Project: Investigate a Dataset (FBI Gun Data)¶

Table of Contents¶

  • Introduction
  • Data Wrangling
  • Exploratory Data Analysis
  • Conclusions

Introduction¶

The data comes from the FBI's National Instant Criminal Background Check System. The NICS is used by ammunition merchants to determine whether a prospective buyer is eligible to buy firearms or explosives. Guns shops call into this system to ensure that each customer does not have a criminal record or isn't otherrwise ineligible to make a purchase. The data has been supplemented by state level data from census.gov.

The NICS data is found in one sheet of an .xlsx file. It contains the number of firearm checks by month, state, and type.

The U.S. census data is found in a .csv file. It contains several variables at the state level. Most variables just have one data point per state (2016), but a few have data for more than one year.

Questions to Answer:¶

  1. Does Population estimates in States correlate with total gun registration and purchases?
  2. Is there relation between total gun registration and purchases and the percentage of various races in the given states?
  3. Which States had the highest growth in gun registration and purchases?
  4. What is the overall trend of gun purchases?
In [222]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
sns.set(font_scale=2)

Data Wrangling¶

General Properties¶

In [8]:
# Load your data and print out a few lines. Perform operations to inspect data
#   types and look for instances of missing or possibly errant data.
df_census = pd.read_csv('U.S. Census Data.csv')
df_guns = pd.read_excel("gun_data.xlsx")
df_census.head()
Out[8]:
Fact Fact Note Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware ... South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
0 Population estimates, July 1, 2016, (V2016) NaN 4,863,300 741,894 6,931,071 2,988,248 39,250,017 5,540,545 3,576,452 952,065 ... 865454 6651194 27,862,596 3,051,217 624,594 8,411,808 7,288,000 1,831,102 5,778,708 585,501
1 Population estimates base, April 1, 2010, (V2... NaN 4,780,131 710,249 6,392,301 2,916,025 37,254,522 5,029,324 3,574,114 897,936 ... 814195 6346298 25,146,100 2,763,888 625,741 8,001,041 6,724,545 1,853,011 5,687,289 563,767
2 Population, percent change - April 1, 2010 (es... NaN 1.70% 4.50% 8.40% 2.50% 5.40% 10.20% 0.10% 6.00% ... 0.063 0.048 10.80% 10.40% -0.20% 5.10% 8.40% -1.20% 1.60% 3.90%
3 Population, Census, April 1, 2010 NaN 4,779,736 710,231 6,392,017 2,915,918 37,253,956 5,029,196 3,574,097 897,934 ... 814180 6346105 25,145,561 2,763,885 625,741 8,001,024 6,724,540 1,852,994 5,686,986 563,626
4 Persons under 5 years, percent, July 1, 2016, ... NaN 6.00% 7.30% 6.30% 6.40% 6.30% 6.10% 5.20% 5.80% ... 0.071 0.061 7.20% 8.30% 4.90% 6.10% 6.20% 5.50% 5.80% 6.50%

5 rows × 52 columns

In [10]:
df_guns.head()
Out[10]:
month state permit permit_recheck handgun long_gun other multiple admin prepawn_handgun ... returned_other rentals_handgun rentals_long_gun private_sale_handgun private_sale_long_gun private_sale_other return_to_seller_handgun return_to_seller_long_gun return_to_seller_other totals
0 2017-09 Alabama 16717.0 0.0 5734.0 6320.0 221.0 317 0.0 15.0 ... 0.0 0.0 0.0 9.0 16.0 3.0 0.0 0.0 3.0 32019
1 2017-09 Alaska 209.0 2.0 2320.0 2930.0 219.0 160 0.0 5.0 ... 0.0 0.0 0.0 17.0 24.0 1.0 0.0 0.0 0.0 6303
2 2017-09 Arizona 5069.0 382.0 11063.0 7946.0 920.0 631 0.0 13.0 ... 0.0 0.0 0.0 38.0 12.0 2.0 0.0 0.0 0.0 28394
3 2017-09 Arkansas 2935.0 632.0 4347.0 6063.0 165.0 366 51.0 12.0 ... 0.0 0.0 0.0 13.0 23.0 0.0 0.0 2.0 1.0 17747
4 2017-09 California 57839.0 0.0 37165.0 24581.0 2984.0 0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 123506

5 rows × 27 columns

In [11]:
df_guns.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12485 entries, 0 to 12484
Data columns (total 27 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   month                      12485 non-null  object 
 1   state                      12485 non-null  object 
 2   permit                     12461 non-null  float64
 3   permit_recheck             1100 non-null   float64
 4   handgun                    12465 non-null  float64
 5   long_gun                   12466 non-null  float64
 6   other                      5500 non-null   float64
 7   multiple                   12485 non-null  int64  
 8   admin                      12462 non-null  float64
 9   prepawn_handgun            10542 non-null  float64
 10  prepawn_long_gun           10540 non-null  float64
 11  prepawn_other              5115 non-null   float64
 12  redemption_handgun         10545 non-null  float64
 13  redemption_long_gun        10544 non-null  float64
 14  redemption_other           5115 non-null   float64
 15  returned_handgun           2200 non-null   float64
 16  returned_long_gun          2145 non-null   float64
 17  returned_other             1815 non-null   float64
 18  rentals_handgun            990 non-null    float64
 19  rentals_long_gun           825 non-null    float64
 20  private_sale_handgun       2750 non-null   float64
 21  private_sale_long_gun      2750 non-null   float64
 22  private_sale_other         2750 non-null   float64
 23  return_to_seller_handgun   2475 non-null   float64
 24  return_to_seller_long_gun  2750 non-null   float64
 25  return_to_seller_other     2255 non-null   float64
 26  totals                     12485 non-null  int64  
dtypes: float64(23), int64(2), object(2)
memory usage: 2.6+ MB
In [6]:
df_census.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 85 entries, 0 to 84
Data columns (total 52 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Fact            80 non-null     object
 1   Fact Note       28 non-null     object
 2   Alabama         65 non-null     object
 3   Alaska          65 non-null     object
 4   Arizona         65 non-null     object
 5   Arkansas        65 non-null     object
 6   California      65 non-null     object
 7   Colorado        65 non-null     object
 8   Connecticut     65 non-null     object
 9   Delaware        65 non-null     object
 10  Florida         65 non-null     object
 11  Georgia         65 non-null     object
 12  Hawaii          65 non-null     object
 13  Idaho           65 non-null     object
 14  Illinois        65 non-null     object
 15  Indiana         65 non-null     object
 16  Iowa            65 non-null     object
 17  Kansas          65 non-null     object
 18  Kentucky        65 non-null     object
 19  Louisiana       65 non-null     object
 20  Maine           65 non-null     object
 21  Maryland        65 non-null     object
 22  Massachusetts   65 non-null     object
 23  Michigan        65 non-null     object
 24  Minnesota       65 non-null     object
 25  Mississippi     65 non-null     object
 26  Missouri        65 non-null     object
 27  Montana         65 non-null     object
 28  Nebraska        65 non-null     object
 29  Nevada          65 non-null     object
 30  New Hampshire   65 non-null     object
 31  New Jersey      65 non-null     object
 32  New Mexico      65 non-null     object
 33  New York        65 non-null     object
 34  North Carolina  65 non-null     object
 35  North Dakota    65 non-null     object
 36  Ohio            65 non-null     object
 37  Oklahoma        65 non-null     object
 38  Oregon          65 non-null     object
 39  Pennsylvania    65 non-null     object
 40  Rhode Island    65 non-null     object
 41  South Carolina  65 non-null     object
 42  South Dakota    65 non-null     object
 43  Tennessee       65 non-null     object
 44  Texas           65 non-null     object
 45  Utah            65 non-null     object
 46  Vermont         65 non-null     object
 47  Virginia        65 non-null     object
 48  Washington      65 non-null     object
 49  West Virginia   65 non-null     object
 50  Wisconsin       65 non-null     object
 51  Wyoming         65 non-null     object
dtypes: object(52)
memory usage: 34.7+ KB
In [7]:
# check for duplicate records in census data
df_census[df_census.duplicated()]
Out[7]:
Fact Fact Note Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware ... South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
67 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
70 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
75 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

3 rows × 52 columns

In [12]:
#check for duplicate rows in gun_data
df_guns[df_guns.duplicated()]
Out[12]:
month state permit permit_recheck handgun long_gun other multiple admin prepawn_handgun ... returned_other rentals_handgun rentals_long_gun private_sale_handgun private_sale_long_gun private_sale_other return_to_seller_handgun return_to_seller_long_gun return_to_seller_other totals

0 rows × 27 columns

Data Assesment¶

Quality Issues:¶

gun_data.xlsx

  • month column data type is object instead of datetime
  • state column data type is object instead of category
  • missing data on the various columns depicting types of guns
  • the various columns depicting types of guns have data types of float64 instead of int64
Tidiness Issues:¶

U.S. Census Data.csv

  • the structure of the U.S.Census Data is messy as data of different types are the same columns

Data Cleaning¶

In [43]:
#change the data type of gun_data month column to datetime
df_guns['month'] = pd.to_datetime(df_guns['month'])
df_guns['month'].head()
Out[43]:
0   2017-09-01
1   2017-09-01
2   2017-09-01
3   2017-09-01
4   2017-09-01
Name: month, dtype: datetime64[ns]
In [48]:
#fill in missing values with 0s
df_guns.fillna(0, inplace=True)
df_guns.tail()
Out[48]:
month state permit permit_recheck handgun long_gun other multiple admin prepawn_handgun ... returned_other rentals_handgun rentals_long_gun private_sale_handgun private_sale_long_gun private_sale_other return_to_seller_handgun return_to_seller_long_gun return_to_seller_other totals
12480 1998-11-01 Virginia 0.0 0.0 14.0 2.0 0.0 8 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24
12481 1998-11-01 Washington 1.0 0.0 65.0 286.0 0.0 8 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 361
12482 1998-11-01 West Virginia 3.0 0.0 149.0 251.0 0.0 5 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 408
12483 1998-11-01 Wisconsin 0.0 0.0 25.0 214.0 0.0 2 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 241
12484 1998-11-01 Wyoming 8.0 0.0 45.0 49.0 0.0 5 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 107

5 rows × 27 columns

In [49]:
#change the data type of gun_data gun types columns to int64
int_cols = [col for col in df_guns.columns if col not in ['month', 'state']]

for col in int_cols:
    df_guns[col] = df_guns[col].astype('int64')
df_guns.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12485 entries, 0 to 12484
Data columns (total 27 columns):
 #   Column                     Non-Null Count  Dtype         
---  ------                     --------------  -----         
 0   month                      12485 non-null  datetime64[ns]
 1   state                      12485 non-null  object        
 2   permit                     12485 non-null  int64         
 3   permit_recheck             12485 non-null  int64         
 4   handgun                    12485 non-null  int64         
 5   long_gun                   12485 non-null  int64         
 6   other                      12485 non-null  int64         
 7   multiple                   12485 non-null  int64         
 8   admin                      12485 non-null  int64         
 9   prepawn_handgun            12485 non-null  int64         
 10  prepawn_long_gun           12485 non-null  int64         
 11  prepawn_other              12485 non-null  int64         
 12  redemption_handgun         12485 non-null  int64         
 13  redemption_long_gun        12485 non-null  int64         
 14  redemption_other           12485 non-null  int64         
 15  returned_handgun           12485 non-null  int64         
 16  returned_long_gun          12485 non-null  int64         
 17  returned_other             12485 non-null  int64         
 18  rentals_handgun            12485 non-null  int64         
 19  rentals_long_gun           12485 non-null  int64         
 20  private_sale_handgun       12485 non-null  int64         
 21  private_sale_long_gun      12485 non-null  int64         
 22  private_sale_other         12485 non-null  int64         
 23  return_to_seller_handgun   12485 non-null  int64         
 24  return_to_seller_long_gun  12485 non-null  int64         
 25  return_to_seller_other     12485 non-null  int64         
 26  totals                     12485 non-null  int64         
dtypes: datetime64[ns](1), int64(25), object(1)
memory usage: 2.6+ MB
In [51]:
#change the data type of gun_data state column to category
df_guns['state'] = df_guns['state'].astype('category')
df_guns['state'].info()

<class 'pandas.core.series.Series'>
RangeIndex: 12485 entries, 0 to 12484
Series name: state
Non-Null Count  Dtype   
--------------  -----   
12485 non-null  category
dtypes: category(1)
memory usage: 14.8 KB
In [15]:
#Remove the Fact Note column of U.S. Census Data
df_census.drop(axis=1, columns=['Fact Note'], inplace = True )
df_census.head()
Out[15]:
Fact Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida ... South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
0 Population estimates, July 1, 2016, (V2016) 4,863,300 741,894 6,931,071 2,988,248 39,250,017 5,540,545 3,576,452 952,065 20,612,439 ... 865454 6651194 27,862,596 3,051,217 624,594 8,411,808 7,288,000 1,831,102 5,778,708 585,501
1 Population estimates base, April 1, 2010, (V2... 4,780,131 710,249 6,392,301 2,916,025 37,254,522 5,029,324 3,574,114 897,936 18,804,592 ... 814195 6346298 25,146,100 2,763,888 625,741 8,001,041 6,724,545 1,853,011 5,687,289 563,767
2 Population, percent change - April 1, 2010 (es... 1.70% 4.50% 8.40% 2.50% 5.40% 10.20% 0.10% 6.00% 9.60% ... 0.063 0.048 10.80% 10.40% -0.20% 5.10% 8.40% -1.20% 1.60% 3.90%
3 Population, Census, April 1, 2010 4,779,736 710,231 6,392,017 2,915,918 37,253,956 5,029,196 3,574,097 897,934 18,801,310 ... 814180 6346105 25,145,561 2,763,885 625,741 8,001,024 6,724,540 1,852,994 5,686,986 563,626
4 Persons under 5 years, percent, July 1, 2016, ... 6.00% 7.30% 6.30% 6.40% 6.30% 6.10% 5.20% 5.80% 5.50% ... 0.071 0.061 7.20% 8.30% 4.90% 6.10% 6.20% 5.50% 5.80% 6.50%

