In [4]:

    

import csv

with open("guns.csv", "r") as f:
    reader = csv.reader(f)
    data = list(reader)


    

In [5]:

    

print(data[:5])


    

    




[['', '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']]

In [6]:

    

headers = data[:1]
data = data[1:]
print(headers)
print(data[:5])


    

    




[['', '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'], ['5', '2012', '02', 'Suicide', '0', 'M', '31', 'White', '100', 'Other specified', '2']]

In [7]:

    

years = [row[1] for row in data]

year_counts = {}
for year in years:
    if year not in year_counts:
        year_counts[year] = 0
    year_counts[year] += 1

year_counts


    

    
Out[7]:





{'2012': 33563, '2013': 33636, '2014': 33599}

In [8]:

    

import datetime
dates = [datetime.datetime(year=int(row[1]), month=int(row[2]), day=1) for row in data]
dates[:5]


    

    
Out[8]:





[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)]

In [9]:

    

date_counts = {}

for date in dates:
    if date not in date_counts:
        date_counts[date] = 0
    date_counts[date] += 1

date_counts


    

    
Out[9]:





{datetime.datetime(2012, 1, 1, 0, 0): 2758,
 datetime.datetime(2012, 2, 1, 0, 0): 2357,
 datetime.datetime(2012, 3, 1, 0, 0): 2743,
 datetime.datetime(2012, 4, 1, 0, 0): 2795,
 datetime.datetime(2012, 5, 1, 0, 0): 2999,
 datetime.datetime(2012, 6, 1, 0, 0): 2826,
 datetime.datetime(2012, 7, 1, 0, 0): 3026,
 datetime.datetime(2012, 8, 1, 0, 0): 2954,
 datetime.datetime(2012, 9, 1, 0, 0): 2852,
 datetime.datetime(2012, 10, 1, 0, 0): 2733,
 datetime.datetime(2012, 11, 1, 0, 0): 2729,
 datetime.datetime(2012, 12, 1, 0, 0): 2791,
 datetime.datetime(2013, 1, 1, 0, 0): 2864,
 datetime.datetime(2013, 2, 1, 0, 0): 2375,
 datetime.datetime(2013, 3, 1, 0, 0): 2862,
 datetime.datetime(2013, 4, 1, 0, 0): 2798,
 datetime.datetime(2013, 5, 1, 0, 0): 2806,
 datetime.datetime(2013, 6, 1, 0, 0): 2920,
 datetime.datetime(2013, 7, 1, 0, 0): 3079,
 datetime.datetime(2013, 8, 1, 0, 0): 2859,
 datetime.datetime(2013, 9, 1, 0, 0): 2742,
 datetime.datetime(2013, 10, 1, 0, 0): 2808,
 datetime.datetime(2013, 11, 1, 0, 0): 2758,
 datetime.datetime(2013, 12, 1, 0, 0): 2765,
 datetime.datetime(2014, 1, 1, 0, 0): 2651,
 datetime.datetime(2014, 2, 1, 0, 0): 2361,
 datetime.datetime(2014, 3, 1, 0, 0): 2684,
 datetime.datetime(2014, 4, 1, 0, 0): 2862,
 datetime.datetime(2014, 5, 1, 0, 0): 2864,
 datetime.datetime(2014, 6, 1, 0, 0): 2931,
 datetime.datetime(2014, 7, 1, 0, 0): 2884,
 datetime.datetime(2014, 8, 1, 0, 0): 2970,
 datetime.datetime(2014, 9, 1, 0, 0): 2914,
 datetime.datetime(2014, 10, 1, 0, 0): 2865,
 datetime.datetime(2014, 11, 1, 0, 0): 2756,
 datetime.datetime(2014, 12, 1, 0, 0): 2857}

In [10]:

    

sexes = [row[5] for row in data]
sex_counts = {}
for sex in sexes:
    if sex not in sex_counts:
        sex_counts[sex] = 0
    sex_counts[sex] += 1
sex_counts


    

    
Out[10]:





{'F': 14449, 'M': 86349}

In [11]:

    

races = [row[7] for row in data]
race_counts = {}
for race in races:
    if race not in race_counts:
        race_counts[race] = 0
    race_counts[race] += 1
race_counts


    

    
Out[11]:





{'Asian/Pacific Islander': 1326,
 'Black': 23296,
 'Hispanic': 9022,
 'Native American/Native Alaskan': 917,
 'White': 66237}

In [12]:

    

import csv

with open("census.csv", "r") as f:
    reader = csv.reader(f)
    census = list(reader)
    
census


    

    
Out[12]:





[['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']]

In [13]:

    

mapping = {
    "Asian/Pacific Islander": 15159516 + 674625,
    "Native American/Native Alaskan": 3739506,
    "Black": 40250635,
    "Hispanic": 44618105,
    "White": 197318956
}

race_per_hundredk = {}
for k,v in race_counts.items():
    race_per_hundredk[k] = (v / mapping[k]) * 100000

race_per_hundredk


    

    
Out[13]:





{'Asian/Pacific Islander': 8.374309664161762,
 'Black': 57.8773477735196,
 'Hispanic': 20.220491210910907,
 'Native American/Native Alaskan': 24.521955573811088,
 'White': 33.56849303419181}

In [14]:

    

intents = [row[3] for row in data]
homicide_race_counts = {}
for i,race in enumerate(races):
    if race not in homicide_race_counts:
        homicide_race_counts[race] = 0
    if intents[i] == "Homicide":
        homicide_race_counts[race] += 1

race_per_hundredk = {}
for k,v in homicide_race_counts.items():
    race_per_hundredk[k] = (v / mapping[k]) * 100000

race_per_hundredk


    

    
Out[14]:





{'Asian/Pacific Islander': 3.530346230970155,
 'Black': 48.471284987180944,
 'Hispanic': 12.627161104219914,
 'Native American/Native Alaskan': 8.717729026240365,
 'White': 4.6356417981453335}

In [ ]: