, color='blue')\n",
				"plt.show()\n"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"The gun death by mont has a low in February and peaks in July. Speculation: because it is cold in Feburary people are less outside and are less likely to be killed outside. Further investigation: this makes sense for homicides to a degree but does it also for suicide? Also temperature for the southern region are in February also higher. A seperation by \"cold\" and \"hot\" southern region could help to understand this. The differentitation between northern and southern region is not in the data set and has to be constructed with other sources if possible.\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 5,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"[datetime.datetime(2012, 1, 1, 0, 0), datetime.datetime(2012, 1, 1, 0, 0), datetime.datetime(2012, 1, 1, 0, 0), datetime.datetime(2012, 2, 1, 0, 0), datetime.datetime(2012, 2, 1, 0, 0)]\n",
					"[datetime.datetime(2014, 9, 1, 0, 0), datetime.datetime(2014, 9, 1, 0, 0), datetime.datetime(2014, 9, 1, 0, 0), datetime.datetime(2014, 9, 1, 0, 0), datetime.datetime(2014, 9, 1, 0, 0)]\n",
					"36\n",
					"2012-06-01 00:00:00 2826\n",
					"2013-08-01 00:00:00 2859\n",
					"2013-07-01 00:00:00 3079\n",
					"2012-04-01 00:00:00 2795\n",
					"2013-10-01 00:00:00 2808\n",
					"2014-01-01 00:00:00 2651\n"
				]
			}],
			"source": [
				"import datetime\n",
				"dates = [datetime.datetime(year=int(i[1]), month=int(i[2]), day=1) for i in data]\n",
				"print(dates[:5])\n",
				"len(dates)\n",
				"print(dates[90000:90005])\n",
				"date_counts = {}\n",
				"for date in dates:\n",
				"    if date in date_counts.keys():\n",
				"        date_counts[date] += 1\n",
				"    else:\n",
				"        date_counts[date] = 1\n",
				"print(len(date_counts))\n",
				"\n",
				"counter_5=0\n",
				"for key, value in date_counts.items():\n",
				"    print(key, value)\n",
				"    counter_5+=1\n",
				"    if counter_5 > 5:\n",
				"        break"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 6,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"['', 'year', 'month', 'intent', 'police', 'sex', 'age', 'race', 'hispanic', 'place', 'education']\n"
				]
			}],
			"source": [
				"print(headers)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 7,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
					"name": "stdout",
					"output_type": "stream",
					"text": [
						"{'F', 'M'}\n",
						"['M', 'F', 'M', 'M', 'M']\n",
						"dict_items([('F', 14449), ('M', 86349)])\n",
						"{'Hispanic', 'Native American/Native Alaskan', 'Black', 'White', 'Asian/Pacific Islander'}\n",
						"{'Hispanic': 9022, 'Native American/Native Alaskan': 917, 'Black': 23296, 'White': 66237, 'Asian/Pacific Islander': 1326}\n",
						"['F', 'M']\n"
					]
				},
				{
					"data": {
						"image/png": 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89tln1vc0Z78vOuurzy/HmX/ciYiInZoNm7/97W8AxMXFsWPHDnr16gXAoUOH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\ncE3GAAAAAElFTkSuQmCC\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x7fa329088278>"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				},
				{
					"data": {
						"image/png": 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ePCQkJKCgoAAFBQXYt28fACAhIQFWVlY4d+4cYmNjsWDBAsn2lYhIX2hUsamursYPP/yA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\nAwCwHGUDALAcZQMAsBxlAwCw3H8HtLXeZ13FKgAAAABJRU5ErkJggg==\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x7fa326d96d30>"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				}
			],
			"source": [
				"# sex: 5\n",
				"sex_column = []\n",
				"for i in data:\n",
				"    sex_column.append(i[5])\n",
				"    \n",
				"sex_column_set = set(sex_column)\n",
				"print(sex_column_set)\n",
				"\n",
				"sex_column_2 = [i[5] for i in data]\n",
				"print(sex_column_2[:5])\n",
				"\n",
				"sex_counts={}\n",
				"for sex in sex_column_2:\n",
				"    if sex in sex_counts.keys():\n",
				"        sex_counts[sex] += 1\n",
				"    else:\n",
				"        sex_counts[sex] = 1\n",
				"\n",
				"print(sex_counts.items())\n",
				"# race: 7 \n",
				"race_counts = {}\n",
				"race_column = [ i[7] for i in data]\n",
				"race_column_set = set(race_column)\n",
				"print(race_column_set)\n",
				"for race in race_column:\n",
				"    if race in race_counts.keys():\n",
				"        race_counts[race] += 1\n",
				"    else:\n",
				"        race_counts[race] = 1\n",
				"print(race_counts)\n",
				"        \n",
				"\n",
				"\n",
				"names_sex = list(sex_counts.keys())\n",
				"print(names_sex )\n",
				"values_sex = list(sex_counts.values())\n",
				"\n",
				"#tick_label does the some work as plt.xticks()\n",
				"plt.bar(range(len(sex_counts)),values_sex , tick_label=names_sex,  align='center', color=('green', 'red'))\n",
				"plt.xticks(rotation=90)\n",
				"plt.title(\"US gun deaths by gender \\n\")\n",
				"plt.xlabel(\"Gender \\n\")\n",
				"plt.ylabel(\"Number of deaths \\n\")\n",
				"plt.show()      \n",
				"\n",
				"name_race=race_counts.keys()\n",
				"values_race=race_counts.values()\n",
				"length_race=range(len(race_counts))\n",
				"\n",
				"plt.bar(length_race, values_race, tick_label=name_race, align=\"center\")\n",
				"plt.xticks(rotation=90)\n",
				"plt.title(\"US gun deaths by race \\n\")\n",
				"plt.xlabel(\"Races \\n\")\n",
				"plt.ylabel(\"Number of deaths \\n\")\n",
				"plt.show()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 8,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"[['Id', 'Year', 'Id', 'Sex', 'Id', 'Hispanic Origin', 'Id', 'Id2', 'Geography', 'Total', 'Race Alone - White', 'Race Alone - Hispanic', 'Race Alone - Black or African American', 'Race Alone - American Indian and Alaska Native', 'Race Alone - Asian', 'Race Alone - Native Hawaiian and Other Pacific Islander', 'Two or More Races'], ['cen42010', 'April 1, 2010 Census', 'totsex', 'Both Sexes', 'tothisp', 'Total', '0100000US', '', 'United States', '308745538', '197318956', '44618105', '40250635', '3739506', '15159516', '674625', '6984195']]\n"
				]
			}],
			"source": [
				"f = open(\"census.csv\", \"r\")\n",
				"csvfile = csv.reader(f)\n",
				"censusdata = list(csvfile)\n",
				"print(censusdata)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 9,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"dict_keys(['Hispanic', 'Native American/Native Alaskan', 'Black', 'White', 'Asian/Pacific Islander'])\n",
					"197318956\n",
					"44618105\n",
					"40250635\n",
					"3739506\n",
					"15159516\n",
					"674625\n",
					"15834141\n",
					"{'Hispanic': 44618105, 'Native American/Native Alaskan': 3739506, 'Black': 40250635, 'White': 197318956, 'Asian/Pacific Islander': 15834141}\n"
				]
			}],
			"source": [
				"mapping = {}\n",
				"\n",
				"print(race_counts.keys())\n",
				"\n",
				"censusdata_white = [ i[10] for i in censusdata]\n",
				"censusdata_white=int(censusdata_white[1])\n",
				"print(censusdata_white)\n",
				"censusdata_hispanic = [ i[11] for i in censusdata]\n",
				"censusdata_hispanic=int(censusdata_hispanic[1])\n",
				"print(censusdata_hispanic)\n",
				"censusdata_black = [ i[12] for i in censusdata]\n",
				"censusdata_black=int(censusdata_black[1])\n",
				"print(censusdata_black)\n",
				"censusdata_native = [ i[13] for i in censusdata]\n",
				"censusdata_native=int(censusdata_native[1])\n",
				"print(censusdata_native)\n",
				"censusdata_asian = [i[14] for i in censusdata]\n",
				"censusdata_asian=int(censusdata_asian[1])\n",
				"print(censusdata_asian)\n",
				"censusdata_hawai = [i[15] for i in censusdata]\n",
				"censusdata_hawai=int(censusdata_hawai[1])\n",
				"print(censusdata_hawai)\n",
				"asian_hawai = censusdata_hawai + censusdata_asian\n",
				"print(asian_hawai)\n",
				"\n",
				"mapping['White'] = censusdata_white\n",
				"mapping['Asian/Pacific Islander'] = asian_hawai\n",
				"mapping[ 'Black'] = censusdata_black \n",
				"mapping['Native American/Native Alaskan'] = censusdata_native\n",
				"mapping['Hispanic'] = censusdata_hispanic\n",
				"print(mapping)\n",
				"\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 10,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"{'Hispanic': 20.22, 'Native American/Native Alaskan': 24.52, 'Black': 57.88, 'White': 33.57, 'Asian/Pacific Islander': 8.37}\n"
				]
			}],
			"source": [
				"\n",
				"race_per_hundredk={}\n",
				"for i,v in race_counts.items():\n",
				"    for u,b in mapping.items():\n",
				"        if i == u:\n",
				"            rr=v/b*100000\n",
				"            race_per_hundredk[i]=round(rr,2)\n",
				"    \n",
				"print(race_per_hundredk)\n",
				"\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 11,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
					"name": "stdout",
					"output_type": "stream",
					"text": [
						"['Hispanic', 'Native American/Native Alaskan', 'Black', 'White', 'Asian/Pacific Islander']\n"
					]
				},
				{
					"data": {
						"image/png": 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FCxYgPT0dN27cwJIlS+SxAAAIDAzEL7/8gg0bNmDEiBElPlZRjx49QvXq1aFSqXDr1i0s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\nggAAOKIgAACO/gfI7L71fOgRkQAAAABJRU5ErkJggg==\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x7fa3290efc18>"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				}
			],
			"source": [
				"\n",
				"from matplotlib import pyplot as plt\n",
				"%matplotlib inline\n",
				"\n",
				"names = list(race_per_hundredk.keys())\n",
				"print(names)\n",
				"values = list(race_per_hundredk.values())\n",
				"\n",
				"#tick_label does the some work as plt.xticks()\n",
				"plt.bar(range(len(race_per_hundredk)), values, tick_label=names,  align='center', color=('magenta', \"brown\"))\n",
				"plt.xticks(rotation=90)\n",
				"plt.title(\"US gun deaths grouped by race per 100.000 people \\n\")\n",
				"plt.xlabel(\"Races \\n\")\n",
				"plt.ylabel(\"Number of deaths per 100.000 \\n\")\n",
				"plt.show()\n",
				"\n",
				"    "
			]
		},
		{
			"cell_type": "code",
			"execution_count": 12,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"[['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"
				]
			}],
			"source": [
				"print(data[:5])"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 13,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"Intents: ['Suicide', 'Suicide', 'Suicide', 'Suicide', 'Suicide', 'Suicide', 'Undetermined', 'Suicide', 'Accidental', 'Suicide', 'Suicide', 'Suicide', 'Suicide', 'Suicide', 'Homicide', 'Suicide', 'Suicide', 'Suicide', 'Homicide', 'Suicide']\n",
					"Length of intents: 100798\n",
					"[(0, 'Suicide'), (1, 'Suicide'), (2, 'Suicide'), (3, 'Suicide'), (4, 'Suicide'), (5, 'Suicide'), (6, 'Undetermined'), (7, 'Suicide'), (8, 'Accidental'), (9, 'Suicide')]\n"
				]
			}],
			"source": [
				"\n",
				"intents = [ i[3] for i in data]\n",
				"\n",
				"print(\"Intents: {}\".format(intents[:20]))\n",
				"print(\"Length of intents: {}\".format(len(intents)))\n",
				"\n",
				"intent_enumerate_list =[]\n",
				"for i,value in enumerate(intents):\n",
				"    intent_enumerate_list.append((i,value))\n",
				"print(intent_enumerate_list[:10])"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 14,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"DATA: ['1', '2012', '01', 'Suicide', '0', 'M', '34', 'Asian/Pacific Islander', '100', 'Home', '4']\n"
				]
			}],
			"source": [
				"print(\"DATA: {}\".format(data[0]))"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 15,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
					"name": "stdout",
					"output_type": "stream",
					"text": [
						"Count Death Types: {'Suicide': 63175, 'NA': 1, 'Accidental': 1639, 'Undetermined': 807, 'Homicide': 35176}\n"
					]
				},
				{
					"data": {
						"image/png": 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hgvHss88ahmEYP/74o9GlSxfj5MmTxvbt242rr77aqKqqMgzDMG644Qbjm2++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\n",
						"text/plain": [
							"<matplotlib.figure.Figure at 0x7fa326cbdfd0>"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				}
			],
			"source": [
				"count_death_types = {}\n",
				"\n",
				"for i in intents:\n",
				"    if i in count_death_types.keys():\n",
				"        count_death_types[i] += 1\n",
				"    else:\n",
				"        count_death_types[i] = 1\n",
				"\n",
				"print(\"Count Death Types: {}\".format(count_death_types))\n",
				"\n",
				"\n",
				"names_count_death_types = count_death_types.keys()\n",
				"values_count_death_types = count_death_types.values()\n",
				"range_count_death_types = range(len(count_death_types))\n",
				"\n",
				"plt.bar(range_count_death_types, values_count_death_types, \n",
				"        tick_label=names_count_death_types , align=\"center\" , \n",
				"        color=(\"black\", \"red\"))\n",
				"plt.title(\"Death types numbers \\n\")\n",
				"plt.xlabel(\"Death types \\n\")\n",
				"plt.ylabel(\"Numbers \\n\")\n",
				"plt.xticks(rotation=90)\n",
				"plt.show()\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 16,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"data": {
					"image/png": 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gTp06dOzYsYzOuOoQPd2HXH5+Pq9Nf42m9Zrit9+PNQVraEMbkXAfcs7Ozhw+fBi5XM7I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\n",
					"text/plain": [
						"<matplotlib.figure.Figure at 0x7fa326cf2cf8>"
					]
				},
				"metadata": {},
				"output_type": "display_data"
			}],
			"source": [
				"explode =  (0.1, 0, 0, 0, 0)\n",
				"plt.pie(list(values_count_death_types),  autopct='%1.1f%%', \n",
				"        shadow=True, startangle=140, explode=explode, pctdistance= 0.3, labeldistance=1.4)\n",
				"plt.title(\"Death types numbers \\n\")\n",
				"plt.legend(names_count_death_types , loc=1)\n",
				"plt.axis('equal')\n",
				"\n",
				"plt.show()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 17,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"Number of homicides: 35176\n"
				]
			}],
			"source": [
				"\n",
				"homicides= 0\n",
				"for i in intents:\n",
				"    if i == 'Homicide':\n",
				"        homicides += 1\n",
				"print(\"Number of homicides: {}\".format(homicides))"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 18,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"Races: ['Asian/Pacific Islander', 'White', 'White', 'White', 'White', 'Native American/Native Alaskan', 'White', 'Native American/Native Alaskan', 'White', 'Black']\n",
					"Count races_2: 100798\n",
					"Count intents: 100798\n",
					"A\n"
				]
			}],
			"source": [
				"races_2=[ i[7]  for i in data]\n",
				"\n",
				"print(\"Races: {}\".format(races_2[:10]))\n",
				"print(\"Count races_2: {}\".format(len(races_2)))\n",
				"print(\"Count intents: {}\".format(len(intents)))\n",
				"\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 19,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"whitesmoke #F5F5F5\n",
					"lime #00FF00\n",
					"springgreen #00FF7F\n",
					"sage #87AE73\n",
					"wheat #F5DEB3\n",
					"chocolate #D2691E\n",
					"indianred #CD5C5C\n",
					"burlywood #DEB887\n",
					"darkorange #FF8C00\n",
					"lightsage #BCECAC\n",
					"beige #F5F5DC\n",
					"firebrick #B22222\n",
					"fuchsia #FF00FF\n",
					"turquoise #40E0D0\n",
					"lemonchiffon #FFFACD\n",
					"orangered #FF4500\n",
					"plum #DDA0DD\n",
					"pink #FFC0CB\n",
					"darkturquoise #00CED1\n",
					"mediumspringgreen #00FA9A\n",
					"dodgerblue #1E90FF\n",
					"snow #FFFAFA\n",
					"rosybrown #BC8F8F\n",
					"darkslategray #2F4F4F\n",
					"lightgray #D3D3D3\n",
					"darkred #8B0000\n",
					"gold #FFD700\n",
					"olivedrab #6B8E23\n",
					"lightskyblue #87CEFA\n",
					"darkolivegreen #556B2F\n",
					"lightpink #FFB6C1\n",
					"deeppink #FF1493\n",
					"sandybrown #FAA460\n",
					"blueviolet #8A2BE2\n",
					"coral #FF7F50\n",
					"lavender #E6E6FA\n",
					"palegoldenrod #EEE8AA\n",
					"antiquewhite #FAEBD7\n",
					"mediumblue #0000CD\n",
					"maroon #800000\n",
					"lawngreen #7CFC00\n",
					"steelblue #4682B4\n",
					"darkgoldenrod #B8860B\n",
					"dimgrey #696969\n",
					"teal #008080\n",
					"ghostwhite #F8F8FF\n",
					"orange #FFA500\n",
					"goldenrod #DAA520\n",
					"navy #000080\n",
					"darksalmon #E9967A\n",
					"lightgreen #90EE90\n",
					"mediumpurple #9370DB\n",
					"aqua #00FFFF\n",
					"lightgrey #D3D3D3\n",
					"gainsboro #DCDCDC\n",
					"honeydew #F0FFF0\n",
					"lightsteelblue #B0C4DE\n",
					"tan #D2B48C\n",
					"orchid #DA70D6\n",
					"khaki #F0E68C\n",
					"crimson #DC143C\n",
					"lightslategray #778899\n",
					"navajowhite #FFDEAD\n",
					"slategray #708090\n",
					"seashell #FFF5EE\n",
					"cadetblue #5F9EA0\n",
					"gray #808080\n",
					"ivory #FFFFF0\n",
					"darkkhaki #BDB76B\n",
					"forestgreen #228B22\n",
					"royalblue #4169E1\n",
					"cornsilk #FFF8DC\n",
					"floralwhite #FFFAF0\n",
					"darksage #598556\n",
					"sienna #A0522D\n",
					"brown #A52A2A\n",
					"peru #CD853F\n",
					"purple #800080\n",
					"oldlace #FDF5E6\n",
					"darkslateblue #483D8B\n",
					"darkcyan #008B8B\n",
					"darkgray #A9A9A9\n",
					"bisque #FFE4C4\n",
					"aliceblue #F0F8FF\n",
					"yellow #FFFF00\n",
					"black #000000\n",
					"tomato #FF6347\n",
					"aquamarine #7FFFD4\n",
					"mediumvioletred #C71585\n",
					"lightyellow #FFFFE0\n",
					"skyblue #87CEEB\n",
					"peachpuff #FFDAB9\n",
					"seagreen #2E8B57\n",
					"green #008000\n",
					"lightslategrey #778899\n",
					"powderblue #B0E0E6\n",
					"darkgreen #006400\n",
					"mediumorchid #BA55D3\n",
					"chartreuse #7FFF00\n",
					"slategrey #708090\n",
					"darkslategrey #2F4F4F\n",
					"indigo #4B0082\n",
					"grey #808080\n",
					"deepskyblue #00BFFF\n",
					"lightseagreen #20B2AA\n",
					"olive #808000\n",
					"blanchedalmond #FFEBCD\n",
					"darkorchid #9932CC\n",
					"darkseagreen #8FBC8F\n",
					"palevioletred #DB7093\n",
					"mediumaquamarine #66CDAA\n",
					"greenyellow #ADFF2F\n",
					"yellowgreen #9ACD32\n",
					"violet #EE82EE\n",
					"moccasin #FFE4B5\n",
					"lavenderblush #FFF0F5\n",
					"silver #C0C0C0\n",
					"lightcoral #F08080\n",
					"midnightblue #191970\n",
					"lightblue #ADD8E6\n",
					"blue #0000FF\n",
					"red #FF0000\n",
					"white #FFFFFF\n",
					"palegreen #98FB98\n",
					"limegreen #32CD32\n",
					"hotpink #FF69B4\n",
					"mistyrose #FFE4E1\n",
					"darkgrey #A9A9A9\n",
					"linen #FAF0E6\n",
					"saddlebrown #8B4513\n",
					"azure #F0FFFF\n",
					"paleturquoise #AFEEEE\n",
					"mediumslateblue #7B68EE\n",
					"darkmagenta #8B008B\n",
					"thistle #D8BFD8\n",
					"slateblue #6A5ACD\n",
					"magenta #FF00FF\n",
					"lightcyan #E0FFFF\n",
					"papayawhip #FFEFD5\n",
					"mediumturquoise #48D1CC\n",
					"lightgoldenrodyellow #FAFAD2\n",
					"darkblue #00008B\n",
					"mintcream #F5FFFA\n",
					"dimgray #696969\n",
					"lightsalmon #FFA07A\n",
					"cornflowerblue #6495ED\n",
					"mediumseagreen #3CB371\n",
					"cyan #00FFFF\n",
					"darkviolet #9400D3\n",
					"salmon #FA8072\n"
				]
			}],
			"source": [
				"import matplotlib\n",
				"for name, hex in matplotlib.colors.cnames.items():\n",
				"    print(name, hex)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 20,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"{'Hispanic': 5634, 'Native American/Native Alaskan': 326, 'Black': 19510, 'White': 9147, 'Asian/Pacific Islander': 559}\n"
				]
			}],
			"source": [
				"\n",
				"\n",
				"homicide_race_counts = {}\n",
				"\n",
				"\n",
				"for e,race in enumerate(races_2):\n",
				"    if intents[e] == \"Homicide\":\n",
				"        if race in homicide_race_counts.keys():\n",
				"            homicide_race_counts[race]+=1\n",
				"        else:\n",
				"            homicide_race_counts[race]=1\n",
				"\n",
				"print(homicide_race_counts)\n",
				"\n",
				"\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 21,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"data": {
					"image/png": 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IkSPx/fffS+/N6tWr0bJlSzRt2hQ//PCDNFKtpm2tnP3tt9+Gubk5HBwcMHbsWIwYMULr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\nWWpOrrcAAAAASUVORK5CYII=\n",
					"text/plain": [
						"<matplotlib.figure.Figure at 0x7fa326d4e390>"
					]
				},
				"metadata": {},
				"output_type": "display_data"
			}],
			"source": [
				"homicide_race_counts_name = homicide_race_counts.keys()\n",
				"homicide_race_counts_values = homicide_race_counts.values()\n",
				"homicide_race_counts_range = range(len(homicide_race_counts))\n",
				"\n",
				"plt.bar(homicide_race_counts_range, homicide_race_counts_values, \n",
				"        tick_label=homicide_race_counts_name, align='center',\n",
				"       color=('darkgoldenrod', 'maroon'))\n",
				"plt.xticks(rotation=90)\n",
				"plt.title('Homicide counts absolute grouped by race \\n')\n",
				"plt.xlabel('Race')\n",
				"plt.ylabel('Absolute number \\n')\n",
				"plt.show()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 35,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"data": {
					"image/png": 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DTQsZC8KbWFuT/L//cXLaNOo1bcq+nTtxcXGRO5XwGuIZZAbasGEDX3/9NTdu3OD69etE\nRkZSsmRJDh06xK1bt3Bzc8PPz49vv/32Pz/EY2NjyZMnD7a2tly8ePGlW4GvKmRBQUE0adLktfvF\nxsbi5uaGUqlk3759REZGAtCoUSM2btyY+vzz6dOnb3x/z47ftGlTli9fTnJy8lu1vXXrFg0aNGDS\npEnExcWRkJDw0va4uDgKFy4MmHq3vun89evXJyAgAI1GQ3x8PFu3bgVMz0hLlizJxo0bU9ucOXPm\nje/rTVb9+SdtO3VCO2qUKI6CeVQq1EOGcL5QIWp9+CGPHz+WO5HwGqJAZqB169bRrl27l15r3749\na9euZf/+/Xh6euLt7c369esZOHAg8Py5XosWLdDpdLi7uzN8+HBq166deoy0ens+fvwYe3v7l66K\n0trvyy+/5Pjx43h6erJ69WoqVqwIQKVKlfjll19o0KABXl5eDB48+I3v79nxmzdvTps2bahevTre\n3t5MmzbtlW30ej1dunTB09OTatWqMWDAgP/02h01ahSfffYZPj4+5M+f/43n9/LyomPHjlSpUoVW\nrVpRo0aN1H1Wr17N0qVLqVq1Kh4eHqmdkt61x+z4yZPp1qsX+okTxVyqwrtRKtH068c1d3eq163L\n/fv35U4kvIKYKCCb8Pf35+7du/z0009yR8m2ps+ew48DBmLtkhfN+tVgYyN3JMHCWa1ZQ/49ewjZ\nu5cSJUrIHUf4F1EgBeEtTJj6G1N+mcBU3WTmKeZzKW8Uav9lokgK703511/k2bSJw0FBYn3JLEYU\nSEF4g7nT5/LLj/9jlnEmJSiBBg1DFEO44haLZvUysBJ93YT3o9i5E5eVKzm0Zw8eHh5yxxFSiAIp\nCK+xfMFyBvYfyFTDVMpRLvV1NWp+UAzieqFktCt/F0VSeH///EPelSsJCw2lqJjcPksQBVIQXuGv\ntX/R9auujNePpzKV/7M9mWS+VwzgVlEj2uWLQSn6vAnvR7V+PUX37SMsNNSsschCxhAFUhDScODv\nA7Rp3YahuqH48uqhHAkk8L1iAHdLWKFdskAUSeH9SBI2CxZQ5f59Dv3zD3Z2dnInytHEv2ZB+JcL\nZy7QoV0Heuh6vLY4AjjhxCxpJgVvqrHu3R+MxkxKKWRLCgXaXr04Z2PD1999J3eaHE8USEF4wYM7\nD2hevzkN1Q1pRau3auOMM7Ol2bhdicXq+0EZnFDI9mJjUURFYSuuHmUnCqQgpEhKSOLj5h2Iio3j\nmPE40US/dVsXXJgjzSHf+YdYDXzzJAsWKSoKBg2Cb76B7t1h06bn2zZvhq5dTa8vXpx2+2PH4Ouv\n4auv4MWl3xYvBj8/mDTp+Wv//PPy8XOKyEjs+vWjb+vWrFy0SO40OZ4okIIA6HR6vmrnx+nL0ai5\nzR1K4YcfV7jy1sfIQx7mSfNwPX0H1Y8/Z2BamahU0KcPrFgB8+bBli1w6xaEh0NoKCxbZvr4/PP/\ntjUaYdYsmDIFli+HoCBT28REuHIFli419QS+cQO0Wti9G9KYyD5bCwtD1b8/3bt0YcqECSjF82zZ\nif8DQo4nSRK//jqXgKC/0el3AM7o2UMs3fme7wnm7ReGdsWVucwl94mrKIePyLjQcnB1hTJlTJ/b\n20OxYqaryi1b4IsvTAUUIK0JuC9ehCJFoGBBUyFs1AgOHzZ1anq2RqJabdq2bh20a/f8eDnBzp1Y\njRrF2PHjmfvilbQgK1EghRwvMPAQv/02DqO0BijxwpbpqFnEeCawWrEaibfr8J2f/MxjHi6h51D+\nOiYjIsvvwQO4ehUqVYI7d+DMGdPV5Q8/wAsLVqeKioIX59XNn9/0mr091KgB330H+fKBo6OpmNap\nk3nvRU6ShHLJEuyWLsXf359h/fu/8zzBQvoTBVLI0a5cuUvPnv3R678Bmqexx9eoOcgaaSPjFOPQ\non2r4xagAPOYh/OBUyjGT0jPyPJLToZRo6BfP1OBMxggPh7mz4eePeGF5cneSqdO8Pvv0KuX6RZt\nt26wfbvpOKtXZ8x7yAq0WlRjxpA7JIQDe/bQ8eOP5U4k/IsokEKOFR+fRPfug4iOtsZonPiaPauT\nzGVCpCv0pz8xxLzV8QtRiLnMxWnPUZg6NX1Cy81gMBXHpk2hbl3Ta25uUK+e6fMKFUwLR8fGvtwu\nf3549Oj51/++ogTTs0gw3Yo9cMB0nrt3TR/ZTWwsqh9+oER8PKcOHaJG1apyJxLSIAqkkCMZjUZG\nj15IaOge9PqNgPUbWrih5jo3yIcfftzgxludpwhFmMscnHYchJmz3ju37CZPhuLF4bPPnr9Wpw6E\nhZk+v33bVET//RyyfHlToXvwAHQ62Lv3v2tpLl9u6gWr18Oz+UuUStBoMu79yOH2bVS9euFbujSn\n9u+n+AcfyJ1IeAVRIIUcaceOI8ybNwWDYTEvP3d8HSt0HOYJn9GXvhzl6Fu1KkYxZjMbxy3/wLz5\n7xpZfhERpt6nYWGmZ4Y9epiGbrRsCffvm4rbuHHwc0oP3uhoGDbM9LlKBQMGwE8/mW6hNmpkKrTP\nBAebiqirKzg5QenSpqEfOh2UKpX57zWjnD6Nql8/vvzsM4I2byaXk5NZzSMjI99qMXMhfYip5oQc\n59atKJo06cD168UxGFa+41EWYctA/OjOZ3yGgjd3rLjKVQYwgKTObU0FRshZdu/Gat48Ro4Zwy/f\nf2/2MI6AgAC6dupEqWLFCA4Le2lxdCFjiCtIIUdJTtYwbNgMbty4gMEw8z2O1BMN/7CMVUxRTEWP\n/o0tylCG6UzHfk2AaSyhkDNIEoply7BbvJhVq1czYuBAs4qjJEn8NnEifTt3Zo9GQ9Xbt+naoQNG\nMa1hhhNXkEKOsnLlP/Tt243ExFlA+3Q44h3sFTUoRV4mShNwxvmNLc5znsEMQf1dF+jcOR0yCFmW\nVotqyhRyXb3K9s2bqe3tbVZzvV7Pt506cWTLFnbr9RQHNEAjR0ca9+vHGDFmMkOJAinkGNeu3eeT\nT7px6ZINen1gOh5Ziw21ceEO05lGEYq8sUUEEfzET6j7+r3c4UXIPmJjsfrlF4paWxO0dSsli7z5\n++JF8fHxfFy/PsqICP4yGHix29NDoKaDA5OXLuXzTp3SNbbwnLjFKuQIWq2OCROWcunSMfT6hel8\ndBu0nOQxTelJT05x6o0tKlOZCUzAdt5SCAhI5zyC7O7eRdW7Nz7FixN28KDZxfHWrVtUL1eO0mfO\nsPtfxRGgALAlKYl+fn6cPXs23WILLxMFUsgRdu06xebNK9HrJwKFM+QcEqtJYizD+YVA3nyF6oUX\nYxmD7axFpoHxQvYQEYGqb18+/+QT9gcE4OL85tvuLzp29Cg1KlXC7+FDlhiNrxyA5AlMTE7m6/bt\n0WrfbgILwTyiQArZ3t270UyfPp/EREcgo3uPDkJDIAtYzCzFLAwYXru3Dz78yihsf5sDf/+dwdmE\nDLdnD1a//ML/Roxg1bx52Fi/aXztyzauX0+LevWYm5jIT5L0xr7RfpJE4Tt3GDdy5LtnFl5JPIMU\nsjXThAD+TJnyI2r1OqBBJp35BvaKmlSgCGOlsTjy+i75wQQzjvFoRv4EDRtmUkYh3UgSylWrsN6y\nhSWLF9OlvfkdwCaNHs2MceMI1OupaUa7+0BVe3u27t9PjRo1zD6v8GqiQArZ2okTV/juu8GcO2dE\np9uWyWdPwoaa5OUJ05lGQQq+du/97GcSk9GMGf586jYh69PpUE2divPFi2zdtIm6Pj5mNTcYDPT4\n8kuCN21it17/1tNWvGg9MLJIEcIuX8be3v4djiCkRdxiFbItjUbH779v4+zZg+h002VI4ICW0zyk\nFt/xHRFEvHbvD/mQwQzCdtQEOHIkkzIK7yU+HqvBgyny6BEng4PNLo4JCQk0q1mTqxs3cvQdiyNA\nR8ArOpphgwa94xGEtIgCKWRb+/dHsG/fFiTpS6CcTCmUGNlEAkP5kZ/Yze7X7t2UpgyQvsd2+Gg4\ncSKTMgrv5N49VL17U61wYcIOHqRUsWJmNb99+zY1ypenaFgYewwGcr9nnHnJyWz84w/27dv3nkcS\nnhEFUsiWYmMTWbFiK9evh2MwmLn8Uob4HxrWMZPZLGQhRl49C0pLWtJH6oPtTyPh9OlMzCi8tbNn\nUfXty2etWnEgMJA8uXKZ1fzEiRPUqFSJLvfvs/w1PVXN4Qr8npREt88/J/bfq6kI70QUSCFb2r79\nOCdP7kOSegL55I6Tog1qThDIHoYrhpNM8mv2/JgefIvdD8Pg3LlMzCi80d69qIYN4+eff2bNggXY\n2tiY1Xzzxo008/VlZkICw9+ip6o5WgLN4+P5oVevdDxqziUKpJDtPHz4lMDAg9y4EYbROFjuOP9S\nkWRaclQ6wSd8QhRRae4VTjg7pZ3YS3Yo+30Ply6Z1lj8/nvTKheHDz/f+X//gydPMil/DiZJKFav\nxnbuXJYsXcq4oUPNnnD8t/Hj6fXFF2zX6fg8g2JOU6s5EBhIYGB6zhaVM4kCKWQ7u3eHERFxCEn6\nCnCTO04aegLH0GGHH99ykYsvbU0ggZnMZAIT2MxmOtMZuz6DYN06aNMGFiyAjRtNO4eEQNmypmWi\nhIyj16OaPJlcu3fz986dfNOxo1nNDQYDPbt0YdGvv3JEr6d2BsUEcAKWJCUx4Lvv0GS3tTQzmSiQ\nQrYSHR3Hrl1HuXz5GAbDULnjvEJdIC9Qgnj6MJAf2MfzjhVBBFGf+uQnPwB++NFR+gyrtZvg3j3T\nAsIqlWlh4k2b4Isv5HkbOUVCAqohQyh87x7HDx6kfk1zRilCYmIiLXx9Ob9uHcf0ejJjdcuGgHti\nIgvnzcuEs2VfYhykkK2sXx/M+PETOH/eDb1+hdxxXiMS+Bg4A6zDlm58rujAN9I3zGc+evTc5CbJ\nJPMpn9KMZsxjPpsVmzGWLA79+sGNG+DoCM2by/xesrH791H99BNeFSuye8MGXF3+PSvq6927d49m\nNWvidf8+Sw0GzHta+X4igCbOzly5c4dcZnYiEkzEFaSQbcTGJrJjx1EuXAhFrx8udxwzfI6GUDZI\ngfyq+BUtWq5whckp/61iFXe5Sx968ymfYnc7CnLlgtBQaNAAfvsNfv0Vzp+X+41kL+fPo+rTh3bN\nmxO8bZvZxTHs1Cmqly9Px7t3+SOTiyNAZaC5Xs80sSTWOxMFUsg29u+P4Pr1MBQKb+Qb9/iuPEnm\nGse4RTDBeOCBDTa44EIVqnCVqyhQ0EfqTXN9I1Q9+kDr1hAUBFWqwLBhYhHm9HTgAKqhQ/nxxx9Z\nt3ix2T1VAwMCaFy7Nr8lJDAynXuqmmNMcjJzZ87k4cOHMiWwbKJACtlCcrKGHTtOcvFiGFptP7nj\nvAUp5eNFrqila8RSmk1s4gpXUKPmAhcoTnEAFChoL7XHzZgXu4kzTL1XFQqQJBArOrw/SULx55/Y\nzJzJot9/Z8KwYWb3VJ05ZQp+HToQqNUi93LYJYCvDQbG/vKLzEksk3gGKWQLISEXmDx5BTt2rECv\nvwPpMvQ6o3QG9gPRmFb2Gw1oAQXQI2WfuigIwQ03OtKRT/k0tfUYxtCd7qxSrOKA7VE0JQqZOu50\n7w5162buW8lO9HpUM2bgePo0f61fTyNfX7OaG41G+nXrxu41a9it11Mmg2Ka6zFQwd6eoxERlC5d\nWu44FsVK7gCC8L4kSWLXrlNcu3YSg+E7snZxBFjzFvsEI7GMGPqiUWiQJAlFyo26kZiWNhoqDUWn\nGUfo7dOo1yw3PZcU3k1CAlYjR1JAqyXo0CHKlyxpVvOkpCTaN21K7LFjHNPryZtBMd9FPmCATseI\nwYNZIxbnNou4xSpYvFu3orh+/S6XL59Aknq8uYHF6I6G/ayS1jJBMQEdupe2KlHyi/QLPknu2H3p\nBwkJMuW0cA8eoOrbl8p58hD+DsXx/v371KpYkdxHj7IvixXHZ37Q69n399+cOnVK7igWRRRIweId\nOHCW27cjUChqAOZNGJ311SSZSwRzkf58Tywvz7GpQsUoaRRVE8uYimRSkkw5LdTFi6j69KFNo0Yc\n3r6dfHnymNX8dHg41StUoO3t26wxGLDNoJjvywn4n1rNsP795Y5iUUSBFCxaUpKGQ4fOcePGBbTa\nb+SOk0HaBPidAAAgAElEQVQKopZucB0X/PAjksiXtqpQMUYag0d8Mey6dAe1WqacFubQIVQ//sig\ngQPZsGQJ9nZ2ZjXfsW0bDWvWZGJcHGNk7Kn6tr6TJC6Fh3Ps2DG5o1gMUSAFixYWdo3ExATu3buI\naeB9dmWFjiNE04be9OY4x1/aao0146XxVIgpjG0XP9Gj9XUkCcW6ddhMm8a8hQuZPGIEKpXKrEPM\nnTGDru3aEaDV8nUGxUxvNkAvtZpFM2fKHcViiF6sgkWbMGED+/fv4MCBR2i12+WOk0nmYsuPfMe3\ntKf9S1u0aBmsGMyV/DFoVi0FM8fvZXsGA8pZs3A8cYJN69bRtF49s5objUYG9ujB1pUr2a3XW9xo\n24dABTs7bj54gIuZEx/kROIKUrBYMTEJXL16j0uXzqHVfil3nEzUDw07WcIKflP8hgFD6hYbbJgq\nTaVUlDM23XqAXi9jziwmMRHVzz9T8OpVjhw8aHZxTE5O5uMPP+ToypUct8DiCKZBRU1VKlavWiV3\nFIsgCqRgsSIiIlGrk7h79wLZ+/ZqWj5EzTn2cpIfFD+QwPMerHbYMU2aRvH7Nth07wXGVy/OnGM8\neoRVv354ODoSdugQlcqYN0rx4cOH1HZ3xzEkhAN6fZZZYfRd9ExMZOG0aYibh28mCqRgsYKDzxMV\ndRmVqj7gLHccGRQjWbrBJUmFH99yl7upW+yxZ4Y0gyJ3JGy+7Z2zi+Tly6h696ZF3bqE7tqFW17z\nBmKcjYigevnytLp5k3UGA+Z15cl6GgLqqChCQ0PljpLliQIpWKSYmAQuX75HZOQ1tNpP39wg27JD\nSxhRNKQHPQknPHWLI47MkmZR8KYa6179cmaRDAlBNWQI/fv1I2DFCrN7qu7euZMGPj6MiY1lvAX0\nVH0bSqBncjKLZsyQO0qWJwqkYJHOnr2F0WggMvIs0ETuOLKT+JMkRvIzw9jO885KTjgxR5qD29V4\nrPr/IGPCzKfYuBHrKVOYPWcO03/91eyeqgtmz+bLNm3YqNHQLYMyyuUbo5Et27bx9OlTuaNkaaJA\nChbp2LFLaLVPABdImchb+AkNm5nLAuYyN7XzTi5yMVeaQ74Lj7AaMFjmjJnAYEA5cyaOmzYRuGUL\nfbp2RaF4+2s/SZIY1Ls3kwYPJlivp2EGRpVLPuAjpZI/Vq6UO0qWJgpkDuHs/PIzupUrV/L9998D\nsGjRIlavXp0pOUaNGsXevXvf6xg6nZ4LF+4SFXUdo7FpOiXLLlqiJpwdigP8xFCSMM2sk5vczJPm\n4XrmDqohQ2XOmIGSk1ENH47bxYuEHjhAiwYNzGquVqtp27gxh5Ys4YReT4UMipkV9ExKEp113kAU\nyBzidb9B9+zZky5dumRKjtGjR9OoUaP3OsatW1EYDAauXLmCXt8snZJlJ2VIlm5yjiS+owcPeACA\nK67MZS65T15DOex/MmfMAFFRWPXtSyVbW8IPH8ajnHkDMaKioqjj4YHVwYMc1OvJn0Exs4r6ADEx\nHDp0SO4oWZYokAKjR49m+vTpAMyePRt3d3eqVq1K586dU7d//fXX+Pr6Ur58eZYsWQJAYmIiTZo0\noXr16nh6ehIYGAhAZGQklSpVokePHnh4eNCiRQs0Gg0A3bp1Y/PmzQAcP36cOnXqULVqVWrVqkVi\nYuJb5b18+S4Ggz5leEd2vAGWHhzRcJaHVOc7vuMc5wDIT37mMQ+XIxdQjhotc8Z0dPUqqt69aVar\nFkf+/psCZvZUvXD+PNXLlaPJ9etsMBiwz6CYWYkC6JqYyFqx0PYriQKZQyQlJeHt7Y23tzdeXl6M\nGjUqzf0mT55MeHg44eHhLFy4MPX1iIgI9u/fT0hICGPGjOHBgwfY2dkREBDAiRMn2Lt3L4MHP3++\ndfXqVfr378/Zs2dxcXFh06ZNL51Hp9PRqVMn5syZQ3h4OHv27MHe/u1+LJ04cRW1OgorqxKQJddO\nyCqUGAgggcEMZgh/8zcABSjAPObifDAcxbgJMmdMB6GhqAYNonfPngSuWoWDmT1V9/zzD/W8vRkR\nE8NkScpRPxTbSBLbAgPFbdZXyEnfCzmag4MDp06d4tSpU4SFhTF6dNpXD56ennTu3Bl/f/+Xev19\n8skn2NjYkDdvXho1asSxY8eQJImff/4ZT09PmjRpwr1793j06BEAJUuWpHLlygBUq1aNmzdvvnSe\nS5cuUbhwYby9vQFwcnJ6q5Xbk5I03LjxkJiYuxgM5i1om3P9ioY1TGcmi1mMESOFKMQ85uIUdBSm\nTJE74DtTbN6M9aRJzJg5k9njxpndU3Xx/Pl0+ugj1mk0fJtBGbOyioC1Ws3p06fljpIliQIpvGT7\n9u3069ePU6dO4ePjgzFl7NyLzzAlSUKhUODv7090dDRhYWGEhYXh5uaGOmUlCVvb5wv/qFQq9GlM\nefYuv7VGRj5K+fM2en0ts9vnXO3QcJy/2MUv/IIaNR/wAXOZg9POYJhuYWPiDAaUc+bgsH49f23e\nTP/u3c3uqfpjv36MHTCAg3o9jTMwalamAD7W6dgqFlJOkyiQOcTbFqNbt27RoEEDJk2aRFxcHAkp\ni/Bu2bIFrVZLdHQ0Bw4cwMfHh9jYWNzc3FAqlezbt4/IyOfLML3pfOXLl+fBgwecPHkSgISEhNRi\n/DrPCuTdu9cBn7d6T8Iz7qi5ThhR9KIXj3lMMYoxm1k4bg2CufPkDvh2kpOxGjGC/GfPErJvH60a\nm1feNBoN7Zs1I2jRIk7o9VTKoJiW4mOtlq1r18odI0uykjuAkDne5rdrvV5Ply5diIuLQ5IkBgwY\nQK5cuQCoUqUKH374IdHR0YwcOZKCBQvy5Zdf8vHHH+Pp6Un16tWpWLHiG8/37HVra2vWrVtHv379\nSE5OxsHBgT179uDg4PDajOfP38baGpKSojDdIBLMkxsNl7lDc/zwYypTKUc5ZjKTAZsGkGRjDT16\nyB3y1aKjsfr5Z8q5ubEnOJhC+c3ra/r48WNa+vpS+Pp1gg0GXv/dljPUA67cvMnDhw8pUKCA3HGy\nFLHclfBGo0ePxtnZmUGDBsmaQ5Ik+vRZQFLSA9atC0SjCX9zI+E1fsSWuQxnGPWpzyUu8QODSP76\nM+iWBeeOuX4d1dChNG7UiM0rVuD4lp26nrl06RLNa9fms9hYphiN4vbZCz5xdqbTokV88cUXckfJ\nUsT3iGAxnj5NQKPR8fjxHQwGL7njZANT0bCECUzkD8UflKMcvzEVuz82gL+/3OFeduwYqoED6enn\nx3Z/f7OL496gIOpUrcrPT5/ymyiO/9E4Pp6gbdvkjpHliFuswhu9akhIZnvwwDRv5MOHD9Hr32+y\nAeGZL9FQkT+lRtxQ3GCYNIwpTOanJT+htrKCzz+XOyCKwECsli5lyrRpDPj2W7M64wAs//13hvTp\nw596PWJaibQ1Bmb884/cMbIc8YuUYDHu3o1GkiAqKhooncln98O03GyVf70+B9Oz0MrAz69ouwuo\nAJQDJr/w+s+AJ/DNC6/5A7PfP65ZvFFzlVDpGn3pSxGKMJGJ2C5cAX/9lclZXmA0opw/H3t/fzZu\n3MjA774zu6fqsB9+YESfPhwQxfG1KgHqhASuX78ud5QsRRRIwWJERkZhb29NTMwjoFQmn70bsPtf\nr+0HtgIRKR9D0mhnBPqltD0H/AlcBOKAMOA0YJ2yTQ2sAPqmd/i3kA8N17lJQfzwIxe5GMdYbGcv\nhq1bMz+OWo1q5EjyhoURvHcvbZqaN+euVqulY8uW7Jw7l+N6PR4ZFDO7UACNFAqCgoLkjpKliAIp\nWIy7d6Oxs7MhIeE+UDKTz14XyPOv1xZgugp89qQirXXmjwFlMa04Yg10ArZg+qenS9knKWXbb0B/\nwLzB7unHCj2HeMrn9KUvOnSM5ldsp8+Dv//OvBhPnmD1/feUNRgICw7Gy93dzOZPqOfpSfKePRzW\n6ymUQTGzmwZJSYSI26wvEQVSsAiSJPHgwVMMhiSUSgfA+Y1tMt5l4CBQC9OcsCfS2OcuUPSFr4uk\nvOYEtAS8gA+AXJiKaZsMzPu2FqBmFqMZw03FTUYwAtuJ0+E9V2F5KzduoOrViwaVK3M8KIgPzBx2\ncOXKFaqVK0fty5fZYjDgmEExsyMP4JyYUeclokAKFkGt1qJW64iLi0alKiF3nBR64ClwBJgCdDSz\n/Y+YbrNOAUYAY4ClwOeA3HOk9kDDHlZI/hxSHOJHfsR27FTIyJUfTp5ENWAA3b/+ml3r1uH0hjGx\n/3Zg3z58q1RhSHQ0M41G2a7DLVUl4EJk5FtN2JFTiAIpWISnTxNQKhXExj5GkkrIHSdFUeDTlM99\nMP1ziv7XPh8At174+k7Kay8KS/mzHLABWAdcBa6lZ9h3UAc1FznAGTYpNtOTHtiOmgChoel/qm3b\nsBo9momTJ7No6lSsrMzrYL9y6VI+bd6cP9RqWZ7gZge5gVwqFbdv35Y7SpYhCqRgEZ4+TUSSJJKS\n4jEYCsqUQkr5eKYt8Oy242VMzxT/vbqID6ZiFwlogbX89zbqSGBsSvtnv70rIWWxY3kVRi3d5Krk\nyBrW0F76FNtfxsCJtG4nvwOjEeWiRdivXMnadev4sXdvs3uqjhgyhGG9erFXp6Nl+qTKsdytrTl3\n7pzcMbIMUSAFi/D0aQJGo0RycgIGQ1qdYTJaZ8AXUyEsBiwHugPXMQ3x6Az8kbLvfaB1yucqYC7Q\nDHDH1EnnxSnytmAqogUBF0zDPqoAmpTjZgU26DhGNB/xFwH4SrWx/WkEhL/nTEYaDapff8X1+HEO\n7dtH+5bmlTedTscXH39MwKxZnNDr8Xy/NAJQKTmZ8+fPyx0jyxATBQgWISYmAZVKSUKCGnCVIcGa\nV7y+Ko3XCgEvzkrSArj0ivafpHw8MzXlI+uRWEky3oTwM4WlQtwbNBzNrMlQ+R0K+dOnWA0bRslc\nuQg6dIiihczra/r06VNa1a1LrkuXCDUYcDI/gZAGd62WkGPH5I6RZYgrSMEixMUlY2WlJDExCXkK\npGAyAA3buE8UekmL7cChcPGieYeIjETVqxd1K1bk5L59ZhfHq1evUr1cObwuXmS7KI7pyh3Rk/VF\nokAKFiEuLgkrKxWJiYmIAim3xqg5i7UiHxpjMnZ9B8PVq2/X9NQpVP3707VzZ/7esAFnR/MGYhw+\ndAjfKlXoHx3NPNFTNd0968kq1rAwEQVSsAhxcUlYW6tQqxP574B9IfMVRy3dxEZRGbUxCbteA+HG\njdc32bEDq19/Zez48SyZPh1rM3uq+v/xBx83bsyy5GQGih/gGeJZT9Zbt269cd+cQBRIwSLExydj\nZWWFVptM1pgkQAA7tNIZlHyGxpCE3XffQ1pDBIxGlL//jv2yZaxZs4Zh/fub3VP112HDGOLnR5BO\nl9r9ScgY7tbWoqNOClEgBYuQkKDG2lqVcutH3FjLSoxsQGIceoMau+594P795xu1WlRjx5InJIT9\ne/bQobV55U2n0/FVu3Zs+O03juv1iEXOMl7F5GQuXLggd4wsQRRIwSIkJ2tRqZQpBVJ822Y9w9Gz\nGYNeh03XHvDwIcTEYDVwICXi4zkZHEyNqlXNOmJMTAwNq1Xj0bZtHNHrKZJByYWXuWm1RD96JHeM\nLEH8pBEsgtFoTLktZ0R822ZVH6PjNJLeiLJrN5Q9e+Jbtiyn9u+n+Af/nj3o9W7cuIFP+fK4nzvH\nDoNB3FTPRLmA2MeP5Y6RJYifNIJFkCQJhYKUK0jzFswVMoMRWAeK7ugkDUadjg6ffMKeTZvI5WTe\nQIzQw4ep6eFBr6goFhqNYrB2JnMB4p48kTtGliAKpGARnnU7F7dYsxIdMB9UVcHKtJSXjeNpPBs1\nYOM/u/lz0SKze6quW7OGVg0b8ntSEoMlSfwqJINcQKwokICYSUewEEbjs2794gpSXgnAbBTWy5Gk\na6CXsLa1pfAHlcmVtyAYlMyfMQnfKlXMOqokSUwYOZI5kybxt15P9YwJL7yFXEBcbKzcMbIEUSAF\niyBJoFAoUCqtMc1TKmSeR8BUFDZ/IhnuggEwKshXqBiuBQpjl9ueyhUr8kmzFrRo+CHOZt5S1ev1\nfNupE0e2bOGYXk+xDHkPwttyAeLi4+WOkSWIAilYhGe3WG1s7IE4ecPkCNeBSShsNyPposEIkhYc\nXF3Ik78wzgVcKFumBK0bt6B146YULlD4nc4SHx/Px/Xro4yI4JjBQK70fRPCO8gFxIoCCYgCKVgI\nKyvTEA87OwdA3P7JGGHAZBR2O5A08SCBpAGVnRUubgXIXTAvJcsUpaFvfdo2a0WFUhVQqd59TOqt\nW7doWrMm9R49YoHRiHX6vRHhPeQC4pKywlJr8hMFUrAI9va26PVG7OzEFWT6CgKmgd0+0KvByVQU\nUShwyOdMnoJulChXjGqenrRr1gZvd29yOb3/dd6xo0dp07gxg5KS+FF0xslSXIDY5GS5Y2QJokAK\nFsHBwQaDwYCdnR3iCvJ9GIGNoJgNNscAHRRXgF5CGa1CoVfh+kE+ipT9AA/3SrSs3xxfb1+KFS5m\n1vRwr7Nx/Xp6dunCYp2O9ulyRCE92WJ6pKHRaLC1tZU7jqxEgRQsgoODHXFxSTg42CGuIM2lB5aA\nahGozoC1ZFq2wUZC+VCFdNOIS4F8FK5aiAoe5ahRxYcmvk3wKOeBrU36/YCUJInJY8YwY9w4dur1\n1Ei3IwvpSQG42NgQFxdH/vz55Y4jK1EgBYvg6GjLkyfxKQVSjNF6s0SeD8e4Ck5AZQkKAHcVKMIV\n2Dk6UKx0cdybVqDoB0VpUqcJNavUJL9r+v5QfPToEatWruT3GTOwioriqF5PiXQ9g5DerJVKtFqt\n3DFkJwqkYBEcHe0wGIy4uLhibR2JTid3oqwoCvgNhc0aJMMdFHmVSJWNUAa4D8oTKhQnFBQrVxz3\nzyvhVig/NarUoIFPA8qWKPteHW7+Ta/Xs2vXLpbNns2+gwdpA/yu0VAXMYrVEsTrdOTKJfoUiwIp\nWARnZ3v0egMuLnlRKg/LHScLuYlpOMYmJP1jFIWUSFWMUAGkGCOKEwoIVuBWxA332pX4oOQHFCtc\njGZ1m+FV0Qtnp/Sd5fTy5cssW7SIP5YupYTRSPf4eFaAGL5hQYxAkl6Po5mLWWdHokAKFiF/fhe0\nWj0uLvmQpDtyx5FZOKnDMXRxKIqnFMVyIBmMEA6q5SpslXZU9vKgeL3i5Mmbhw9rfIivly9FCxVN\ntw43AAkJCWzYsIFlM2dy5coVuuj1BOl0VEy3MwiZKQFwsLFBqRRTOooCKVgEV1fTlY6LSz50unvk\nvFU99gLTUNjtRTKoUZR5fvtUUhnhMqj+UsFtKFu5LKVblyZfoXy4l3WniW8TKpWplO4dbkJDQ1k6\ndy6bAwKop1QyJDGRj0CMZ7RwcUAue3u5Y2QJokAKFsHFxQGFQoG1tQ3W1o5otQ+BQnLHykBGYHPK\ncIwjgA5FRSWShxFKgGRlhEeg2KtAGaEkr1teKnpUpEjrIuTPl5+mvk2pUaUG+VzzpWuqBw8e8MeK\nFSybOxdiYuiWnMx5ozFb/5/IaeIBZwcHuWNkCaJAChYhd+7nz0OcnNx48iSS7Fcg9cAyUC0E1WnT\ncAwPoJIERUFSGkENnAbrMGtUcSoqVa1Esc7FyJs/LzU9a1K/en3KFC+Trh1udDodO3bsYOmsWRwK\nCaGdQsEytZraiA432VE8mL1EWXYlCqRgEVxcHDEYjEiSRN68bjx5cgWoJXesdJAEzAar5aC4Ao6Y\nhmNUAgpiqkASEAlWYVZIlySKlilKWd+yFCxekOKFi9O8XnOqVqiKk2P6/lC7cOECyxYuZNWKFZSV\nJLrHx7MG04gRIfuKA7MnnM+uRIEULIKtrTWOjrbo9QaKFCnEtWvhGI1fyR0L2AUMxHRL1A8Y+q/t\nMUB34BpgDywD3IAxoFhkmgE8lwJ8JKgI/APUAJyBOFCEK7A6bYW9lT3unu4UrVcUV1dXGtZsiK+3\nLx8U+CBdO9zExcWxfv16ls2cyY3r1/lap+OAXk/5dDuDkNXFA7lcXOSOkSWIAilYjAIF8hATk0DB\ngsWwtj6ORvZVr4xAP0zzmRYGfIBPgAov7DMB8AJmAD+DoioodeCkgBISNAACJagHXMI0kP822ITb\nYLxtpFzlcpRsVZJ8BfNRuXxlGtduTKXSlbCxsUm3dyFJEsHBwSydM4eArVv5UKVieGIiLRA/IHKi\nOMA5d265Y2QJ4vtfsBilSxfkwIGzFCxYDINhGfIvnnwMKAsUT/m6E7CF5wXyNOAPNjFgGGkajvHQ\nCN2AG5KpE649pj/vA9tBpVeRv1B+KrhXSO1w07xuc6p7VCdvnrzpmv7u3bv8sXw5y+bNwzohge6J\niUyWJAqk61kESxOPKJDPiAIpWIxSpQqyZ89pnJ3zoVDogQfI21HnLlD0ha+LAH8BrcFuL+iTTUsj\n5AXagxRlNN1h1QGVgQ3AQbCytoKVUKR4EbwaeOGaz5XaVWtT36c+pYuVTtfxaFqtlm3btrFs9mwO\nHznCZ8AqjYaaiA43gkkckCtf+vZ+tlSiQAoWo2DB3CgUChQKBfnyleL+/dPI35NVwjQcYxaoDoNk\nQOGhQPKQoCRgAHYCyzHdPi0I3APrI9YY7xopXq44xUsV52L4RTp/15mIwxE8efwE94/dKVuibLql\nPHfuHMsWLmT1ypVUAPzi41mHqU+QILzopr09XiVLyh0jSxAFUrAYBQrkQZJMPVmLFPmA+/fDgBYy\nJNEDy0E5DaRLYL8EPBRgkCA3SPWk57taAW2BWFOHGylCItfRXFT2rkyRekVwzevK7YjbzJkxhydR\nT6jQrgKfffYZ7dq1Y9euXe+VMjY2lrV//smyWbO4ExlJV52OYL2e9Cu7QnZ0xcaGjmXFdwmIAilY\nEAcHW1xdnVGrdZQoUZrTpw+g1Q7LpLMnAXPBehlwGRwADwkiMD1TdJbgd14eeaIHIsD6rDXch3wF\n8qEqqaJx+8Z4VvSkUa1GWButGXNxDO3btmf27Nk4ODggSRJqtfqdUhqNRg4ePMiyuXMJ3L6dxkol\no5KSaIb4xy68nSs6HWVFgQTEvxnBwpQqVZDz529RvHgFDIblmO5hpt+g+JdFY1odwx/JeBtFnpQ5\nTysCzx7RlABWY7rT6gXkB/aC4rYC1SMVLnlcSIpJwtrOGidHJyZPnUyDWg1wze0KQKdOnRg/fjwA\nX3zxBW3btmXSpEmMHTvWrKR37txh5bJlLJs/H4fERLonJjJNksjZq/kJ5koCorVaihYt+sZ9cwJR\nIAWL4u5ejBMnrpA/vxuOjvmIiwsHqqXjGSKByShsNyLpo1AUfGF1DBfjf3cvm/KRDESAzWIbVEkq\n3Ku6U6xZMVzzuuLr7Uu96vUoVbTUfzrcrF27NvXz/Pnzc/jw269UotFoCAwMZNmsWRw9cYKOwFqN\nhuqIDjfCu7kKlCxYUExUnkIUSMGilCpVMHVgfOnS5QgL28f7F8gITKtjbDWtjlEspSiWB8khjaL4\njBG4Adbh1hivGClRrgSl65WmQNEClC5emmZ1muFZwRMH+/Sd1/LMmTMsW7AA/9WrqQx0T0hgE6a7\nvoLwPi4B5cTt1VSiQAoWpXBhV6ysrNDrDZQpU55z5/ag1Q55hyMdAqagsAtC0ie/vDqG7WuKIkBM\nygw34VY4OTiZZrhpWJQ8rnloXKsxtarWopBboXSd4ebp06f8uWYNy2bP5uGdO3TVaDhqMFAq3c4g\nCBChVFKldm25Y2QZokAKFsXKSkWlSkW4du2+mc8hjUAgKGaCTQigQ1Hh+XAMyeoNRVEHXDTNcCPd\nl6hQpQIl2pYgb8G8eFX0omHNhlQoVQFr6/Rb7MloNLJv3z6WzZ3L9l27aKZUMj4piSZk3FNXIWc7\n7eTEV15ecsfIMkSBFCxO1aqlOH36BnnzuuHklJ/Y2GNAWr/16oGVoFoAqnCwShmjWEmCYiAppTTa\nvEAC7oMqXAURUKBIAcpXLs8HbT+gkFshmtVpRjWPauRxyZOu7y8yMpIVS5awfOFCXNRquicmMluS\nSN95dAThv84YDHh6esodI8sQBVKwOC8+h3R3r8KRIwEYjc8KZDIwD6yWgCJlOMaz1TEKgaR4Q1EE\nU1e+M6arRSuNlanDTVdTh5s61epQt1pdShYpma4dGdRqNQEBASybNYuTYWF8DmzWaPBCdLgRMkcs\nEKXTUaqUuHH/jCiQgsUpXNgVGxsrdDo9FSt6cfz4SoxGKxQ2q5GMt1DkTulkU4nnwzHexMj/27vv\nuKrr/v/jj7M4IEOUkSgghIqAgCw1txKgmZkLy4aD0rT6XlZW2lVXl9llP68rK5tmy7w0LsxLbaLm\nyFEO0srMRQ7cypApcNb79wd4LgflCD2M1/12Ozc5n/MZz3PU8+L9+bw/7zccqO5w85uNm9vfTEif\nEHz9fWkb3JakrklEhUbh4ly7M63/+OOPfPjOO6R/8gkdNRrSSkv5jKohWoW4kX4BIoKDa3Uu0fpO\nCqSod/R6HXFxbdi27TdatboZZTiOptn/Q8VW3aNY4+0Yv6fgf1NKubu7Ex4ZTkBiAF5eXvTt0pcu\n0VUdbmpTQUEBnyxcyAezZ1Nw4gSjKyr4wWYjqFaPIsTV2aTRkNC9u6Nj1ClSIEW9FB/fhu++282x\nU8fxvSmQ48H7rnz+ZDOwq+oUKqehfXR7goYE4XWTFzHh/+two9fX3n8Pq9XK6tWr+fCNN8j85hv6\n63T86+xZ+lI1mYcQjrbC3Z3/u+MOR8eoUzRKqSu4KCNE3VJWVkHKXfdSaj6NQVn5eUcW5ofMv7+B\nAo6D7kcd/Ap+gX60i2hHy6CWtPJrRXK3ZGIjYvH0qN1pfg4ePMi8999n3rvv4m0yMaakhJFA81o9\nihB/ThnQwsmJ43l5uLu7OzpOnXHZX161Wi1PPvmk/fmsWbN44YUX/nCbdevWsWnTJvvzd999lwUL\nFqbI/xwAACAASURBVPyJmBfq2LEjI0eOrLX91WTcuHHs2bOn1vc7c+ZM0tPTmTZtGq6uruTl5dlf\nu5J/mC+99NIFz7vX4imRZcuWodVq2bdvn31ZTk4OkZGR17S/6/kfzdXVmUGDutC9axT9b0tEe1YL\nuTWsWAZsAqc5Trj+15U4jziGjB5C/2H9GXXfKF6Y9AIvPfESfW/pW2vFsby8nE8++YTETp1ICA+n\n4OWX+Sw/n20lJTyCFEdR96wD4iIipDhe5LIF0mg0smTJEgoKCq54p99++y3ff/+9/fn48eO59957\nry3hRfbs2YOzszObN2+mvLy8VvZ5MZvNxty5c2nfvv3lV75KK1asIDk5GagaWmzWrFn2167kxvIZ\nM2Zc8Hzjxo21lu0///kPt99+O+np6Rcsv9Yb3mvzRvma9EzoQVl5GVqtlqjYKDQ7qo9nA/aBYZEB\n/Rt62p1uR9/Evgx9YCiDhg1i8kOTmf3sbO6/836CA4JrJadSih9++IGJY8fi7+3NvPHjGZ+VxdGK\nCt4wmej4p48gxPWz3MmJlKFDHR2jzrlsgdTr9YwbN45XXnnlkte+/PJLunTpQlxcHMnJyeTm5pKT\nk8OcOXN47bXXiI2N5bvvvmPatGm88sor7N27l86dO9u3z8nJISoqCoBt27bRu3dvEhIS6N+/P6dO\nnaoxT3p6OiNHjiQlJYVly5bZl/fp04fHH3+chIQEwsPDycrKYsiQIYSGhvLcc8/Z11u4cCGdO3cm\nNjaWCRMmcO4Ms7u7O5MnTyYmJoZNmzbRp08ftm/fDsDy5cuJi4sjJiaGpKQkALKysujatStxcXF0\n796d7OxsAD7++GOGDh1K//79CQ0N5emnn7Yfu6SkBLPZjJdX1R1tY8aMISMjg8LCQgDOP9s9ePBg\nEhISiIyM5P333wdg6tSplJeXExsby3333WfPDVUDXWdmZtq3HzNmDEuWLMFms/HUU0/RuXNnOnbs\nyHvvvVfj51pWVsaWLVt46623Lhgf9Hw5OTn07NmT+Ph44uPj2bx5MwAnT56kV69exMbGEhUVZR9P\n9Nz7ycvLo2vXrmRmZlJWVsatt95KfHw80dHRfP755/Z9h4eHM27cODp06EC/fv2orKysMcc5EW0i\n0Gq0WK1WYjvFot+hR7tai+E1A14bvOgS3IXhDw0neXAyE8ZOYObkmTwz4Rk6RXWqtd6oeXl5zH7t\nNTqGhDC8Vy9azJ/Pj2fPsrK0lFTAuVaOIsT1tdLJiZT+/R0do+5Rl+Hu7q5KSkpUUFCQKi4uVi+/\n/LKaNm2aUkqpwsJC+3rvv/++mjx5slJKqb///e9q1qxZ9tfOfx4TE6MOHTqklFJq5syZ6h//+Icy\nm82qa9euKi8vTymlVEZGhho7dmyNeUJDQ9Xx48fV6tWr1cCBA+3Le/furaZMmaKUUmr27NnKz89P\nnTp1SlVWVip/f39VUFCgdu/erQYOHKgsFotSSqmJEyeqf//730oppTQajVq8ePEF+9u2bZvKzc1V\nAQEBKicnRyml1JkzZ5RSSpWUlCir1aqUUmrVqlVq6NChSiml5s2bp0JCQlRJSYmqqKhQrVu3VkeP\nHlVKKbVkyRL1/PPPX/CZTJ8+3b7Mzc3NfvxzxykvL1cdOnRQBQUF9r+Pi/9+lFJq6dKlatSoUUop\npUwmkwoMDFQVFRVq7ty56h//+IdSSqnKykoVHx9v//zPt3DhQjV+/HillFI9e/ZU27dvV0opdejQ\nIRUZGamUUurs2bOqsrJSKaVUdna2io+PV0opNWvWLDVjxgyllFI2m02Vlpbas506dUp17txZrV69\nWimllNVqVSUlJUoppfLy8lSbNm3sxzEYDGrHjh1KKaVSU1PVwoULL8l5sdc/fl1NeH6Cev7151WH\nWzqo6G7RatDYQWrMlDHqzQVvql+zf1Vms/my+7kaFotFZWZmqmH9+6umRqMa6eKiVoOyglLykEc9\nexwC5evubv8+E/9zRd303NzcGDVqFLNnz8bF5X+/eR85coTU1FROnDiB2Wwm+ApmoR4+fDgZGRk8\n9dRTZGRksGjRIvbu3cvOnTtJSkpCKYXNZqNly5aXbLtt2zZ8fHzw8/PD19eX0aNHU1hYiKdn1bWj\nO6p7YEVGRhIZGYmvry8AISEhHDlyhA0bNrB9+3YSEhJQqmrOvRYtWgCg0+kYMmTIJcfcvHkzvXr1\nIjAwEMB+rMLCQu6//36ys7PRaDRYLBb7NomJibi5uQEQHh5OTk4OrVq1Yvny5YwdO/aC/T/66KPE\nxMQwefKF44m+9tpr9hby0aNHyc7OplOnTr/7ufbv359JkyZhNpvJzMykZ8+eGI1GVq5cyS+//MKn\nn34KQHFxMdnZ2bRu3fqC7dPT03nsscfsf0effPIJMRcNOWU2mxk/fjw//fQTOp3O3mpOSEggLS0N\ns9nMoEGD7CNxmEwmbr31Vt566y169OgBVJ2+njp1KuvXr0er1XL8+HFOnz4NQHBwsP16Z1xcHIcO\nHfrd93tOr069yNqZhdlsJq5nHH4+fiR3TyY2PJam7k0vu/3V2L9/Px/Nncu8997Dz2JhbEkJ7wG1\n261HiBtrBZCUmCgzeNTgivux/+UvfyE2NpYxY8bYlz366KNMnjyZAQMGsG7dOqZNm3bZ/YwYMYLh\nw4czePBgtFotISEh7Ny5kw4dOlx2qp/09HT27NnDzTffjFKK4uJi/vvf/5KWlgZUXS+Fqo5F534G\n7AVMKcWoUaPs8++dz8XF5XevRSmlLln23HPP0bdvX5YsWUJOTg59+vSxv3b+sXU6nb14bt26lTlz\n5lywn6ZNmzJy5Ejeeust+/HXrVvHmjVr2LJlC0ajkT59+tgn0K0py7lj9u7dm+XLl5ORkcHdd99t\nX/+NN96wnxquyZkzZ1izZg07d+5Eo9FgtVrRaDT861//umC9V199lRYtWrBjxw6sVqv9l6UePXqw\nfv16vvrqK0aPHs0TTzzBvffei16vJy4ujuXLl9sL5MKFC8nLy+PHH39Eq9USHBxsf28Xf25XMmlw\nWEgYEW0j8L/Jn26x3WjdqnWtXvs8e/Ysixcv5sPZs/l11y7usdn42mQiqtaOIIRjrXBz484aGgfi\nCq5BnvtCbtasGampqXzwwQf214qLi+0tvY8//ti+3N3dneLi4hr3d/PNN6PT6Zg+fTojRowAIDQ0\nlNzcXPs1LYvFwq5duy7JsWjRInbu3MmBAwc4ePAgy5Yt45NPPrniN5uYmMjixYvJza3q7njmzBmO\nHDlywfu8WJcuXdiwYQM5OTn2bc6991atWgHw0UcfXfbYu3btIiwsrMYv78cee4x3333XXkiLiopo\n1qwZRqORPXv22D8XACcnpwtaq+fnTk1N5aOPPmLjxo3069cPgJSUFN5++237NtnZ2Zd0bvr000+5\n//77OXjwIAcOHCAnJ4fg4OBLOgAVFRXh51d10/z8+fOxWq0AHD58GF9fX9LS0njggQfs1241Gg0f\nfvghe/bssRfboqIifH190Wq1rF271v65XvxerpTBYODpB5/mnjvuIcg/qNY63GzZsoXxo0bh7+1N\n+sMP88j27RytqOA1KY6iATEDq00me8dBcaHLFsjzv3CeeOIJ8vPz7cuef/55hg0bRkJCAj4+/5u7\nfODAgSxdutTeSefiL60RI0awcOFCUlNTgaovucWLF/P000/TsWNHe0eZ823YsAF/f39uuukm+7Ke\nPXuye/duTp069YdfjOdeCwsL48UXXyQ5OZno6GiSk5M5ceLEJe/z/Ofe3t7MnTuXwYMHExMTw113\n3QXAk08+yZQpU4iLi8Nm+/2RW87tJzMz0160Lubl5cXgwYMxmUwA9OvXD7PZTEREBM888wy3nDf9\nzLhx44iKirJ30jk/d3JyMuvXrycpKcl+k/sDDzxAeHg4sbGxREZG8tBDD11QYAEyMjIYPHjwBcuG\nDBlySW/WiRMnMm/ePGJiYti3b5/9NPK3335LdHQ0sbGxLFq0iEmTJtmzaTQa0tPTWbNmDXPmzOHe\ne+8lKyuL6OhoFixYQFhY2CWflaOcPn2aV15+mcjgYO5JTCRwwQJ2lJeTWVrKMMB42T0IUb9sAYIv\n+l4V/yMDBdwgKSkpzJ8/X/4h1jEWi4UVK1bwwezZrFm/nju0WtLKy+mBjHAjGr7HnZxoMmkSL86c\n6egodZIUSNEo7du3j4/mzuXjDz4g0GplbEkJI4Da7dYjRN1VCQQ4O7Np505CQkIcHadOkrFYRaNR\nWlpq73Czd88e7rVa+cZsJsLRwYRwgKVAdHS0FMc/IAVSNGhKKTZv3swHb77Jf5cupZtWy2NlZQwA\nnBwdTggHmuvmxoQnnnB0jDpNTrGKBm3Dhg0MSExkqtXKKJuNS++uFaLx2Qf0cHfnSF4eTk7yq+Lv\nkRakaLCK8/KwnjoFNhtjbDZaODqQEHXEewYDo9PSpDhehrQgRYN0eNculvzzn2i0WpZt3kzinj08\nK//UhaASCHRxYePPP9O2bVtHx6nTpCe7aJDcm1dNKuUdEMCt3bszR6/H6uBMQtQFnwEdOnSQ4ngF\npECKBqlZixb4h4VRkp9PcMuWuHp4sNLRoYSoA+a6uTFOOudcESmQosGKTUmhorQUgJ5duzLbYHBw\nIiEc6zdgh0bDnXfe6ego9YIUSNFgBUZE4Ozmhqmigm6RkWzT6djm6FBCONBcg4FRY8ZcMDGA+H1S\nIEWDpTcY6JiURNHp0zgZDNzeuzfPSCtSNFJ5wIc6HRP+8hdHR6k3pECKBi2iR4+q6c7MZnrHxfGz\nwcCmy28mRIMzQ68n9e67ufnmmx0dpd6QAikaNPfmzYlJTqbw5EkMej0D+/ZlqrQiRSNzBPjYYOC5\nGubCFb9PCqRo8GJSUqpakSYTPTp25DejkbWODiXEDfS8kxPjH37YPp+ruDJSIEWD5+bpSdxtt3Hm\n5En0Oh13JCUx1ckJGTZANAZ7gC8MBp76618dHaXekQIpGoWOSUnoDQbMlZV07dCBk87OrHB0KCFu\ngGecnXnymWfw9PR0dJR6RwqkaBSauLuTMHAghSdPotVqGZSczBSDQVqR9VgQEA3EAJ2ql00D/IHY\n6sfyq9gWYEr18tHnLVsIvF47kW+4LGCriwuPTJrk6Cj1khRI0WhE9emDwWjEVFFBQng4JW5ufObo\nUOKaaYFvgR+BrectfxzYXv3odxXbFlc//xkwAL8CFcA84OHaDH4DPe3iwt9mzKBJkyaOjlIvSYEU\njYazqyud77yzqhWp0TA4JYWpBoOM0VpPKcD2O8uvZVstYK7++SxVRfJl4FFAd40ZHWkVcNTTkzFp\naY6OUm9JgRSNSodevXB2daWyvJyYdu3QeXnxukbj6FjiGmiAJCABeO+85W8CHYEHgKKr2NYN6E/V\naddWgAdVrcs7ajv4DaCoaj1Of/VVDHJb0zWT6a5Eo/PzmjWsnT8f36AgTubnM33OHLIsFto4Opi4\nKicAPyCXqmL3JhAKeFNVAJ+tXueDK9y2+0XrPEjVqdVtwEqqrk0+U9tv4jp5H3i3fXu2/PorWq20\ng66VfHKi0Qnv1g13Ly/Kiopo4eXFgN69GWUw1Hi6TtRd5+7o8wEGU9Xa86GqOEJVgcu6im3P92P1\nn+2AT4EMqgb63v+nU19/x4Cpzs58kJEhxfFPkk9PNDoGo5HkBx6gtKAAm9VKv1tuoaBZM96WU631\nxlmgtPrnMqpaeB2Ak+ets6R62ZVue76/AdOpuiZ57hcnbfW2dZkCxhkMTJg0iaioKEfHqfekQIpG\nKSAsjOjERPKPHkWr1ZI2fDjP6XQcdHQwcUVOUXVKNAboAgwEkoGngCiqrkGuA16tXv8EcPtltj3n\nM6quTbYAmlJ1ajUKqAQir9cbqiXpwMEWLXh22jRHR2kQ5BqkaLQqyspY8Ne/ojQaXJs25cv16zm2\ncSPfms1IW1LUN7lAB6ORL9avp1OnTpddX1yetCBFo+Xs6krygw9Smp+PzWajf/funPT0ZI6jgwlx\nlRQwxmBg1PjxUhxrkRRI0agFRkQQ2bcvBceOodNqSRs2jL/q9Rx2dDAhrsJc4HBAANP/+U9HR2lQ\npECKRq/bsGE4u7lxtrgYf19fkrp1Y4wMQyfqiX3AVKORjC+/xGg0OjpOgyIFUjR6Lm5uJKWlUZyb\ni81mY0CPHhz28OBtRwcT4jLMwAgnJ16YOZOwsDBHx2lwpEAKAQRFRtKhVy8Kjh1Dr9Px0N138zcn\nJ9Y5OpgQf+AZrRbfhAQe/r//c3SUBkkKpBCARqOh+4gRGJs04WxxMX5eXoxLTWWYXs8BR4cTogbz\ngAxPT/69ZAkauYf3upACKUS1Ju7uDHj4YUry8zFXVhIZEsKAxET6GwwUOzqcEOdZDzxhNJK5fj2+\nvr6OjtNgSYEU4jz+7duTOHo0+ceOYbNaSercmdbh4aTKrB+ijsgGhur1zP/0UyIiIhwdp0GTAinE\nRSJ79yYmOZncnBwA7h04kBO+vjypq4+THomGpABI0et5dsYMBgwc6Og4DZ4USCEuotFo6DFiBK0j\nI+2ddiaOHMkiFxc+dHQ40WiZgIE6HSkjR/KXJ590dJxGQYaaE+J3nC0pIeOFF6g4e5amPj4cy83l\npffe4wuzmW6ODicaFQXcp9WS26kTX2/ciE7OZtwQ0oIU4nc0cXfnjscew2axUF5SQisfHx5MTWWI\nXk+Oo8OJRuVFjYafWrZkyapVUhxvICmQQvwBr5Ytuf3RRynOy8NsMhHdpg0pffqQYjCQ5+hwolH4\nD/C2qysrNm/G1dXV0XEaFSmQQlxG6w4d6HPffeQfOYLNZiPlllsIi4+nlxRJcZ2tAiYaDHy5di2t\nWrVydJxGRwqkEFcgOjGRjrfeSu6hQwAMS0qibVwcvQ0G8h0bTTRQy4BUg4FFX3xBXHy8o+M0SlIg\nhbgCGo2GniNH0jYhwV4khycn0yYujl5SJEUtmw+MNRhY8tVX3JqS4ug4jZYUSCGukN5goN9DD9Em\nPr7GInnasfFEA/GWRsMTRiOfff01vZOSHB2nUZPbPIS4ShazmeVz5pD9ww/4BgUBsHT1an7aupU1\nZjOBjo0n6rEZGg1vuLjw5apVxN1yi6PjNHpSIIW4BmaTiRXvvmsvkhqNhuXff8+atWtZbbEQ6uiA\nol5RwBStlnQ3N5avX094dLSjIwmkQApxzcwmE9+8/z57N2/GJygIrVbLuu3bWZaZSabFQqyjA4p6\nwQZM1GpZ3awZ32zaRFDbto6OJKpJgRTiT7BaLKxdsIAdq1fjExSETqcja/du5i9ZwgKLhf6ODijq\nNAtwv07HDl9fvtmyBb+AAEdHEueRAinEn2Sz2fh+8WK2fvEF3oGB6A0G9h4+zDvp6Yw3mfi7zYaM\nfSIulg+M0OkoDAhg5datNPfxcXQkcREpkELUAqUUP3z1FRsyMmjesiVOLi4UlpbyTno6N+XmkmE2\n4+XokKLO+B4YptNxS9euzP/qK1zd3R0dSdRAbvMQohZoNBriBwyg/4QJFOflUZKfj6ebG0+lpdEk\nJoaOej1Zjg4pHE4B/9JoGKDX8+CDD5KxerUUxzpMWpBC1LLcw4f54o03KC0owMvfH41GQ9bu3cxb\nupQZZjMPARpHhxQ33BngPr2e3a6u/GvmTAalpaHT6x0dS/wBKZBCXAflpaWs+vBDsrOy7Nclj+fl\n8dbChXQrLWWuxUITR4cUN0wWMFSvp127dryzYAFtY2IcHUlcASmQQlwnNpuN7ZmZbFi0CHcvL5p4\neFBhMjFv6VLy9+/nM7OZNo4OKa4rBbyu0fB3nY6xw4fz/Btv4OElV6PrCymQQlxnR3bv5qs338Rq\nseDZogUA32zdyherVvGqxcJ9yCnXhqgIGK3X81OTJsycMYOh48fLKdV6RgqkEDdAcX4+me+8w4ns\nbLwDA9HqdBw4fpyPlyyhRXEx75nNtHd0SFFrlgPj9HpuDglhzr//TfuEBEdHEtdACqQQN4jZZGLj\nokX8uHIlnr6+OLu5YbPZWLV1K5+tWcN4q5W/2WxybbIeOwE8otfznV7P6OHDeeaVV/Dw9nZ0LHGN\npEAKcQMppdi/fTur582joqyM5q1aodPpOFNSwn++/JKcgwd5x2xmgKODiqtiBeYAz+n1xLdty5NT\nptDn7rvRGwyOjib+BCmQQjhARVkZmz/7jJ9WrsTo6oqHtzcajYYd+/ezYNky4ioredNsRgYeq/s2\nAQ8bDJS5u3PvwIGMeuopAsPDHR1L1AIpkEI40KlDh1g9bx6nDhygmZ8fTi4umCwWvly3jtWbN/OM\n1cokpZB2SN1zBHhCr2etTkdKp05MnDSJhAEDMBiNjo4maokUSCEczGq1smvDBtanp2OzWmnWsiVa\nrZYT+fksWLYM06lTvGQ2czsy9FVdUAb8P42GN3Q6uoSGcs/w4dz24IN4tWzp6GiilkmBFKKOKD1z\nho2LFrH7++9xbdYMN09PlFJk7d7Nl6tXYygt5VmTiRGA3Cxw450B3tZoeF2no7WfH0MSE7lz/Hja\ndeqEViu/ujREUiCFqEOUUhzds4dVH31E4enTNLvpJpxcXFBKsWP/fr5evZqi/HyeNptJA5wdHbgR\nyAFm6XR8DHQICKBHTAyDR4+mY1ISRhcXR8cT15EUSCHqILPJxM5vv2XL559TUVqKh7c3zm5uAOw9\nfJiv16zh8LFjPGa1MlEpPByctyH6CXhJr2eFRkOndu3oGhZG58REug0bdlW3buh0OqKjo7HZbOj1\net588026dOlCTk4Ot99+O7/88stVZ+vTpw+zZs0iNlam5b6e5EyNEHWQwcmJmORkInr2ZO+WLWxZ\ntozTBw/i2rw5oYGBhI4ezeFTp/h67Vpm7t/PBJuNx2w2ZEbBP0cB3wAznJzYpdXStUMHpoSG0rFn\nT2JTUvBt3fqq9+nq6sr27dsBWLlyJVOmTOHbb78FqmaBEXWXFEgh6jAnZ2cie/UirGtXDvz4I5uW\nLOH0wYO4NG1KgK8vD911FycLCli+bh1tdu1ihFKMtVrpjAxfdzUqgcXAS05OlDo70zUykkHt2hGf\nkkJU3740/ROTGZ9/kq6oqIjmzZtfsk5OTg733XcfZ8+eBbC3MgFmzpzJwoUL0el09O/fnxkzZlyw\n77FjxxIQEMALL7xwzRlFzaRAClEP6A0G2nXqREhcHId//ZVNS5dy+sABjG5u3OTtzejBgxl0662s\n376d1G3b0FdUMMpi4X6lCHZ0+DqqAlgJpBsMfG2z0drHh64REcSEhpJw++2Ed+9Ok1qYq7G8vJzY\n2FjKy8s5efIka9asuWQdX19fVq1ahZOTE7/99ht33303WVlZZGZm8sUXX5CVlYXRaKSwsNC+jdls\n5p577iEyMpKpU6f+6ZziUnINUoh6SCnFsb172fL55xzetQuDwYCHry96gwGlFAeOH+e7bdvYvHMn\nYRoNI00mhgKN/UaECmAFVUUx02ajtbc34e3aEXbTTQQGB9N50CDaxsfX6r2MHh4eFBcXA7B582Ye\neOABdu7cSU5ODgMHDmTHjh0UFxfzyCOP8NNPP6HT6cjOzqa0tJTJkycTFhZGWlraBfvs06cPZ86c\nYcSIEVIcryNpQQpRD2k0Gvzbt6dVaCinDh1i14YN7P7uOywmE05NmhDs50fIHXcwcsAAfsrO5r87\ndvBsdjahWi13VRfLq7+aVj9VUDV4eLrBwPLqlmJkWBhP+Pjg4eKCX5s2dLrjDgIjItDpdNc1S5cu\nXcjLyyMvL++C5a+++iotWrRgx44dWK1WXK6gd2y3bt1Yu3Ytjz/+OEYZnOC6kAIpRD2m0WhoERxM\ni+BguqemcmT3bn5Zu5ac6p6RLh4exIWGEt++PRarlZ0HDvD1jh28sHcvfhoNPaxWelitdAOCaRjX\nLa3ALmALsLK6KAb7+tKhfXsm+/jg3qQJPv7+RPTqRVBUFE19fK5rZ5nzT9Lt2bMHm82Gl5cXZWVl\n9uVFRUUEBFQNLDh//nysVisASUlJTJ8+nZEjR+Li4sKZM2do1qwZAGlpaaxfv57U1FSWLFly3Yt7\nYyQFUogGwsnZmZCYGEJiYigtLOTgzz+za8MGTu7fD4CzuztRISF0bNuWUTYbh06cYN+RI7z72288\nfuwYWCx00WrpZTLRDYgF6kO75DhVxXCTRsN3Tk78bLHQzMWFkFat8G/Viqd8fHB3ccE7IIAOvXrR\nOjIST1/fG5avoqKC2NhYe6GcP3/+JQV54sSJDB06lPnz59OvXz9cXV0BSElJ4eeffyY+Ph6j0cht\nt93Giy++aN9+0qRJFBYWcv/997Nw4cIb9p4aC7kGKUQDV3rmDId//ZVdGzdybO/eqi9XrZYmHh44\nu7mh1WpRSpFXVET2kSPsP3SI/QcPcrSoiEiDgR5mM11sNoKBAMAHx7U0C4EdwGbgOycntipFOdC2\nRQtaBwcT5OdHS3d3dBYLAD6BgUT07GlvKQpxNaRACtGInC0u5tTBg5z47TcO/fILuYcPV72gFEY3\nN5p4eNinaKowmdh/7Bj7cnI4kpNDfmEhp8vKqLRa8dPr8ddoCLLZCDabCQT7IwBwvcpcJiAPOA3k\nVv95Ajig03FQr+cQcNRiwQYEenoSFBREUEAAAc2a4arRQPXXmMFopFVoKEFRUQRFRkpRFH+KFEgh\nGjFTRQX5x45x+tAhDv3yC8f27sVSWYlNKfROTri4u+Pk7IxWp7Of1qswmSgoLiavqIiCoiLyCwsp\nzM+n4MwZ8kpKyD17lkqbDS2grd7m3M8aqlqf537WUnXNsMJmo6mTE02dnfFo0gQPNzfcPT1p3rw5\n3p6eeHl44GE0ojWbMVdUVI19qtHgExhIYEQELUJC8Pb3t08bJkRtkAIphLCz2WwUnT5N3pEjHNmz\nh+P79lGcm4upshKdVouiutOJUuiNRgzOzjgZjeiNRvuA3UopVNUP2JSyP1fnfj7/Ub2ek16PFVtd\n4QAAAOJJREFUsliwmEyYTSbMFRVAVSckDWC12fDw8sK/fXv8w8Lw9venecuWMiGxuK6kQAoh/pBS\nCnNlJWVFRZytfpQUFHDmxAkKT5+mKDeX0oIClFJoNJqqPwH+qCVXfVpUKYWy2TC6uuLq4UETT0+a\nenvTzM8PD29vmjRtiqunJ65Nm+LkLEOzixtLCqQQ4k+z2WxUlJVhs1jsrcNzBRCocZlGo8Ho6oqx\nSRO5RUHUSVIghRBCiBrILJ9CCCFEDaRACiGEEDWQAimEEELUQAqkEEIIUQMpkEIIIUQNpEAKIYQQ\nNfj/WwNNgiNA3V8AAAAASUVORK5CYII=\n",
					"text/plain": [
						"<matplotlib.figure.Figure at 0x7fa3162d37b8>"
					]
				},
				"metadata": {},
				"output_type": "display_data"
			}],
			"source": [
				"explode =  (0, 0, 0.1, 0, 0)\n",
				"plt.pie(list(homicide_race_counts_values ), labels=homicide_race_counts_name,  autopct='%1.1f%%', \n",
				"        shadow=True, startangle=140, explode=explode)\n",
				"plt.axis('equal')\n",
				"\n",
				"plt.show()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 38,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"{'Hispanic': 44618105, 'Native American/Native Alaskan': 3739506, 'Black': 40250635, 'White': 197318956, 'Asian/Pacific Islander': 15834141}\n",
					"{'Hispanic': 5634, 'Native American/Native Alaskan': 326, 'Black': 19510, 'White': 9147, 'Asian/Pacific Islander': 559}\n"
				]
			}],
			"source": [
				"print(mapping)\n",
				"print(homicide_race_counts)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 39,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"{'Hispanic': 12.63, 'Native American/Native Alaskan': 8.72, 'Black': 48.47, 'White': 4.64, 'Asian/Pacific Islander': 3.53}\n"
				]
			}],
			"source": [
				"homicide_race_counts_per_hundredk={}\n",
				"\n",
				"for i,v in homicide_race_counts.items():\n",
				"    for u,b in mapping.items():\n",
				"        if i == u:\n",
				"            rr=v/b*100000\n",
				"            homicide_race_counts_per_hundredk[i]=round(rr,2)\n",
				"    \n",
				"print(homicide_race_counts_per_hundredk)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 43,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"data": {
					"image/png": 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XX30VwP0h0Dlz5qBTp0745JNPEBIS8o9eV19UorzxEaKHDBgwABMnTtQpGapan376KcLD\nw3H48OFqeX4jIyP89ttvBvHba3JyMjp27IiUlBQ0bNhQdhx6CNcgiCRLSUnBiRMnIIRAfHw8Vq1a\nVWP2tH0cGo0GK1euxHPPPcdyMFBlb1pBpIB7iFe9oqIiTJ8+Hb///jsaN26McePG4aWXXqq21zOE\nf8P8/HzY2dnB2dkZ+/fvlx2HysAhJiIiUsQhJiIiUsSCICIiRSwIIiJSxIIgIiJFLAgiIlLEgiAi\nIkUsCCIiUsSCICIiRSwIIiJSxIIgIiJFLAgiIlJkcAfra926NaysrGBkZARTU1OcPn0aGRkZGDt2\nLBITE9G6dWvs2LEDVlZWsqMSEdVqBrcGYWRkhKioKJw/fx6nT58GACxduhSDBg1CfHw8fH19Df4k\nG0REtYHBFYQQQnsGsgd27dqF4OBgAPfPQBURESEjGhFRnWJwBaFSqTB48GB069YNX375JQAgNTUV\ndnZ2AO6fsi8tLU1mRCKiOsHg5iCOHz8Oe3t73L59G35+fujQocMjJzgp64QnhnAiFCKimqas0wIZ\n3BqEvb09AKBZs2YYOXIkTp8+DTs7O6SmpgK4f3pGW1vbMh8vhKiVPwsXLpSegcvH5ePy1b6f8hhU\nQeTn5yM3NxcAkJeXhx9//BFubm4YMWIENm3aBADYvHkzAgICJKYkIqobDGqIKTU1FaNGjYJKpUJJ\nSQmCgoLg5+eHJ598EoGBgdiwYQOcnJywY8cO2VGJiGo9gyoIZ2dnXLhw4ZHrmzRpgp9++klCIsPh\n4+MjO0K14vLVbFy+2kklKhqEqkFUKlWFY2pERPSX8r43DWoOgoiIDAcLgqgUB8cWUKlUNebHwbGF\n7LeMajEOMRGVolKp4L5irOwYlRYzO5yfeXosHGIiIqK/jQVBRESKWBBERKSIBUFERIpYEEREpIgF\nQUREilgQRESkiAVBRESKWBBERKSIBUFERIpYEEREpIgFQUREilgQRESkiAVBRESKWBBERKSIBUFE\nRIpYEEREpIgFQUREilgQRESkiAVBRESKWBBERKSIBUFERIpYEEREpIgFQUREilgQRESkiAVBRESK\nWBBERKSIBUFERIpYEEREpIgFQUREilgQRESkiAVBRESKDK4gNBoNunTpghEjRgAAMjIy4Ofnhw4d\nOsDf3x9ZWVmSExIR1Q0GVxChoaFwdXXVXl66dCkGDRqE+Ph4+Pr6IiQkRGI6IqK6w6AKIjk5Gd9/\n/z2mTp2qvW7Xrl0IDg4GAAQHByMiIkJWPCKiOsWgCuKNN97AihUroFKptNelpqbCzs4OANC8eXOk\npaXJikdEVKeYyA7wwL59+2BnZwdPT09ERUWVeb/S5aFk0aJF2r/7+PjAx8enagISEdUCUVFR5X7H\nlqYSQojqjVM5c+fORVhYGExMTFBQUICcnByMGjUKZ8+eRVRUFOzs7JCSkoIBAwbgypUris+hUqlg\nIItDNZRKpYL7irGyY1RazOxwfubpsZT3vWkwQ0xLlixBUlISrl+/ju3bt8PX1xdff/01nn76aWza\ntAkAsHnzZgQEBMgNSkRURxhMQZRlzpw5OHjwIDp06IBDhw5hzpw5siMREdUJBjPEVBU4xESPi0NM\nVNfUiCEmIiIyLCwIIiJSxIIgIiJFLAgiIlLEgiAiIkUsCCIiUsSCICIiRSwIIiJSxIIgIiJFLAgi\nIlLEgiAiIkUsCCIiUsSCICIiRSwIIiJSVKlTjubl5eHmzZtQqVRo2bIlLCwsqjsXERFJVmZB5Obm\n4vPPP8f27dtx584d2NraQgiB1NRUNG3aFOPHj8e//vUvNGzYUJ95iYhIT8osiICAAIwbNw579uyB\nnZ2dzm2pqanYvXs3Ro4ciZ9++qnaQxIRkf7xjHJEpfCMclTXlPe9Wak5CAC4ffs2QkNDUVBQgBdf\nfBHt2rWrsoBERGR4Kr0V06xZs+Dv749Ro0Zh/Pjx1ZmJiIgMQJkF4e/vj6NHj2ovFxUVoXXr1mjd\nujXu3bunl3BERCRPmQWxY8cO7NmzB+PGjUNCQgI++OADvPvuu3jttdewbt06fWYkIiIJypyDsLKy\nwooVK3D9+nW89957cHBwwCeffILGjRvrMx8REUlSZkEkJCTgv//9L+rVq4dVq1YhISEBY8eOxbBh\nw/DKK6/A2NhYnzmJiEjPyhxiGjduHJ555hkMGDAAEydORL9+/fDDDz+gcePG8PPz02dGIiKSoMw1\niHv37sHZ2Rm5ubnIz8/XXj9p0iSMGTNGL+GIiEieMgti3bp1mDFjBurVq4dPP/1U57YGDRpUezAi\nIpKrzILo06cP+vTpo88sRERkQMqcgxg2bBh27typM7z0QH5+PsLDwzFs2LBqDUdERPKUuQaxadMm\nfPLJJ1iwYAFMTExgb28PIQT+/PNPqNVqjB07Fps2bdJjVCIi0qdKHawvNTUViYmJAAAnJ6dHju5q\nKHiwPnpcPFgf1TWPfbA+Ozs7gy0FIiKqHjzlKBERKWJBEBGRonILQq1WIygoSF9ZiIjIgJRbEMbG\nxkhMTERRUZFewty7dw89evSAl5cXOnXqhLlz5wIAMjIy4Ofnhw4dOsDf3x9ZWVl6yUNEVJdVOEnd\npk0b9OnTByNGjICFhYX2+jfffLPKw5iZmeHw4cMwNzeHWq1Gnz59cPz4cezevRuDBg3C22+/jWXL\nliEkJARLly6t8tcnIqK/VDgH0bZtWwwfPhwajQY5OTnan+pibm4O4P7ahEajgbW1NXbt2oXg4GAA\nQHBwMCIiIqrt9YmI6L4K1yAWLlwI4P7e0w++vKuTRqNB165dkZCQgBdffBGurq5ITU3VbmbbvHlz\npKWlVXsOIqK6rsI1iF9++QWurq7o2LEjAODixYt4+eWXqy+QkRHOnz+P5ORk/Pzzz4iKioJKpdK5\nz8OXiYio6lW4BvH666/jhx9+wIgRIwAAHh4eOueqri6NGjXCU089hbNnz8LOzk67FpGSkgJbW9sy\nH7do0SLt3318fODj41PtWYmIaoqoqChERUVV6r6V2pPa0dFR53J1nU0uPT0dpqamsLKyQkFBAQ4e\nPIiFCxdixIgR2LRpE9555x1s3rwZAQEBZT5H6YIgIiJdD//ivHjx4jLvW2FBODo64sSJE1CpVCgu\nLkZoaChcXFyqJOjD/vzzTwQHB0MIAY1Gg4kTJ2LgwIHw8vJCYGAgNmzYACcnJ+zYsaNaXp+IiP5S\n4cH60tPT8dprr+Gnn36CRqOBv78/QkNDYWNjo6+MlcaD9dHj4sH6qK55rIP1NW3aFFu2bKnyUERE\nZNgq3Irp+vXrePrpp9GsWTPY2toiICAA169f10c2IiKSqMKCGD9+PAIDA/Hnn3/ijz/+wJgxYzBu\n3Dh9ZCMiIokqLIj8/HxMnDgRJiYmMDExwYQJE1BYWKiPbEREJFGZcxB3794FAAwdOhRLly7Fc889\nB5VKhfDwcDz11FN6C0hERHKUuRWTs7NzmbPbKpXKIOchuBUTPS5uxUR1zT/aiunGjRvVFoiIiAxf\nhZu5qtVq7Nu3D7///jtKSkq011fH4b6JiMhwVFgQTz/9NOrXrw83NzcYGfEMpUREdUWFBZGcnIyY\nmBh9ZCEiIgNS4SqBv78/fvzxR31kISIiA1LhGkSvXr0wcuRICCFgamoKIQRUKhWys7P1kY+IiCSp\nsCBmzZqFkydPws3NjSfqISKqQyocYnJ0dETnzp1ZDkREdUyFaxBt2rSBj48Phg4dCjMzM+313MyV\niKh2q7AgnJ2d4ezsjKKiIhQVFekjExERGYAKC2LhwoX6yEFERAamwoIYMGCA4vxDZGRktQQiIiLD\nUGFBrFy5Uvv3wsJCfPvttzAxqfBhRERUw1X4Td+1a1edy3369EH37t2rLRARERmGCgviwXkhAECj\n0eDcuXPIysqq1lBERCRfpdYgHhwv3MTEBM7Ozli/fr0+shERkUQVFgTPC0FEVDdVarb5xIkTj5wP\nYtKkSdUWioiI5KuwICZOnIiEhAR4enrC2NgYwP1T1LEgiIhqtwoL4uzZs7h8+TKPxUREVMdUeLC+\nzp07IyUlRR9ZiIjIgFS4BpGeng5XV1d0795d52B9u3fvrtZgREQkV4UFsWjRIj3EICIiQ1NhQfTv\n318fOYiIyMBUOAdBRER1EwuCiIgUVaogCgoKEB8fX91ZiIjIgFRYEHv27IGnpyeGDBkCALhw4QJG\njBhR7cGIiEiuCgti0aJFOH36NBo3bgwA8PT05PGZiIjqgAoLwtTUFFZWVjrXca9qIqLar8KC6NSp\nE7Zu3Qq1Wo1r165h5syZ6N27d7WESU5Ohq+vLzp16gQ3NzesWbMGAJCRkQE/Pz906NAB/v7+PB8F\nEZEeVFgQH3/8MWJjY2FmZobx48fDysoK//nPf6oljImJCT766CPExsbil19+wdq1axEXF4elS5di\n0KBBiI+Ph6+vL0JCQqrl9YmI6C8qIYQo7w7R0dHo0qWLvvLoGDlyJGbMmIEZM2bgyJEjsLOzQ0pK\nCnx8fBAXF/fI/R+c2Ijon1KpVHBfMVZ2jEqLmR3Ozzw9lvK+Nytcg5g1axZcXFwwf/58/Prrr1Ue\nriy///47Lly4gJ49eyI1NRV2dnYAgObNmyMtLU1vOYiI6qoKD7Vx+PBhpKSkYMeOHZg+fTqys7Mx\nduxYzJs3r9pC5ebmYvTo0QgNDUXDhg0fmRQvb5K89LGjfHx84OPjU00piYhqnqioKERFRVXqvhUO\nMZV26dIlLF++HOHh4SgqKvqn+cpVUlKC4cOHY+jQoXjttdcAAC4uLoiKitIOMQ0YMABXrlx55LEc\nYqLHxSEmqmsea4jpypUrWLRoEdzc3LRbMCUnJ1d5yAemTJkCV1dXbTkAwIgRI7Bp0yYAwObNmxEQ\nEFBtr09ERPdVuAbRq1cvjB07FoGBgXBwcKjWMMePH4e3tzfc3NygUqmgUqmwZMkSdO/eHYGBgbh5\n8yacnJywY8cO7Y57pXENgh4X1yCorinve/NvDTEZOhYEPS4WBNU15X1vljlJHRgYiB07dmh/m39A\nCAGVSoWYmJiqT0pERAajzIIIDQ0FAOzdu1dvYYiIyHCUOUltb28PAFi3bh2cnJx0ftatW6e3gERE\nJEeFWzEdPHjwkev2799fLWGIiMhwlDnE9N///hfr1q3D9evX4e7urr0+JycHffr00Us4IiKSp8yC\nGD9+PIYOHYp3330XS5cu1V5vaWmJJk2a6CUcERHJU2ZBWFlZwcrKCtu2bQMApKWlobCwELm5ucjN\nzUWrVq30FpKIiPSvUqccbdeuHZydndG/f3+0bt0aQ4cO1Uc2IiKSqMKCmDdvHk6ePIn27dvjxo0b\nOHToEHr27KmPbEREJFGlTjlqY2MDjUYDjUaDAQMG4OzZs/rIRkREElV4uO/GjRsjNzcX3t7eCAoK\ngq2tLSwsLPSRjYiIJKpwDWLXrl1o0KABVq9ejSFDhqBt27bYs2ePPrIREZFEFa5BlF5bCA4OrtYw\nRERkOMosCEtLS8WD9D34Mzs7Wy8BiYhIjjILIicnR585iIjIwFQ4BwEAx44dw8aNGwEA6enpuHHj\nRrWGIiIi+SosiMWLF2PZsmUICQkBABQVFWHChAnVHoyIiOSqsCD+97//Yffu3drJagcHBw4/ERHV\nARUWRL169bTnhwaAvLy8ag9FRETyVVgQgYGBmD59OjIzM/HFF19g0KBBmDp1qj6yERGRRBXuB/HW\nW2/h4MGDaNSoEeLj4/H+++9j8ODB+shGREQSVVgQADB48GCdUggPD8fYsWOrLRQREclX5hBTXl4e\nPvroI7zyyitYt24dNBoNIiIi4Orqiq1bt+ozIxERSVDmGsSkSZNgaWmJXr164eDBg9i8eTPq16+P\nrVu3wtPTU58ZiYhIgjIL4tq1a4iJiQEATJ06Ffb29khKSkL9+vX1Fo6IiOQpc4jJxOSv7jA2NkbL\nli1ZDkREdUiZaxAXL15Eo0aNANw/UF9BQQEaNWrEg/UREdURZRaEWq3WZw4iIjIwlTpYH1Weg2ML\n7Z7nNeXHwbGF7LeNiAxQpfaDoMr7M/kPuK+oWfuIxMwOlx2BiAwQ1yCIiEgRC4KIiBSxIIiISBEL\ngoiIFLFeBN83AAAgAElEQVQgiIhIEQuCiIgUGVRBvPDCC7Czs4O7u7v2uoyMDPj5+aFDhw7w9/dH\nVlaWxIRERHWHQRXE5MmT8cMPP+hct3TpUgwaNAjx8fHw9fVFSEiIpHRERHWLQRVE3759YW1trXPd\nrl27EBwcDAAIDg5GRESEjGhERHWOQRWEkrS0NNjZ2QEAmjdvjrS0NMmJiIjqhhp3qA2VSlXu7YsW\nLdL+3cfHBz4+PtUbiIioBomKikJUVFSl7mvwBWFnZ4fU1FTY2dkhJSUFtra25d6/dEEQEZGuh39x\nXrx4cZn3NbghJiEEhBDayyNGjMCmTZsAAJs3b0ZAQICkZEREdYtBFcT48ePRu3dvXL16Fa1atcLG\njRsxZ84cHDx4EB06dMChQ4cwZ84c2TGJiOoEgxpi2rp1q+L1P/30k56TEBGRQa1BkOFzauko/QRH\nf+fHqaWj7LeMqMYyqDUIMnxJt5Jx7fWPZceotHb/mSk7AlGNxTUIIiJSxIIgIiJFLAgiIlLEgiAi\nIkUsCCIiUsSCICIiRSwIIiJSxIIgIiJFLAgiIlLEgiAiIkUsCCIiUsSCICIiRSwIIiJSxIIgIiJF\nLAgiIlLEgiAiIkUsCCIiUsSCIKojatrpYnnKWPl4ylGiOqKmnS4W4CljZeMaBBERKWJBEBGRIhYE\nEREpYkEQUa3g4NhC+qT63/1xcGwh+20rFyepiahW+DP5D7ivGCs7xt8SMztcdoRycQ2CiIgUsSCI\niEgRC4KIiBSxIIiISBELgoiIFLEgiIhIEQuCiIgUsSCIiEgRC4KIiBTVmII4cOAAOnbsiPbt22PZ\nsmWy4+hdbkKa7AjV6tTNa7IjVCv++9Vstf3fryw1oiA0Gg1mzJiBH374AbGxsdi2bRvi4uJkx9Kr\n2v4BPZXML5iajP9+tVONKIjTp0+jXbt2cHJygqmpKZ577jns2rVLdiwiolqtRhTErVu34Oj416kH\nW7ZsiVu3bklMRERU+6mEEEJ2iIp8++23+OGHH/D5558DAMLCwnD69GmsWbNG534qlUpGPCKiGq2s\nGqgRh/tu0aIFkpKStJeTk5PRosWjx1GvAV1HRFRj1Ighpm7duuG3335DYmIiioqKsH37dowYMUJ2\nLCKiWq1GrEEYGxvjk08+gZ+fHzQaDV544QW4uLjIjkVEVKvViDkIIiLSvxoxxERERPrHgiAiKkWt\nVuOtt96SHcMg1Ig5iLoqLy8PDRo0gJHR/R7XaDQoLCyEubm55GSP7969e/j222/x+++/o6SkRHv9\nggULJKYiuj/neezYMdkxDALXIAzYwIEDkZ+fr72cn5+PQYMGSUxUdQICArBr1y6YmJjAwsJC+1Ob\nPFx2arUaQUFBktJULSEEwsLC8P777wMAkpKScPr0acmpqo6XlxdGjBiBr7/+Gt999532p67hGoQB\nKywsRMOGDbWXGzZsqFMYNVlycjIOHDggO0a1unnzJkJCQvDuu+/i3r17CAwMhJeXl+xYVeLll1+G\nkZERIiMjsWDBAlhaWuLZZ5/FmTNnZEerEoWFhbCxsUFkZKT2OpVKhWeeeUZiKv1jQRgwCwsLREdH\no0uXLgCAc+fOoUGDBpJTVY3evXvj0qVLcHNzkx2l2mzYsAFBQUEICQnB4cOH8dRTT+H111+XHatK\nnDp1CtHR0drCs7a2RlFRkeRUVWfjxo2yIxgEFoQB+89//oMxY8bAwcEBQgikpKQgPDxcdqwqcezY\nMWzatAnOzs4wMzODEAIqlQoxMTGyoz226Oho7d9fe+01TJ8+HX369IG3t7dO4ddkpqamUKvV2sPb\n3L59WztXVhtcvXoVL730ElJTU/Hrr78iJiYGu3fvxrx582RH0yvuB2HgiouLER8fDwDo0KEDTE1N\nJSeqGomJiYrXOzk56TlJ1RswYECZt6lUKp1hi5pqy5YtCA8PR3R0NIKDg7Fz50588MEHCAwMlB2t\nSvTv3x8rVqzA9OnTcf78eQBA586d8euvv0pOpl9cgzBAkZGR8PX1fWRS7OrVqwBQK8ZBHxRBWloa\nCgsLJaepWocPH5YdodoFBQWha9euOHToEIQQiIiIqFVHN8jPz0f37t11rjMxqXtfl3VviWuAI0eO\nwNfXF3v27HnkttoyUbZ7927MmjULf/zxB2xtbZGYmAgXFxfExsbKjlZl5s6di7fffhuNGzcGAGRk\nZGDVqlX48MMPJSd7fBMnTsTXX3+Njh07PnJdbdC0aVMkJCRoh9B27twJe3t7yakkEEQSuLu7i/T0\ndOHp6SmEECIyMlJMmTJFcqqq9WDZSvPy8pKQpOo9vBwlJSXCxcVFUpqql5CQIAYOHCgaNGggHBwc\nRJ8+fcSNGzdkx9I7rkEYsNq8M5mpqSlsbGyg0Wig0WgwYMCAWrOFzwNqtRr37t2DmZkZAKCgoAD3\n7t2TnOrxhISEYMmSJSgoKECjRo20h9ivV68epk2bJjld1WnTpg1++ukn5OXlQaPRwNLSUnYkKVgQ\nBiwgIABWVlbo2rWr9kumtmjcuDFyc3Ph7e2NoKAg2Nra1rod5YKCgjBw4EBMnjwZwP1NJ4ODgyWn\nejzvvvuu9ickJER2nCr30UcflXv7m2++qackhoFbMRmw2rzVxIPDiGg0GmzZsgVZWVmYMGECmjRp\nIjtaldq/fz8OHToEABg8eDD8/f0lJ3o8cXFx6Nixo86mvKXV9E14Fy9eDACIj4/HmTNntOed2bNn\nD7p3746wsDCZ8fSOBWHApk2bhpkzZ9bKncn279+PoUOH6lz36aef4sUXX5SUiCpj2rRp+PzzzxU3\n5a0tm/ACgLe3N/bt26cdWsrJycGwYcNw9OhRycn0iwVhwFxdXfHbb7/Vyp3JevfujQ8//BC+vr4A\ngBUrViAyMhL79++XnKzqnDx5EjNnzsSVK1dQVFQEtVoNCwsLZGdny45GFejQoQNiYmK0Q7v37t2D\nu7u7dp+kuoJzEAasNn1ZPmz37t0YPnw4VqxYgQMHDiAuLg67du2SHatKzZgxA9u3b8eYMWNw9uxZ\nfPXVV9p9WWqDEydOPLIBxaRJkyQmqjqTJk1C9+7dMWrUKABAREREjZ8/+ie4BlEDPLwzWatWrSSm\nqTppaWkYNGgQunbtig0bNmi3Oa8tnnzySZw9exbu7u7atT4vLy/tnrk12cSJE5GQkABPT08YGxsD\nuD/EtGbNGsnJqs65c+e0h/329vauNQda/Du4BmHAauPOZJaWllCpVNrhsqKiIly/fh07d+6ESqWq\nVcMv5ubmKCoqgqenJ95++23Y29tDo9HIjlUlzp49i8uXL9e6Ui/N09MT9vb22jWkpKSkWvPLWWXV\nnqNr1ULz58/HyZMn0b59e9y4cQOHDh1Cz549Zcd6LDk5OcjOztb+WVhYiNzcXO3l2uTrr7+GWq3G\nJ598AgsLC9y8eRPffvut7FhVonPnzkhJSZEdo9p8/PHHsLOzw+DBgzF8+HAMGzYMw4cPlx1L7zjE\nZMAeDFF4eHjg/PnzMDIygoeHBy5evCg72mM7fvw4PD09YWFhgbCwMERHR+P111+vc7+h1TRPP/00\nVCoVcnJycOHCBXTv3l1nH53du3dLTFd1nnjiCZw6dQo2Njayo0jFISYD9mBnsn79+tW6ncleeukl\nXLx4ERcvXsSqVaswdepUTJw4EUeOHJEd7bG5ubmVO/RSk7dC8/X1RXFxMbp06VJrjiysxNHREVZW\nVrJjSMc1CAOWl5eH+vXrQwih3ZksKCioVvxW06VLF0RHR+P9999HixYt8MILL2ivq+nKOpT5AzX5\nkOZvvfUWTpw4gStXrsDd3R19+vRB79690bt371q1k+MLL7yA+Ph4DBs2TGcNqa7tSc01CANmYWGB\nlJQUnD59Gk2aNIG/v3+tKAfg/mR1SEgIwsLCcPToUWg0GhQXF8uOVSWUCiA9PR02NjY1flJ35cqV\nAICioiKcPXsWJ06cwMaNGzFt2jQ0btwYly9flpywarRq1QqtWrVCUVFRrTpT3t/FgjBgX375Jd5/\n/334+vpCCIGZM2diwYIFmDJliuxojy08PBxbt27F+vXr0bx5cyQlJWH27NmyY1WJkydPYs6cOWjS\npAnmz5+PiRMnIj09HRqNBl999RWGDBkiO+JjKygoQHZ2NrKyspCVlQUHB4datcf/woULZUcwCBxi\nMmAdOnTAiRMntGsNd+7cQe/evevc3pw1zZNPPoklS5YgKysL06ZNw/79+9GzZ0/ExcVh3LhxNXo/\niGnTpiE2NhaWlpbo0aMHevbsiZ49e8La2lp2tCp1+/ZtLF++HLGxsTr7INWWQ4lUFjdzNWA2NjY6\nhxm2tLSsNUNMJ0+eRLdu3dCwYUPUq1cPxsbGtWZSsKSkBH5+fhgzZgyaN2+u3TS59Ml1aqqkpCTc\nu3cPzZs3R4sWLdCyZUvtCZFqk6CgIHTs2BE3btzAwoUL0bp1a3Tr1k12LL3jEJMBe+KJJ9CjRw8E\nBARApVJh165dcHd31x6SuCZPmNXmw1AYGf31e1eDBg10bqvpcxAHDhyAEAKxsbE4ceIEVq1ahV9/\n/RVNmjRBr169tEdDrenu3LmDF154AaGhoejfvz/69+/PgiDD0rZtW7Rt21Z7OSAgAMD9nc1qgyee\neAJqtRrGxsaYPHkyvLy8asU5Bi5evKg9mc6DE+sAgBCiVpx/W6VSoXPnzmjcuDGsrKxgZWWFvXv3\n4vTp07WmIB5swmtvb499+/bBwcEBd+/elZxK/1gQBqz0RJlGo0Fubq72y6amq82HoVCr1bIjVJs1\na9bgxIkTOHHiBExNTbWbuE6ZMqVWTVLPmzcPWVlZWLVqFWbOnIns7GysXr1adiy94yS1ARs/fjw+\n/fRTGBsbo1u3bsjOzsZrr71WK7b2SUxMhK2tLYqLi7F69WpkZWXh5ZdfxhNPPCE7GpXjzTff1O77\nYG9vLzsOVTMWhAHz9PTEhQsXsGXLFkRHR2Pp0qXo2rVrjd4Tl8iQzZw5s9x5otp0tNrK4BCTASsu\nLkZxcTEiIiIwY8YMmJqa1vhJztp8GAqq+Z588knZEQwKC8KATZ8+Ha1bt4aHhwe8vb2RmJhY4+cg\n9u7dKzsCUZmUTgpU2+b//g4OMdUwJSUlMDGpfb1+7NgxbNu2DWvXrpUdhahWz//9HbXvm6YWCAsL\nw4QJE7T7OzysJu//UNr58+exdetWfPPNN3B2dsYzzzwjOxIRAODy5cto1KgRtmzZgqFDh2rn/1gQ\nJF1eXh6A2rO/Q2lXr17Ftm3bsH37dtja2mLMmDEQQuDw4cOyoxFp1cb5v3+CQ0ykV0ZGRhg+fDjW\nrl0LR0dHAECbNm1w/fp1ycmI/rJmzRosW7YMHh4e2LdvH5KSkjBhwgT8/PPPsqPpFQvCAL366qvl\n3l6TN7WLiIjA9u3bcerUKfj7+yMwMBAvvPACbty4ITsaUblq6/xfeVgQBmjz5s3avy9cuPCRwxco\nbWlR0+Tl5WHXrl3Ytm0bIiMjMWnSJIwaNQp+fn6yo1EdVta83wO1Zf6vslgQBs7Ly6tGHx66MjIy\nMvDNN98gPDwchw4dkh2H6rCKjiVV184TwYIwcLXlNJxEVPPwfBBERKSIaxAGyNLSUrtJXX5+PszN\nzQHcP1y0SqVCdna2zHhEVEewIEiaxMREXLt2DYMGDUJBQQFKSkp0zqBHRHJxiImk+OKLLzB69GhM\nnz4dAJCcnIyRI0dKTkV039y5c5GZmam9nJGRgXnz5klMJAcLgqRYu3Ytjh8/rj0AWrt27ZCWliY5\nFdF9+/fv1znXtrW1Nb7//nuJieRgQZAUZmZmqFevnvZySUlJnTyUARkmtVqNe/fuaS8XFBToXK4r\n6tZugWQw+vfvjyVLlqCgoAAHDx7EunXr8PTTT8uORQQACAoKwsCBAzF58mQAwMaNG2vFDqp/Fyep\nSQqNRoP169fjxx9/hBAC/v7+mDp1KtciyGDs379fu+Pm4MGD4e/vLzmR/rEgSIrvvvsOw4YNg5mZ\nmewoRFQGzkGQFHv27EH79u0xceJE7N27FyUlJbIjEaFv374A7u+L1KhRI+3Pg8t1DdcgSJri4mLs\n378f4eHhOHbsGAYPHowvv/xSdiyqw65fv442bdrIjmEwuAZB0piammLo0KF47rnn0LVrV0RERMiO\nRHXcmDFjAAADBw6UnMQwcCsmkuLBmkNUVBR8fHwwdepU7NixQ3YsquM0Gg2WLFmCq1evKh76u64d\n7psFQVJ89dVXGDt2LD777DNOVJPB2L59OyIiIlBSUlIrT/n7d3EOgojoIfv378fQoUNlx5COaxCk\nV3379sWxY8d0jlgL8Ei1ZBjCwsIwYcIEXL58GVeuXHnkdg4xEVWjY8eOAQBX38kg5eXlAQByc3Ml\nJzEMHGIiKSZOnIivv/66wuuISB5u5kpSxMbG6lwuKSnBuXPnJKUh0hUcHPzI4b6nTJkiMZEcLAjS\nq5CQEFhaWiImJkZnL1U7OzsEBATIjkcEAIiJiXnkcN/nz5+XmEgOFgTp1bvvvoucnBzMnj0b2dnZ\nyM7ORk5ODu7cuYOQkBDZ8YgA3N8fIiMjQ3v57t27dfJwMJykJilCQkKQkZGBa9euobCwUHu9t7e3\nxFRE982aNQu9evXCmDFjIITAzp078d5778mOpXecpCYpvvzyS4SGhiI5ORmenp44efIkevXqhcjI\nSNnRiADcnyc7fPgwAMDX1xeurq6SE+kfC4KkcHNzw5kzZ9CzZ09cuHABcXFxmDt3Lr777jvZ0Yi0\n0tLSdNZwW7VqJTGN/nEOgqSoX78+6tevDwC4d+8eOnbsiPj4eMmpiO7bvXs32rVrB2dnZ/Tv3x+t\nW7euk3tWsyBIipYtWyIzMxMjR47E4MGDERAQACcnJ9mxiAAA8+fPx8mTJ9G+fXvcuHEDhw4dQs+e\nPWXH0jsOMZF0R44cQVZWFoYMGYJ69erJjkOEJ598EmfPnoWHhwfOnz8PIyMjeHh44OLFi7Kj6RW3\nYiLp+vfvLzsCkY7GjRsjNzcX3t7eCAoKgq2tLSwsLGTH0juuQZBePThIX+mPnUqlQklJCYqKiurk\ntuZkePLy8tCgQQNoNBps2bIFWVlZCAoKgo2NjexoesU1CNKrhw/Sl5ubi7Vr1+Kzzz7DqFGjJKUi\n+ktERAR+++03uLm5wd/fH8HBwbIjScNJapIiMzMTixYtgru7O3JycnDmzBmsWrVKdiyq415++WWs\nXr0ad+7cwfz58/HBBx/IjiQVh5hIr9LT07Fq1SqEh4djypQpmDlzJqysrGTHIgIAdO7cGRcvXoSx\nsTHy8/PRr1+/On0QSQ4xkV45OTmhWbNmmDx5MszNzbF+/Xqd2+vaCVnIsNSrVw/GxsYAAHNzc9T1\n359ZEKRXb7/9tvbvPGkQGZq4uDi4u7sDuH+Ww4SEBLi7u2vPeBgTEyM5oX6xIEiv2rdvDz8/vzq3\nNQjVDEqnGa3LWBCkV0lJSRgzZgyKi4sxcOBADB06FN27d9c5PzWRLNOmTcOQIUMwdOhQdOzYUXYc\n6ThJTVLk5OTgp59+woEDB3D69Gm4uLhgyJAh8Pf3h52dnex4VEelpKTgwIEDOHDgAK5evYoePXpg\nyJAhGDRoEHeUI5Ll8uXL2L9/P3788Uf88MMPsuMQQaPR4NSpU9i/fz8OHTqEBg0awM/PT2cerbZj\nQZA0t27dQmJios7e0zxhEBmq9PR0/PDDDwgKCpIdRW84B0FSvPPOOwgPD4erq6t2s0KVSsWCIINw\n+/ZtfPHFF/j99991foHZsGGDxFT6x4IgKSIiIhAfHw8zMzPZUYgeERAQgH79+mHQoEHaX2DqIhYE\nSdGmTRsUFxezIMgg5efnY9myZbJjSMeCICnMzc3h6emJgQMH6pTEmjVrJKYium/48OH4/vvv8dRT\nT8mOIhUnqUmKzZs3K15fl4+cSYbD0tISeXl5MDMzg6mpqXZP6uzsbNnR9IoFQUREijjERFJcu3YN\n7777Li5fvozCwkLt9devX5eYiugvGRkZuHbtms7ns65tZceCICkmT56MxYsX44033sDhw4exceNG\naDQa2bGIAABffvklQkNDkZycDE9PT5w8eRK9evVCZGSk7Gh6xRMGkRQFBQUYOHAghBBwcnLCokWL\nsG/fPtmxiAAAoaGhOHPmDJycnHD48GGcP38ejRs3lh1L77gGQVKYmZlBo9GgXbt2+OSTT9CiRQvk\n5ubKjkUEAKhfvz7q168PALh37x46duyI+Ph4yan0jwVBUoSGhiI/Px9r1qzB/Pnzcfjw4TK3bCLS\nt5YtWyIzMxMjR47E4MGDYW1tDScnJ9mx9I5bMRERlePIkSPIysrCkCFDUK9ePdlx9IpzECTF4MGD\nkZmZqb2ckZEBf39/iYmIoN3P4e7du9ofNzc39O3bt04OgXKIiaRIT0/XmfSztrZGWlqaxEREwPjx\n47F371507doVKpVK55zUKpWqzm2GzYIgKYyMjJCUlIRWrVoBABITE3lWOZJu7969AIAbN25ITmIY\nWBAkxb///W/07dsX/fv3hxACP//8Mz7//HPZsYgAAMePH4enpycsLCwQFhaG6OhovP7669pfaOoK\nTlKTNOnp6Th58iQAoGfPnmjatKnkRET3ubu74+LFi4iJicHzzz+PqVOnYseOHThy5IjsaHrFSWrS\nq7i4OABAdHQ0kpKS4ODgAAcHByQlJSE6OlpyOqL7TExMoFKpsGvXLsyYMQOvvPIKcnJyZMfSOw4x\nkV599NFH+PzzzzFr1qxHblOpVHXuUAZkmCwtLRESEoKwsDAcPXoUGo0GxcXFsmPpHYeYSO80Gg1+\n+eUX9OnTR3YUIkUpKSnYunUrunXrhn79+iEpKQlRUVGYNGmS7Gh6xYIgKby8vHD+/HnZMYgq5eef\nf8b27duxdu1a2VH0inMQJMXAgQPx7bffgr+fkKE6f/48Zs+ejdatW2PBggVwcXGRHUnvuAZBUjw4\nY5exsTEaNGhQZ8/YRYbl6tWr2LZtG7Zv3w5bW1uMGTMGK1asQGJiouxoUrAgiIj+n5GREYYPH461\na9fC0dERANCmTZs6twf1AxxiIimEEAgLC8MHH3wAALh58yZOnz4tORXVdd999x3Mzc3h7e2NF198\nEZGRkXV6GJRrECTFSy+9BCMjI0RGRuLKlSvIyMiAn58fzpw5IzsaEfLy8rBr1y5s27YNkZGRmDRp\nEkaNGgU/Pz/Z0fSKBUFSdOnSBdHR0TpbM3l4eODixYuSkxHpysjIwDfffIPw8HAcOnRIdhy94hAT\nSWFqagq1Wq09QN/t27dhZMSPIxkea2trTJs2rc6VA8CCIEleffVVjBo1CmlpaXjvvffQt29fzJ07\nV3YsIiqFQ0wkTVxcHA4dOgQhBAYOHFgntzMnMmQsCJImIyMDN2/eRElJifa6Ll26SExERKXxYH0k\nxfz587Fp0ya0bdtWOw/Bg/URGRauQZAUHTp0wKVLl+rcSeCJahJOUpMUnTp1QmZmpuwYRFQOrkGQ\nFGfOnEFAQADc3NxgZmamvX737t0SUxFRaSwIksLV1RUvvvgi3NzcdPZ/6N+/v8RURFQaC4Kk6Nat\nGw+rQWTgWBAkxZtvvgkzMzOMGDFCZ4iJm7kSGQ4WBEkxYMCAR67jZq5EhoUFQQYjNTUVdnZ2smMQ\n0f/jZq4kVWZmJtavX4+BAwfCy8tLdhwiKoV7UpPeFRQUICIiAtu2bcOFCxeQnZ2NiIgIeHt7y45G\nRKVwDYL0avz48ejUqROOHj2K119/HTdu3IC1tTV8fHx4uG8iA8P/kaRXly9fhq2tLVxcXODi4gJj\nY2PtsZiIyLBwiIn06sKFC4iLi8O2bdswYMAANGvWDDk5OZygJjJA3IqJpDp37hy2bduGHTt2oGXL\nljhx4oTsSET0/1gQZBCEEPj55585UU1kQFgQRESkiJPURESkiAVBRESKWBAkRWpqKl544QUMHToU\nwP3NX9evXy85FRGVxoIgKZ5//nn4+/vjjz/+AAC0b98e//nPfySnIqLSWBAkRXp6OgIDA7V7T5uY\nmMDY2FhyKiIqjQVBUlhYWODOnTvavahPnjwJKysryamIqDTuSU1SrFq1CiNGjEBCQgL69OmD27dv\nY+fOnbJjEVEp3A+CpCkpKUF8fDyEEOjQoQNMTU1lRyKiUjjERFK4u7tj+fLlqF+/Pjp37sxyIDJA\nLAiSYs+ePTAxMUFgYCC6deuGlStXIikpSXYsIiqFQ0wk3bVr1/DBBx9gy5YtUKvVsuMQ0f/jJDVJ\nk5iYiPDwcISHh8PY2BjLly+XHYmISmFBkBQ9evRAcXExxowZg2+++QZt2rSRHYmIHsIhJpIiPj4e\nHTp0kB2DiMrBgiC9CgsLw4QJE/DRRx8p3v7mm2/qORERlYVDTKRXeXl5AICcnJxHbuO5qYkMC9cg\nSIrjx4+jT58+FV5HRPKwIEiKLl26IDo6usLriEgeDjGRXv3yyy84ceIEbt++rTMPkZ2dzX0giAwM\nC4L0qqioCLm5uSgpKdGZh2jUqBEP1kdkYDjERFIkJibCyclJdgwiKgfXIEgKc3NzzJ49G7GxsSgs\nLNReHxkZKTEVEZXGg/WRFEFBQejYsSNu3LiBhQsXonXr1ujWrZvsWERUCoeYSIquXbvi3LlzcHd3\nR0xMDACgW7duOHPmjORkRPQAh5hIigfnf7C3t8e+ffvg4OCAu3fvSk5FRKWxIEiKefPmISsrC6tW\nrcLMmTORnZ2N1atXy45FRKVwiImIiBRxDYL06v333y/zNpVKhfnz5+sxDRGVh2sQpFerVq165Lq8\nvDysX78ed+7cQW5uroRURKSEBUHS5OTkIDQ0FOvXr0dgYCBmzZoFW1tb2bGI6P9xPwjSu7t372Le\nvHlwd3dHSUkJoqOjsWzZMpYDkYHhHATp1ezZs/Hdd99h2rRpuHTpEho2bCg7EhGVgUNMpFdGRkYw\nM1RvNwYAAAJqSURBVDODiYmJzgmChBBQqVTIzs6WmI6ISmNBEBGRIs5BEBGRIhYEEREpYkEQEZEi\nFgQRESliQRD9Q8bGxujSpQvc3d3x7LPPIi8vT3YkoirFgiD6hywsLBAdHY2YmBhYWlris88+kx2J\nqEqxIIiqQK9evZCQkADg/rGlBg0ahCeffBIeHh7YvXu39n5fffUVPDw84OXlheDgYABAeno6Ro8e\njR49eqBHjx44ceKElGUgehj3gyD6hywtLZGTkwO1Wo2xY8fC19cXL7/8MtRqNQoKCtCwYUPcuXMH\nPXv2xLVr1xAbG4tnn30Wv/zyC6ytrZGZmYnGjRsjKCgIr7zyCnr37o2bN2/C398fly9flr14RCwI\non/KxMQE7u7uSE5OhrOzM3755RcYGRmhpKQEb7zxBo4ePQojIyNcvXoVN27cwI4dO5CamooPPvhA\n53ns7OzQokULPPiveOfOHcTFxcHc3FzGYhFp8VhMRP+Qubk5oqOjUVhYCH9/f+zevRsjR47Eli1b\nkJ6ejvPnz8PIyAjOzs4oLCwEACj9PiaEwKlTp7SnYSUyFJyDIPqHHnzZ169fH6GhoZg7dy4AICsr\nC7a2tjAyMsLhw4eRmJgIAPD19cXOnTu1597OyMgAAPj5+SE0NFT7vBcvXtTnYhCViQVB9A+VPtig\np6cn2rVrh/DwcAQFBeHMmTPw8PBAWFgYXFxcAACurq5477330L9/f3h5eWHWrFkAgNDQUJw9exYe\nHh7o3Lkzt4Yig8E5CCIiUsQ1CCIiUsSCICIiRSwIIiJSxIIgIiJFLAgiIlLEgiAiIkUsCCIiUvR/\nk+//HFvWtjgAAAAASUVORK5CYII=\n",