5 rows × 51 columns

In [19]:
#Remove U.S. Census Data rows that are irrelevant to the analysis
df_census.drop(df_census.index[65:], axis=0, inplace=True)
df_census.tail()
Out[19]:
Fact Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida ... South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
60 Veteran-owned firms, 2012 41,943 7,953 46,780 25,915 252,377 51,722 31,056 7,206 185,756 ... 8604 59379 213,590 18,754 8,237 76,434 49,331 12,912 39,830 6,470
61 Nonveteran-owned firms, 2012 316,984 56,091 427,582 192,988 3,176,341 469,524 281,182 60,318 1,846,686 ... 66219 469392 2,057,218 219,807 63,317 548,439 461,401 94,960 370,755 51,353
62 Population per square mile, 2010 94.4 1.2 56.3 56 239.1 48.5 738.1 460.8 350.6 ... 10.7 153.9 96.3 33.6 67.9 202.6 101.2 77.1 105 5.8
63 Land area in square miles, 2010 50,645.33 570,640.95 113,594.08 52,035.48 155,779.22 103,641.89 4,842.36 1,948.54 53,624.76 ... 75811 41234.9 261,231.71 82,169.62 9,216.66 39,490.09 66,455.52 24,038.21 54,157.80 97,093.14
64 FIPS Code "01" "02" "04" "05" "06" "08" "09" "10" "12" ... "46" "47" "48" "49" "50" "51" "53" "54" "55" "56"

5 rows × 51 columns

In [37]:
#Transpose U.S. Census Data table and change the header column
df_census2 = df_census.transpose()
df_census2.columns = df_census2.iloc[0]
df_census2 = df_census2.iloc[1:].reset_index()
#rename index column to State
df_census2.rename(columns = {'index':'State'}, inplace = True)
df_census2.head()
Out[37]:
Fact State Population estimates, July 1, 2016, (V2016) Population estimates base, April 1, 2010, (V2016) Population, percent change - April 1, 2010 (estimates base) to July 1, 2016, (V2016) Population, Census, April 1, 2010 Persons under 5 years, percent, July 1, 2016, (V2016) Persons under 5 years, percent, April 1, 2010 Persons under 18 years, percent, July 1, 2016, (V2016) Persons under 18 years, percent, April 1, 2010 Persons 65 years and over, percent, July 1, 2016, (V2016) ... All firms, 2012 Men-owned firms, 2012 Women-owned firms, 2012 Minority-owned firms, 2012 Nonminority-owned firms, 2012 Veteran-owned firms, 2012 Nonveteran-owned firms, 2012 Population per square mile, 2010 Land area in square miles, 2010 FIPS Code
0 Alabama 4,863,300 4,780,131 1.70% 4,779,736 6.00% 6.40% 22.60% 23.70% 16.10% ... 374,153 203,604 137,630 92,219 272,651 41,943 316,984 94.4 50,645.33 "01"
1 Alaska 741,894 710,249 4.50% 710,231 7.30% 7.60% 25.20% 26.40% 10.40% ... 68,032 35,402 22,141 13,688 51,147 7,953 56,091 1.2 570,640.95 "02"
2 Arizona 6,931,071 6,392,301 8.40% 6,392,017 6.30% 7.10% 23.50% 25.50% 16.90% ... 499,926 245,243 182,425 135,313 344,981 46,780 427,582 56.3 113,594.08 "04"
3 Arkansas 2,988,248 2,916,025 2.50% 2,915,918 6.40% 6.80% 23.60% 24.40% 16.30% ... 231,959 123,158 75,962 35,982 189,029 25,915 192,988 56 52,035.48 "05"
4 California 39,250,017 37,254,522 5.40% 37,253,956 6.30% 6.80% 23.20% 25.00% 13.60% ... 3,548,449 1,852,580 1,320,085 1,619,857 1,819,107 252,377 3,176,341 239.1 155,779.22 "06"

5 rows × 66 columns

Exploratory Data Analysis¶

In [287]:
#plot a histogram to showcase the distribution of population estimates
plt.hist(data=df_census2, x='Population estimates, July 1, 2016,  (V2016)');
plt.title('July 1, 2016 Population estimates Histogram')
plt.xlabel('Population estimates')
plt.ylabel('Frequency');
  • The distribution of July 1, 2016 Population estimates is right-skewed.
In [293]:
#plot a histogram to showcase the distribution of Gun totals
bins = np.arange(0, df_guns['totals'].max()+5, 50000)
plt.hist(data=df_guns, x='totals');
plt.title('Gun Totals Histogram')
plt.xlabel('Gun Totals')
plt.ylabel('Frequency');
  • The distribution of Gun Totals is right-skewed.
In [291]:
#plot a histogram to showcase the distribution of Gun totals using xlim 
plt.figure(figsize=(5,5))
bins = np.arange(0, df_guns['totals'].max()+5, 10000)
plt.hist(data=df_guns, x='totals', bins=bins);
plt.xlim(0, 100000)
plt.xticks(rotation=90);
plt.title('Gun Totals Histogram')
plt.xlabel('Gun Totals')
plt.ylabel('Frequency');
  • Zooming in on the distribution of the gun totals depicts that majority of the data points are between 0 and 20,000
In [62]:
#compute summarry statistics of the totals column
df_guns[['totals']].describe()
Out[62]:
totals
count 12485.000000
mean 21595.725911
std 32591.418387
min 0.000000
25% 4638.000000
50% 12399.000000
75% 25453.000000
max 541978.000000
In [301]:
#plot a bar graph to showcase Gun totals in each State in July 2016
order = df_mergedJuly.sort_values('totals', ascending=False).state
plt.figure(figsize=(25,15))
base_color = sns.color_palette()[0]
sns.barplot(data=df_mergedJuly, x= 'totals', y='state', color=base_color, order=order)
plt.yticks(size=16)
plt.xticks(size=16)
plt.ylabel('States', size=18)
plt.xlabel('July 2016 Gun Totals', size=18)
plt.title('Bar Graph of July 2016 Gun Totals in States', size=20);
  • The state of Kentucky had the by far the highest gun totals compared to other states followed by the states of Carlifonia, Illinois, Texas and Florida respectively.
  • The states of Hawaii, Rhode Island, Vermont and Delaware had the lowest gun totals respectively.
In [303]:
#plot a bar graph to showcase Population estimates in each State July 2016
order = df_mergedJuly.sort_values('Population estimates, July 1, 2016,  (V2016)', ascending=False).state
plt.figure(figsize=(25,15))
base_color = sns.color_palette()[0]
sns.barplot(data=df_mergedJuly, x= 'Population estimates, July 1, 2016,  (V2016)', y='state', color=base_color, order=order)
plt.ylabel('States', size=18)
plt.xlabel('July 2016 Population', size=18)
plt.yticks(size=16)
plt.xticks(size=16)
plt.title('Bar Graph of July 2016 Population estimates in States', size=20);
  • The state of Carlifonia had the highest population estimates compared to other states followed by the states of Texas, Florida, New York and Illinois respectively.
  • The states of Wyoming, Vermont, Alaska and North Dakota had the lowest population estimates respectively.
In [315]:
#Subset U.S Census Data to obtain population estimates and race data 
df_pop2016 = df_census2[['State', 'Population estimates, July 1, 2016,  (V2016)', 'White alone, percent, July 1, 2016,  (V2016)', 'Black or African American alone, percent, July 1, 2016,  (V2016)', 'American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)', 'Hispanic or Latino, percent, July 1, 2016,  (V2016)', 'Asian alone, percent, July 1, 2016,  (V2016)']]
In [266]:
#Takes a string that represents a percentage or a float value.
#if a string represents a percentage it removes the % sign else if it represents a float value it casts the string 
#to float and multiplies it by 100 and returns back a string
def remove_sign(stri):
    if stri.endswith('%'):
        return stri.replace('%', '')
    else:
        i=float(stri)*100
        return str(i)
In [ ]:
#Utilize the series "apply" function to apply the above "remove_sign" function over the race columns and then 
#change the data type of the columns to float64 using "astype" function
df_pop2016['White alone, percent, July 1, 2016,  (V2016)'] = df_pop2016['White alone, percent, July 1, 2016,  (V2016)'].apply(remove_sign).astype('float64')
df_pop2016['Black or African American alone, percent, July 1, 2016,  (V2016)'] = df_pop2016['Black or African American alone, percent, July 1, 2016,  (V2016)'].apply(remove_sign).astype('float64')
df_pop2016['American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)'] = df_pop2016['American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)'].apply(remove_sign).astype('float64')
df_pop2016['Hispanic or Latino, percent, July 1, 2016,  (V2016)'] = df_pop2016['Hispanic or Latino, percent, July 1, 2016,  (V2016)'].apply(remove_sign).astype('float64')
df_pop2016['Asian alone, percent, July 1, 2016,  (V2016)'] = df_pop2016['Asian alone, percent, July 1, 2016,  (V2016)'].apply(remove_sign).astype('float64')
