Guided Project: Exploring Gun Deaths in the US
Posted on Wed 08 July 2015 in Projects
US Gun Deaths Guided Project Solutions¶
Introducing US Gun Deaths Data¶
In [30]:
import csv
with open("guns.csv", "r") as f:
reader = csv.reader(f)
data = list(reader)
In [31]:
print(data[:5])
Removing Headers From A List Of Lists¶
In [32]:
headers = data[:1]
data = data[1:]
print(headers)
print(data[:5])
Counting Gun Deaths By Year¶
In [33]:
years = [row[1] for row in data]
year_counts = {}
for year in years:
if year not in year_counts:
year_counts[year] = 1
else:
year_counts[year] += 1
year_counts
Out[33]:
Exploring Gun Deaths By Month And Year¶
In [34]:
import datetime
dates = [datetime.datetime(year=int(row[1]), month=int(row[2]), day=1) for row in data]
dates[:5]
Out[34]:
In [35]:
date_counts = {}
for date in dates:
if date not in date_counts:
date_counts[date] = 0
date_counts[date] += 1
date_counts
Out[35]:
Exploring Gun Deaths By Race And Sex¶
In [54]:
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[54]:
In [36]:
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[36]:
Findings so far¶
Gun deaths in the US seem to disproportionately affect men vs women. They also seem to disproportionately affect minorities, although having some data on the percentage of each race in the overall US population would help.
There appears to be a minor seasonal correlation, with gun deaths peaking in the summer and declining in the winter. It might be useful to filter by intent, to see if different categories of intent have different correlations with season, race, or gender.
Reading In A Second Dataset¶
In [57]:
import csv
with open("census.csv", "r") as f:
reader = csv.reader(f)
census = list(reader)
census
Out[57]:
Computing Rates Of Gun Deaths Per Race¶
In [40]:
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[40]:
Filtering By Intent¶
In [41]:
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[41]:
Findings¶
It appears that gun related homicides in the US disproportionately affect people in the Black and Hispanic racial categories.
Some areas to investigate further:
- The link between month and homicide rate.
- Homicide rate by gender.
- The rates of other intents by gender and race.
- Gun death rates by location and education.