					"text/plain": [
						"<matplotlib.figure.Figure at 0x7fa3160dd8d0>"
					]
				},
				"metadata": {},
				"output_type": "display_data"
			}],
			"source": [
				"homicide_race_counts_per_hundredk_name = homicide_race_counts_per_hundredk.keys()\n",
				"homicide_race_counts_per_hundredk_values = homicide_race_counts_per_hundredk.values()\n",
				"homicide_race_counts_per_hundredk_range = range(len(homicide_race_counts_per_hundredk))\n",
				"\n",
				"plt.bar(homicide_race_counts_per_hundredk_range, homicide_race_counts_per_hundredk_values, \n",
				"        tick_label=homicide_race_counts_per_hundredk_name , align='center',\n",
				"       color=('seagreen', 'palevioletred'))\n",
				"plt.xticks(rotation=90)\n",
				"plt.title('Homicide per hundredk grouped by race in % \\n')\n",
				"plt.xlabel('Race')\n",
				"plt.ylabel('Relative number (%) \\n')\n",
				"plt.show()"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {
				"collapsed": true
			},
			"outputs": [],
			"source": []
		},
		{
			"cell_type": "code",
			"execution_count": 24,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"['', 'year', 'month', 'intent', 'police', 'sex', 'age', 'race', 'hispanic', 'place', 'education']\n"
				]
			}],
			"source": [
				"print(headers)"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 25,
			"metadata": {
				"collapsed": true
			},
			"outputs": [],
			"source": [
				"import numpy as np\n",
				"from sklearn import datasets, linear_model\n",
				"from sklearn.metrics import mean_squared_error, r2_score\n",
				"import pandas as pd"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 26,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"      year month   intent police sex age                    race hispanic  \\\n",
					"0  1  2012    01  Suicide      0   M  34  Asian/Pacific Islander      100   \n",
					"1  2  2012    01  Suicide      0   F  21                   White      100   \n",
					"2  3  2012    01  Suicide      0   M  60                   White      100   \n",
					"3  4  2012    02  Suicide      0   M  64                   White      100   \n",
					"4  5  2012    02  Suicide      0   M  31                   White      100   \n",
					"\n",
					"             place education  \n",
					"0             Home         4  \n",
					"1           Street         3  \n",
					"2  Other specified         4  \n",
					"3             Home         4  \n",
					"4  Other specified         2  \n",
					"                year month    intent police sex age      race hispanic  \\\n",
					"100793  100794  2014    12  Homicide      0   M  36     Black      100   \n",
					"100794  100795  2014    12  Homicide      0   M  19     Black      100   \n",
					"100795  100796  2014    12  Homicide      0   M  20     Black      100   \n",
					"100796  100797  2014    12  Homicide      0   M  22  Hispanic      260   \n",
					"100797  100798  2014    10  Homicide      0   M  43     Black      100   \n",
					"\n",
					"                    place education  \n",
					"100793               Home         2  \n",
					"100794             Street         2  \n",
					"100795             Street         2  \n",
					"100796             Street         1  \n",
					"100797  Other unspecified         2  \n",
					"                  year   month   intent  police     sex     age    race  \\\n",
					"count   100798  100798  100798   100798  100798  100798  100798  100798   \n",
					"unique  100798       3      12        5       2       2     105       5   \n",
					"top      32628    2013      07  Suicide       0       M      22   White   \n",
					"freq         1   33636    8989    63175   99396   86349    2712   66237   \n",
					"\n",
					"       hispanic   place education  \n",
					"count    100798  100798    100798  \n",
					"unique       39      11         6  \n",
					"top         100    Home         2  \n",
					"freq      91467   60486     42927  \n"
				]
			}],
			"source": [
				"df=pd.DataFrame(data, columns=headers)\n",
				"print(df.head())\n",
				"print(df.tail())\n",
				"print(df.describe())"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 27,
			"metadata": {
				"collapsed": false
			},
			"outputs": [],
			"source": [
				"df_itent_race=df.iloc[:,[3,7]]\n",
				"\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 28,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"    intent                    race\n",
					"0  Suicide  Asian/Pacific Islander\n",
					"1  Suicide                   White\n",
					"2  Suicide                   White\n",
					"3  Suicide                   White\n",
					"4  Suicide                   White\n"
				]
			}],
			"source": [
				"print(df_itent_race.head())"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 29,
			"metadata": {
				"collapsed": false
			},
			"outputs": [],
			"source": [
				"\n",
				"df_itent_race_2 = []\n",
				"for i, row in df_itent_race.iterrows():\n",
				"    if df_itent_race.loc[i, 'intent'] == \"Homicide\":\n",
				"        \n",
				"        df_itent_race_2.append(1) \n",
				"\n",
				"    else:\n",
				"        \n",
				"        df_itent_race_2.append(0) \n",
				"    \n",
				"    "
			]
		},
		{
			"cell_type": "code",
			"execution_count": 30,
			"metadata": {
				"collapsed": false
			},
			"outputs": [],
			"source": [
				"df_itent_race.is_copy = False\n",
				"df_itent_race[\"Dummy\"] = df_itent_race_2\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 31,
			"metadata": {
				"collapsed": false
			},
			"outputs": [{
				"name": "stdout",
				"output_type": "stream",
				"text": [
					"          intent                            race  Dummy\n",
					"0        Suicide          Asian/Pacific Islander      0\n",
					"1        Suicide                           White      0\n",
					"2        Suicide                           White      0\n",
					"3        Suicide                           White      0\n",
					"4        Suicide                           White      0\n",
					"5        Suicide  Native American/Native Alaskan      0\n",
					"6   Undetermined                           White      0\n",
					"7        Suicide  Native American/Native Alaskan      0\n",
					"8     Accidental                           White      0\n",
					"9        Suicide                           Black      0\n",
					"10       Suicide                           White      0\n",
					"11       Suicide  Native American/Native Alaskan      0\n",
					"12       Suicide                           White      0\n",
					"13       Suicide  Native American/Native Alaskan      0\n",
					"14      Homicide                           White      1\n",
					"15       Suicide  Native American/Native Alaskan      0\n",
					"16       Suicide                           White      0\n",
					"17       Suicide  Native American/Native Alaskan      0\n",
					"18      Homicide          Asian/Pacific Islander      1\n",
					"19       Suicide                           White      0\n",
					"20       Suicide  Native American/Native Alaskan      0\n",
					"21       Suicide                           White      0\n",
					"22      Homicide                           Black      1\n",
					"23       Suicide                           White      0\n",
					"24      Homicide                           White      1\n",
					"25      Homicide                           White      1\n",
					"26       Suicide  Native American/Native Alaskan      0\n",
					"27       Suicide                           White      0\n",
					"28       Suicide                           White      0\n",
					"29      Homicide                           White      1\n",
					"30       Suicide  Native American/Native Alaskan      0\n",
					"31       Suicide                           White      0\n",
					"32       Suicide  Native American/Native Alaskan      0\n",
					"33       Suicide                           White      0\n",
					"34      Homicide                           White      1\n",
					"35  Undetermined                           White      0\n",
					"36       Suicide  Native American/Native Alaskan      0\n",
					"37       Suicide  Native American/Native Alaskan      0\n",
					"38       Suicide                           White      0\n",
					"39       Suicide                           White      0\n",
					"40       Suicide  Native American/Native Alaskan      0\n",
					"41      Homicide          Asian/Pacific Islander      1\n",
					"42       Suicide                           White      0\n",
					"43       Suicide  Native American/Native Alaskan      0\n",
					"44       Suicide  Native American/Native Alaskan      0\n",
					"45       Suicide                           White      0\n",
					"46       Suicide                           White      0\n",
					"47      Homicide                           White      1\n",
					"48      Homicide  Native American/Native Alaskan      1\n",
					"49      Homicide          Asian/Pacific Islander      1\n"
				]
			}],
			"source": [
				"print(df_itent_race[:50])  "
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {
				"collapsed": true
			},
			"outputs": [],
			"source": []
		}
	],
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			"display_name": "Python 3",
			"language": "python",
			"name": "python3"
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			"codemirror_mode": {
				"name": "ipython",
				"version": 3
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			"file_extension": ".py",
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