In [319]:
#Showcase the effect of above changes
df_pop2016
Out[319]:
Fact State Population estimates, July 1, 2016, (V2016) White alone, percent, July 1, 2016, (V2016) Black or African American alone, percent, July 1, 2016, (V2016) American Indian and Alaska Native alone, percent, July 1, 2016, (V2016) Hispanic or Latino, percent, July 1, 2016, (V2016) Asian alone, percent, July 1, 2016, (V2016)
0 Alabama 4863300 69.30% 26.80% 0.70% 4.20% 1.40%
1 Alaska 741894 66.10% 3.80% 15.20% 7.00% 6.30%
2 Arizona 6931071 83.30% 4.90% 5.40% 30.90% 3.40%
3 Arkansas 2988248 79.40% 15.70% 1.00% 7.30% 1.60%
4 California 39250017 72.70% 6.50% 1.70% 38.90% 14.80%
5 Colorado 5540545 87.50% 4.50% 1.60% 21.30% 3.30%
6 Connecticut 3576452 80.60% 11.80% 0.50% 15.70% 4.70%
7 Delaware 952065 70.10% 22.60% 0.60% 9.20% 4.00%
8 Florida 20612439 77.60% 16.80% 0.50% 24.90% 2.90%
9 Georgia 10310371 61.20% 32.00% 0.50% 9.40% 4.10%
10 Hawaii 1428557 25.80% 2.20% 0.40% 10.40% 37.70%
11 Idaho 1683140 93.30% 0.80% 1.80% 12.30% 1.50%
12 Illinois 12801539 77.20% 14.70% 0.60% 17.00% 5.50%
13 Indiana 6633053 85.60% 9.70% 0.40% 6.80% 2.20%
14 Iowa 3134693 91.40% 3.70% 0.50% 5.80% 2.50%
15 Kansas 2907289 86.60% 6.20% 1.20% 11.60% 3.00%
16 Kentucky 4436974 88.00% 8.30% 0.30% 3.50% 1.50%
17 Louisiana 4681666 63.20% 32.60% 0.80% 5.00% 1.80%
18 Maine 1331479 94.80% 1.50% 0.70% 1.60% 1.20%
19 Maryland 6016447 59.30% 30.70% 0.60% 9.80% 6.60%
20 Massachusetts 6811779 81.80% 8.60% 0.50% 11.50% 6.70%
21 Michigan 9928300 79.60% 14.20% 0.70% 5.00% 3.10%
22 Minnesota 5519952 85.00% 6.20% 1.30% 5.20% 4.90%
23 Mississippi 2988726 59.30% 37.70% 0.60% 3.10% 1.10%
24 Missouri 6093000 83.20% 11.80% 0.60% 4.10% 2.00%
25 Montana 1042520 89.20% 0.60% 6.60% 3.60% 0.80%
26 Nebraska 1907116 88.90% 5.00% 1.40% 10.70% 2.50%
27 Nevada 2940058 75.10% 9.60% 1.60% 28.50% 8.70%
28 New Hampshire 1334795 93.80% 1.50% 0.30% 3.50% 2.70%
29 New Jersey 8944469 72.40% 15.00% 0.60% 20.00% 9.80%
30 New Mexico 2081015 0.826 0.025 0.106 0.485 0.017
31 New York 19745289 0.699 0.177 0.01 0.19 0.089
32 North Carolina 10146788 0.71 0.222 0.016 0.092 0.029
33 North Dakota 757952 0.879 0.029 0.055 0.036 0.015
34 Ohio 11614373 0.825 0.128 0.003 0.037 0.022
35 Oklahoma 3923561 0.746 0.078 0.092 0.103 0.022
36 Oregon 4093465 0.874 0.021 0.018 0.128 0.045
37 Pennsylvania 12784227 0.824 0.118 0.004 0.07 0.035
38 Rhode Island 1056426 0.844 0.081 0.01 0.149 0.036
39 South Carolina 4961119 0.685 0.275 0.005 0.055 0.016
40 South Dakota 865454 0.852 0.02 0.09 0.037 0.015
41 Tennessee 6651194 0.787 0.171 0.004 0.052 0.018
42 Texas 27862596 79.40% 12.60% 1.00% 39.10% 4.80%
43 Utah 3051217 91.10% 1.40% 1.60% 13.80% 2.50%
44 Vermont 624594 94.60% 1.30% 0.40% 1.90% 1.80%
45 Virginia 8411808 70.00% 19.80% 0.50% 9.10% 6.60%
46 Washington 7288000 80.00% 4.10% 1.90% 12.40% 8.60%
47 West Virginia 1831102 93.60% 3.60% 0.20% 1.50% 0.80%
48 Wisconsin 5778708 87.50% 6.60% 1.10% 6.70% 2.80%
49 Wyoming 585501 92.80% 1.30% 2.70% 10.00% 1.00%
In [174]:
#Filter Gun data that only pertains to July 2016
df_gJuly = df_guns[df_guns['month']=='2016-07-01'][['month', 'state', 'totals']]
In [278]:
#Merge population data of july 2016 and gun data of july 2016
df_mergedJuly=pd.merge(df_gJuly, df_pop2016, left_on='state', right_on='State')
In [318]:
df_mergedJuly.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 50 entries, 0 to 49
Data columns (total 11 columns):
 #   Column                                                                    Non-Null Count  Dtype         
---  ------                                                                    --------------  -----         
 0   month                                                                     50 non-null     datetime64[ns]
 1   state                                                                     50 non-null     object        
 2   totals                                                                    50 non-null     int64         
 3   State                                                                     50 non-null     object        
 4   Population estimates, July 1, 2016,  (V2016)                              50 non-null     int64         
 5   Population estimates base, April 1, 2010,  (V2016)                        50 non-null     int64         
 6   White alone, percent, July 1, 2016,  (V2016)                              50 non-null     float64       
 7   Black or African American alone, percent, July 1, 2016,  (V2016)          50 non-null     float64       
 8   American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)  50 non-null     float64       
 9   Hispanic or Latino, percent, July 1, 2016,  (V2016)                       50 non-null     float64       
 10  Asian alone, percent, July 1, 2016,  (V2016)                              50 non-null     float64       
dtypes: datetime64[ns](1), float64(5), int64(3), object(2)
memory usage: 4.7+ KB

Does Population in States correlate with total gun registration and purchases?¶

In [306]:
#plot a scatter plot of gun totals against population estimates
plt.figure(figsize=(5,5))
plt.scatter(data=df_mergedJuly, x='Population estimates, July 1, 2016,  (V2016)', y='totals')
plt.xlabel('July 2016 Population')
plt.ylabel('Gun Totals');
plt.title('Scatter Plot of Gun Totals against Population Estimates');
  • The Scatter Plot of Gun Totals against Population Estimates depicts the existance of correlation between gun totals and population estimates variables although this doesn't hold true in several states, a key outlier being the state of Kentucky with relatively low population estimates but with the highest gun totals.

Is there relation between total gun registration and purchases and the percentage of various races in the given states?¶

In [307]:
#plot a scatter plot of gun totals against percentage of white alone population
plt.figure(figsize=(5,5))
plt.scatter(data=df_mergedJuly, x='White alone, percent, July 1, 2016,  (V2016)', y='totals')
plt.xlabel('White alone, percent, July 1, 2016')
plt.ylabel('Gun Totals');
plt.title('Scatter Plot of Gun Totals against Percentage of White alone Population');
  • The Scatter Plot of Gun Totals against Percentage of White alone Population depicts that there's no correlation between the gun totals and the population percentage of white alone variables.
In [308]:
#plot a scatter plot of gun totals against percentage of black or african american alone population
plt.figure(figsize=(5,5))
plt.scatter(data=df_mergedJuly, x='Black or African American alone, percent, July 1, 2016,  (V2016)', y='totals')
plt.xlabel('Black or African American alone, percent, July 1, 2016')
plt.ylabel('Gun Totals');
plt.title('Scatter Plot of Gun Totals against Percentage of Black or African American alone Population');
  • The Scatter Plot of Gun Totals against Percentage of Black or African American alone Population depicts that there's no correlation between the gun totals and the population percentage of Black or African American alone variables.
In [309]:
#plot a scatter plot of gun totals against percentage of hispanic or latino alone population
plt.figure(figsize=(5,5))
plt.scatter(data=df_mergedJuly, x='Hispanic or Latino, percent, July 1, 2016,  (V2016)', y='totals')
plt.xlabel('Hispanic or Latino, percent, July 1, 2016')
plt.ylabel('Gun Totals');
plt.title('Scatter Plot of Gun Totals against Percentage of Hispanic or Latino Population');
  • The Scatter Plot of Gun Totals against Percentage of Hispanic or Latino Population depicts that there's no correlation between the gun totals and the population percentage of Hispanic or Latino variables.

Which state has had the highest growth in gun registrations?¶

In [313]:
#plot a line plot showcasing changes of Gun totals overtime in various States
plt.figure(figsize=(50,50))
sns.relplot(kind='line', data=df_guns[['month', 'state', 'totals']], x='month', y='totals', hue='state', aspect=2.5)
plt.ylabel('Gun totals')
plt.xlabel('Time Change')
plt.title('Line plot depicting changes of Gun totals overtime in various States')
plt.show();

<Figure size 3600x3600 with 0 Axes>
  • The state of Kentucky has had the highest consistent gun purchases and registrations followed by the state of Carlifonia. The state of North Carolina experienced a momentary peak in march 2014 which overally was the highest but then plummated in the consequent periods.
In [76]:
#view the highest 30 totals including state and month 
df_guns.sort_values(by='totals', ascending=False)[['month', 'state', 'totals']].head(30)
Out[76]:
month state totals
2345 2014-03-01 North Carolina 541978
18 2017-09-01 Kentucky 398706
348 2017-03-01 Kentucky 397959
513 2016-12-01 Kentucky 397059
73 2017-08-01 Kentucky 394718
293 2017-04-01 Kentucky 392685
183 2017-06-01 Kentucky 392002
238 2017-05-01 Kentucky 391740
128 2017-07-01 Kentucky 390494
623 2016-10-01 Kentucky 378973
788 2016-07-01 Kentucky 363085
843 2016-06-01 Kentucky 360293
458 2017-01-01 Kentucky 345924
568 2016-11-01 Kentucky 330444
898 2016-05-01 Kentucky 321666
1008 2016-03-01 Kentucky 321318
1778 2015-01-01 Kentucky 320778
403 2017-02-01 Kentucky 313748
1173 2015-12-01 Kentucky 308824
953 2016-04-01 Kentucky 306214
1118 2016-01-01 Kentucky 302383
678 2016-09-01 Kentucky 298753
499 2016-12-01 California 298161
1228 2015-11-01 Kentucky 295891
1833 2014-12-01 Kentucky 295697
1393 2015-08-01 Kentucky 286463
1668 2015-03-01 Kentucky 281518
2328 2014-03-01 Kentucky 280948
1283 2015-10-01 Kentucky 276162
2273 2014-04-01 Kentucky 274155

What is the overall trend of gun purchases?¶

In [85]:
#subset the df_guns dataframe and aggregate based on the month column
df_trend = df_guns[['month', 'totals']].groupby(['month']).sum()
In [314]:
#plot a line plot to showcase the overall trend of gun purchases
plt.figure(figsize=(25,15))
sns.lineplot(data=df_trend, x="month", y="totals")
plt.ylabel('Gun totals')
plt.xlabel('Time Change')
plt.title('Line plot depicting the overall trend of Gun purchases', size=20)
plt.show();
  • The overall trend in gun purchases is an upward/increasing trend as indicated by higher highs and higher lows when the totals gun_data figures are plotted against month time data in a time plot.

Conclusions¶

Results: The data suggests that

  1. There exists correlation between gun totals and population estimates variables although this doesn't hold true in several states, a key outlier being the state of Kentucky with relatively low population estimates but with the highest gun totals.
  2. There is no correlation between total gun registration and purchases and the percentage of various races in the given states.
  3. The state of Kentucky has had the highest consistent gun purchases and registrations followed by the state of Carlifonia. The state of North Carolina experienced a momentary peak in march 2014 which overally was the highest but then plummated in the consequent periods.
  4. The overall trend in gun purchases is an upward/increasing trend as indicated by higher highs and higher lows when the totals gun_data figures are plotted against month time data in a time plot.

Limitations:

  1. The gun_data available had misssing data on various types of gun registrations and purchases.
  2. The U.S. Census Data has most variables with just one data point per state for 2016, but only a few have data for more than one year.
General Resources¶
  • Python for Data Analysis by Wes McKinney
  • Data Wrangling with Python Tips and Tools to Make Your Life Easier by Jacqueline Kazil, Katharine Jarmul.